The Lottery of Birth Remains: Poverty and Inequality Within and Between Socio-Religious Groups in Uttar Pradesh, India

The Lottery of Birth Remains: Poverty and Inequality Within and Between Socio-Religious Groups in Uttar Pradesh, India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Lottery of Birth Remains: Poverty and Inequality Within and Between Socio-Religious Groups in Uttar Pradesh, India Mahtab Alam, Srinivas Goli, Tapasya Raj This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7631505/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract This study utilises a unique survey of 7,101 households from Uttar Pradesh, India, to conduct the first comprehensive measurement of poverty and inequality across consumption, wealth, and landholding at the sub-caste ( biradari ) level. Our findings reveal prominent disparities in poverty incidence, ranging from 6% among Brahmins to 55% among Paasis . The analysis also demonstrates substantial vertical economic inequality within broad social-religious groups, with Gini coefficients of 0.36 for consumption, 0.72 for wealth, and 0.66 for landholding across sub-castes. The results highlight significant variations between poverty and inequality by sub-caste. We further identify a positive correlation between social exclusion/untouchability practices and poverty across districts. The role of caste, land size and Intergenerational occupation attainment and household head education status emerge as significant predictors of sub-caste inequality in household consumption and wealth value. This study contributes significantly to the economic literature on caste stratification, discrimination, and human development. Castes Poverty Wealth Inequality Social Exclusion Untouchability Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Poverty and inequality in India have long been studied in economic terms, and recent scholarship have increasingly emphasised their social foundations. Caste remains one of the most significant determinants of lifetime opportunities in education and employment, reinforcing both social and economic discrimination (Borooah et al., 2014 ; Deshpande & Ramachandran, 2017 ; Mosse, 2018 ). Scholars argue that caste should be integrated into global development policy debates with the same urgency as gender, race, or ethnicity (Desai & Dubey, 2012 ; Mosse, 2018 ). Extant literature has shown that socially marginalised groups, including Scheduled Castes (SCs), Scheduled Tribes (STs), Other Backwards Classes (OBCs), (Boorah 20005; Boorah et al., 2007; Bhaumik & Chakrabarty, 2006) and their counterparts within minority religions, experience disproportionately high levels of poverty (Azam, 2023a , 2023b ; Goli et al., 2021 ; Trivedi et al., 2016a , b ). What is under-explored is the sub-caste variations in economic and social indicators, as caste groups are viewed as uniform entities. The present study sets out to explore whether poverty emerges as a visible outcome of deep-rooted structural inequalities driven by sub-caste categories and social exclusion. India’s caste system is a rigid social hierarchy based on birth, occupation, and wealth that provides a critical lens for examining intersecting inequalities (Jodhka, 2021 ; Mosse, 2018 ). For centuries, it has marginalised lower castes, including Scheduled Castes (SCs), Scheduled Tribes (STs), and Other Backwards Classes (OBCs) (Mamgain, 2023 ). These identities perpetuate vulnerabilities, exacerbating material deprivations and embedding long-term marginalisation (Anand & Moon, 2024 ; Nakamura, 2014 ). Recent studies have empirically documented that caste discrimination is not confined to Hindus but also exists in other religions, such as the acute exclusion faced by Dalits in the Muslim community due to intersecting caste and communal identities (Azam, 2023a , 2023b ; Goli et al., 2021 ; Trivedi et al., 2016a , b ). Untouchability persists as a brutal manifestation of caste-based oppression (Kumar et al., 2009 ), even now, the multi-dimensional disadvantages faced by Dalits across non-Hindu religions remain understudied in Indian scholarship. In this context, the study examines poverty and wealth inequality and their link to caste-based untouchability and social exclusion in India through a sub-caste lens, utilising a unique survey that enables granular data analysis, unavailable since the discontinuation of India’s caste after 1931 Census. This approach offers several advancements: first, sub-caste-level data results in a more precise understanding of caste disparities than broad categories like OBCs and SCs, which aggregate hundreds of distinct castes; second, it facilitates a "reversal of the gaze" (Satyanarayana, 2013 ) shifting focus from marginalised groups to upper-caste privilege and reframing caste as a system of power rather than just disadvantage; third, it allows for intersectional analysis of caste and economic inequality, opening avenues to study compounded discrimination; and finally, it examines untouchability with marginalisation and social exclusion. To strengthen our analysis, for the first time, we introduce an evidence-based index for measuring social inclusion and untouchability, enhancing the study’s analytical rigour. The evidence generated through this study can serve as a valuable tool to revitalise social welfare policies in India, particularly in the context of growing complexities in reservation demands, from the dominant, backwards classes to the upper castes in the country. Note: Poor and non-poor households are coded as 1 and 0. Wealth groups are classified using the consumption poverty line, while consumption groups are classified using wealth value score. 2. Background and literature review 2.1 Caste and poverty Poverty is defined by a severe lack of resources and financial means, which restricts individuals' freedoms and capabilities. It extends beyond low income to represent the deprivation of opportunities and choices that prevent people from living lives they value(Sen, 1983 , 1999 ). This perspective underscores poverty’s broader impact on human development, where resource scarcity limits access to essential services, education, and healthcare, perpetuating a cycle of deprivation ( Sen, 2005 , 2006 ). However, poverty 1 manifests unevenly across social groups, with empirical evidence indicating that lower-caste individuals face greater economic disadvantages compared to others (Deaton & Drèze, 2005 ; Kumar et al., 2009 ). Despite a rapid decline in poverty rates from 53.6% to 21.9% in 2012, systemic disparities persist, leaving lower castes disproportionately affected and posing a persistent challenge to India’s development (Mosse, 2018 ; Nandwani, 2016 ; A. Thorat et al., 2017 ). Critically, poverty cannot be understood solely in economic terms as income and consumption, but also diminished human capabilities such as limited access to education, technical skills, healthcare, and nutrition, along with heightened vulnerability to violence, social exclusion, and the denial of dignity and basic rights (Alkire & Seth, 2008 ). 2.2 Caste and inequality In India, the caste system operates as a vertical hierarchy founded on the "principle of gradation and rank," which enforces unequal access to economic, educational, and civic rights across caste groups (Geetha, 2021 ). This structure reinforces hierarchical dominance, systematically narrowing economic and social entitlements from upper to lower castes (Desai & Dubey, 2012 ). Such inequalities reflect "historically denied opportunities for lower castes," deeply rooted in India’s caste system for generations (Deshpande, 2001 ), still caste remains a persistent determinant of power (Aggarwal & Dreze, 2025). Traditionally, research on caste relations in India has been dominated by non-economists, anthropologists, sociologists, and historians. However, economic studies on caste and inequality have increasingly examined across social groups in consumption, income, education and occupations using large-scale surveys 2 and smaller primary surveys and the recent Caste Census of Bihar (Ansari & Dhar, 2022 ; Deshpande, 2001 ; Demirguc-Kunt et al, 2018 ; Deshpande & Ramachandran, 2017 ; Guilmoto, 2024 ; Hasan & Mehta, 2006 ). A near-consensus emerges from both these types of studies: disadvantaged social groups consistently fare worse than others across measured indicators, though regional variations exist (Mosse, 2018 ). For instance, NSS consumption data analysed by (Kijima & Lanjouw, 2005 ) reveal that lower-caste groups face economic disparities due to both lower endowments (physical and human capital) and structural differences in income generation (Dev, 2006 ). Strikingly, these inequalities have persisted over extended periods, which highlights the enduring rigidity of caste-based disadvantage. Moreover, discrimination manifests across institutional spaces, labour markets, educational institutions, and even marriage practices, further entrenching caste divisions (Banerjee et al., 2013 ; Hoff & Pandey, 2006 ). 2.3 Caste-based social exclusion, marginalisation, and untouchability Deprivation is a material or socio-cultural disadvantage experienced by an individual or group due to different forms of cultural separation and discrimination (Borooah et al., 2014 ; Gang et al., 2008 ). Untouchability is one such discriminatory process experienced by an individual or group based on their caste identity (Trivedi et al., 2016a ). It poses severe disadvantages regarding unequal distribution of resources and skills- education, training, health, employment, housing, financial resources, social security, and so on (Sooryamoorthy, 2008 ). When an individual or group is deprived of income, education, employment, or health opportunities, it is viewed through the basic needs approach (Hammill, 2009 ). However, when an individual or group is deprived of knowledge and awareness due to restricted social interactions and cultural participation, it is considered ‘marginalisation’ within the sociocultural approach (Hasan & Mehta, 2006 ; Nandwani, 2016 ). Marginalisation is the discriminatory process that forces individuals or groups to the edge of the economic, political, social, cultural, and ideological system. Social exclusion is an umbrella concept linked with deprivation and marginalisation that extends beyond economic or material exclusion (Jungari & Bomble, 2013 ; Kabeer, 2000 ). While caste-based inequalities have been widely studied, most research overlooks sub-caste (‘biradari’) variations, treating social groups as homogeneous rather than examining how internal differentiation shapes economic outcomes. Government of India classifications (SCs, STs, OBCs) often mask critical disparities in poverty and inequality with these broad social groups, particularly in a large and socially heterogeneous state like Uttar Pradesh. By analysing sub-caste-level data, this study provides a more nuanced understanding of deprivation, one that captures the escalating heterogeneity within caste groups. Against this backdrop, this study has two objectives: (1) to measure ‘multiple indices of poverty’ and ‘inequality’ in consumption, wealth, and landholding at the sub-caste level for the first time; (2) to assess the relationship between ‘poverty and inequality’ and ‘processes of social discrimination, such as untouchability. With these objectives, we test the following hypotheses: H₁: Socially deprived castes experience higher poverty levels in Uttar Pradesh. H₂: Untouchability and caste-based discrimination exacerbate poverty. By focusing on sub-caste granularity, this study reveals hidden layers of inequality, offering critical insights into how social exclusion and economic disparities interact, dynamics often obscured by broad caste/social group classifications. 3. Data and Methods 3.1 Data This study uses primary data from the Giri Institute of Development Studies (GIDS) under the project “Social and Educational Status of OBCs and Dalit Muslims in Uttar Pradesh.” The household survey was conducted from October 2014 to April 2015, employing a multi-stage stratified random sampling design for data collection. Fourteen districts were distributed according to the population share from all four state regions: six from Western, two from Central, five from Eastern, and two from the Bundelkhand region. Each district's primary sampling units (PSUs) comprised Gram Panchayats/Villages/Wards and were selected based on probability proportional to size (Goli et al., 2021 ; Trivedi et al., 2016a , b ). When selecting villages, it was ensured that sample villages had a mix of castes in both religious groups. If there was no caste heterogeneity, the neighboring town was included in PSUs to cover the required caste distribution. Similar procedures were followed in the case of sample selection from the wards of urban areas. The sample frame for the study design and sampling is the Primary Census Abstract (PCA) of the Census of India, 2011 (Office of RGI and Census Commissioner, 2011). We have ensured a minimum sample of 150 households in a district to ensure a sufficient sample of the six socio-religious groups: Hindu General, Muslim General, Hindu OBCs, Muslim OBCs, Hindu Dalits, and Muslim Dalits. Furthermore, around 50 households from a village and 30 households from a ward in a town/city were selected for the survey. Therefore, the final cumulative sample size of the study was 7124 households from 240 PSUs spreading across 14 districts (for detailed sampling methodology, see Tiwari et al., 2022 ). However, after accounting for missing cases for some of the study variables, the analytical sample of the study accounts for 7101 households. The caste groups categorised here are based on the Varna system and their historical dominance in terms of social and economic status. In addition, Data on caste, religion, and social group were collected from three sets of questions and matched with each other while classifying socio-religious groups (SRGs). The focus of this article is on the significant sub-castes ( jatis ) from all six SRGs, which have sufficient statistical representation in the sample, Brahmins and Thakurs from upper caste Hindus; Yadavs , Kurmis , Jats , Lodhs among OBCs; Jatav Chamars and Pasis among Dalits; Ansaris among OBC Muslims; besides upper caste Muslims and Dalit Muslims. The article prefers the term “upper caste” over “general category”, which gives an illusion of a casteless group (Deshpande & Sharma, 2013 ). 3.2 Choice and measurement of economic variables This study examines both social and economic dimensions of household well-being to comprehensively understand sub-caste inequalities in Uttar Pradesh, India. The economic analysis focuses on four key indicators: (1) poverty ratios, (2) consumption expenditure, (3) wealth valuation, and (4) land ownership. Social dimensions include discrimination (particularly untouchability practices), whereas household consumption expenditure remains a standard poverty metric in policy circles, it fails to capture critical aspects of economic status. First, it overlooks household investments in financial and real assets. Second, in Uttar Pradesh's agrarian context, land ownership serves as a distinct and crucial measure of economic standing and could reflect disparities in wealth accumulation. In this context, our study advances traditional measurement approaches in three significant ways: (1) We move beyond binary asset ownership measures to assess actual wealth value in rupees, addressing valuation limitations identified in prior research (Filmer & Pritchett, 2001 ; Hlasny & AlAzzawi, 2019 ). (2) Following (Piketty, 2018 ) emphasis on wealth as the fundamental inequality metric, we account for both physical assets (land, livestock and agricultural equipment) and financial assets (investments and savings). (3) We incorporate multidimensional poverty indicators, including housing quality, durable goods ownership, and access to utilities, recognising their direct relationship with income generation and mobility (Alkire et al., 2014 ). The constructed wealth index uses respondent-reported asset values rather than simple possession counts, providing a more precise measure of economic inequalities than conventional indices (Wittenberg & Leibbrandt, 2017 ). The following sections detail our econometric methodology and its application to these multidimensional measures. 3.3 Econometric methods 3.3.1 Foster-Greer-Thorbecke (FGT) measures : We used Foster-Greer-Thorbecke's measures (Foster et al., 1984 ; Haughton & Khandker, 2009 ) of poverty for a given population defined in discrete terms: FGT (α) = \(\:\:\:\frac{1}{N}\sum\:_{i=1}^{N}{\left(\frac{{G}_{i}}{z}\right)}^{\alpha\:}\) α>=0 (poverty aversion parameter and G i = (z-y i ) (1) Where \(\:{G}_{i}\:\) is the deviation of household consumption from the poverty line, N is the total no of households, y is monthly per capita expenditure to measure poverty ratio, and z is the official poverty line or monetary cut-off for Uttar Pradesh 3 . If household consumption (y i ) is less than the threshold value (z), then the household will be coded as ‘1’ if poor; otherwise, ‘0’ for those above the cut-off or threshold, equally for all the members of the household. All three poverty measures of the FGT family are calculated based on the aversion parameter value of α concerning censored households. a) Head Count Index of Poverty FGT(α) = 0 : FGT (0) = \(\:\frac{q}{N}\) , where q is no of poor households (1.1) The most widely used poverty indicator is the ‘headcount ratio’ ( hereafter HCR), i.e. , the proportion of the household population below the state poverty line, Uttar Pradesh. The headcount index is easy to compute and interpret, but has two limitations, which are the following: First, it does not capture the intensity of poverty concerning the transfer of income(using consumption as a proxy ) from rich to poor households as a measure of welfare; simply, it violates the transfer principle formulated by (Dalton, 1920 ). Second, the head-count index does not indicate how poor people are experiencing poverty, and hence will not change if households below the poverty line become poorer. Therefore, we used our second measure: the poverty gap index (FGT 1 ), given by the aggregate consumption shortfall of the poor households as a proportion of the poverty line and normalised by the population size. b) Poverty Gap Index FGT( α ) = 1 FGT (1) = \(\:\:\:\frac{1}{N}\sum\:_{i=1}^{N}{\left(\frac{{G}_{i}}{z}\right)}^{1}\) (1.2) The poverty gap index quantifies the average distance by which poor households fall below the poverty line, showing this shortfall as a percentage of the line (Haughton & Khandker, 2009 ). This measure represents the mean proportionate poverty gap in the population (where the non-poor household has zero poverty gap). It shows how much income or other resources would need to be transferred to the poor to bring their incomes or expenditures up to the poverty line (as a proportion of the poverty line). c) Squared Poverty Gap Index FGT(α) = 2 : FGT (2) = \(\:\:\:\frac{1}{N}\sum\:_{i=1}^{N}{\left(\frac{{G}_{i}}{z}\right)}^{2}\) (1.3) The squared poverty gap index is a weighted sum that squares households poverty gaps, thereby assigning greater weight to those furthest below the poverty line. This provides a measure of poverty severity, capturing the depth of hardship among the poorest. For a complete analysis, it is essential to use this measure alongside the headcount and poverty gap indices (Haughton & Khandker, 2009 ). 3.3.2 Blinder-Oaxaca decomposition model We used the Blinder-Oaxaca linear decomposition model to systematically explain the relative contributions of various factors to the observed inequalities in wealth and consumption distribution across households categorised as poor and non-poor (Blinder, 1973 ; Oaxaca, 1973 ). The binary nature of the outcome variable, wherein households are classified as poor (coded as ‘1’) or non-poor (coded as ‘0’). The focal point of this investigation is the dependent variable, denoted as 'y,' representing the wealth index score. The following expression captures the disparity in mean outcomes between these two groups: The Blinder-Oaxaca decomposition for Eq. 3.1 has been written in collapsed terms $$\:\varDelta\:Y=\left({\stackrel{-}{X}}_{Poor}-{\stackrel{-}{X}}_{non-poor}\right){\beta\:}_{non-poor}\:\:+\:\left({\stackrel{-}{X}}_{non-poor}\right)({\beta\:}_{poor}\:-\:{\beta\:}_{non-poor})\:\:+\:\varDelta\:\epsilon\:\:\:$$ 3.1 Where \(\:{\stackrel{-}{X}}_{Poor}\) and \(\:{\stackrel{-}{X}}_{non-Poor}\) are vectors of explanatory variables estimated at the means of poor and non-poor combined, respectively. Further, we estimated how much of the overall gaps or the gap is specific to any one of the X’s (also called the explained component) and differences in βs (also called the unexplained component). It allows for a detailed examination of the extent to which the observed wealth gap is driven by differences in observable characteristics versus the differential impact of these characteristics across the two groups. Mathematically, it is expressed as follows: $$\:\underset{Explained\:by\:differecnes\:in\:Covariates}{\underset{⏟}{\left({\stackrel{-}{X}}_{Poor}-{\stackrel{-}{X}}_{non-poor}\right){\beta\:}_{non-poor}}}\:\:+\:\:\underset{Explained\:by\:differecnes\:in\:cofficients}{\underset{⏟}{\left({\stackrel{-}{X}}_{non-poor}\right)({\beta\:}_{poor}\:-\:{\beta\:}_{non-poor})\:\:}}\:\:\:\:\:+\:\underset{Unexplainded\:component}{\underset{⏟}{\varDelta\:\epsilon\:}}$$ 3.2 3.3.3 Social Exclusion and Untouchability Index Social exclusion has been defined as ‘the process through which individuals or groups are completely or partially excluded from participation in the society within which they live (Rawal, 2008 ). Social exclusion has been used differently by scholars, who often use it simultaneously with poverty and deprivation (S. Thorat & Lee, 2005 ). Untouchability is a distinct Indian social institution that legitimises and enforces practices of discrimination against people born into particular castes and practices that are humiliating, exclusionary, and exploitative. It covers all spheres of life, including social, cultural, and economic, and derives its strength from the concept of purity, one of the essential aspects of the caste system. In its classical form, the caste system considers ‘untouchables’ and keeps them outside the four-tier system (Jha, 1997 ). The practice is so vicious that mere touch or a shadow of an ‘untouchable’ falling on someone else pollutes them. The social exclusion and untouchability index were conceptualised based on questions from socially excluded and untouchable households and their upper caste counterparts (Appendix Table 1). 3.3.4 Pyatt’s Gini decomposition model Pyatt ( 1976 ) developed the decomposition model using the Gini coefficient to calculate the change in inequality (measured using the Gini index [ hereafter , G]) for an economic variable attributable to ‘within’, ‘between’, and ‘overlapping’ components of sub-caste groups. We used the same approach to decompose the economic inequality, or G, in consumption or wealth by sub-castes to derive the contribution of ‘between’ and ‘within group’ inequalities. The steps of the decomposition procedure are explained below: Let a population of ‘n’ individuals, with a given consumption or wealth vector (y1, y2, y3 … y n ) and mean consumption or wealth ‘y’ is desegregated into ‘k’ sub-caste groups, with n= \(\:\sum\:_{j\equiv\:1}^{k}{n}_{j}\:\:\text{a}\text{n}\text{d}\:\) sub-caste groups mean is \(\:{\stackrel{-}{y}}_{j}\) . The G between sub-caste groups j and h can be expressed as: $$\:{G}_{jh}=\frac{1}{{n}_{j}{n}_{h}\left({\stackrel{-}{y}}_{j}+{\stackrel{-}{y}}_{h}\right)}+\sum\:_{i=1}^{{n}_{j}}{\sum\:}_{i=1}^{{n}_{h}}\left|{y}_{ji}-{y}_{hr}\right|\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(4.1\right)$$ Suppose F(y) be the cumulative distribution function of consumption or wealth vector. In that case, the expected consumption or wealth difference between sub-caste groups j and h can be defined as: $$\:{d}_{{j}_{h}}^{1}={\int\:}_{0}^{x}d{F}_{j}\left(y\right)\:{\int\:}_{0}^{x}(y-x)\:d{F}_{h}\left(x\right),\:for\:{y}_{ji}>{y}_{hr}\:and\:\:\:{\stackrel{-}{y}}_{j}>{\stackrel{-}{y}}_{h}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(4.2\right)$$ $$\:{d}_{{j}_{h}}^{2}={\int\:}_{0}^{x}d{F}_{h}\left(y\right)\:{\int\:}_{0}^{x}(y-x)\:d{F}_{j}\left(x\right),\:for\:{y}_{ji}>{y}_{hr}\:and\:\:\:{\stackrel{-}{y}}_{j}>{\stackrel{-}{y}}_{h}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(4.3\right)$$ Relative consumption or wealth affluence is defined as: $$\:{D}_{jh}=\frac{{d}_{{j}_{h}}^{1}-{d}_{{j}_{h}}^{2}}{{d}_{{j}_{h}}^{1}+{d}_{{j}_{h}}^{2}}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(4.4\right)$$ If the population shares in sub-caste group j is \(\:{p}_{j}=\frac{{n}_{j}}{n}\) , and consumption or wealth share in sub-caste group j is \(\:{s}_{j}=\frac{{p}_{j}{\stackrel{-}{y}}_{j}}{\stackrel{-}{y}}\:\) . Then the contribution to total inequality attributable to the difference between the k population sub-caste group is defined as: $$\:{G}_{b}=\sum\:_{j=1}^{k}{\sum\:}_{h=1\:j\ne\:h}^{k}{G}_{jh}{D}_{jh}\left({P}_{j}{S}_{h}+\:{P}_{h}{S}_{j}\right)\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(4.5\right)$$ The G for sub-caste group j is given by: $$\:{G}_{jj}=\frac{{\sum\:}_{i=1}^{{n}_{j}}{\sum\:}_{r=1}^{{n}_{j}}{(y}_{ij}{-y}_{rj})}{2{n}_{j}^{2}{\stackrel{-}{y}}_{j}}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(4.6\right)$$ The within-caste group inequality index is the sum of Gini indices for all sub-caste groups weighted by the product of population shares and consumption or wealth shares of the sub-caste groups. $$\:{G}_{w}=\:\sum\:_{j=1}^{k}{G}_{jj}{P}_{j}{S}_{j}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(4.7\right)$$ If sub-caste groups are not overlapping, total inequality can be expressed as the sum of within-caste group and between-caste group indices. However, if sub-caste groups are overlapping, we can add another component, a part of between-group disparities stemming from the overlap factor between the two distributions, which measures the contribution of the intensity of trans-variation between the sub-populations of G: $$\:{G}_{t}=\sum\:_{j=1}^{k}{\sum\:}_{h=1\:j\ne\:k}^{k}{G}_{jh}(1-{D}_{jh})\:\left({P}_{j}{S}_{h}+\:{P}_{h}{S}_{j}\right)$$ 4.8 Thus, G can be decomposed into three components: within-caste group inequality, between-caste group inequality and inequality due to overlapping factors across caste groups. $$\:\text{G}={G}_{w}+{G}_{b}+Gt$$ 4.9 4. Results and Discussion 4.1. Descriptive statistics Table 1 presents descriptive statistics for 7,101 households across socioeconomic, demographic, and welfare indicators. The sample shows substantial economic disparities with mean monthly per capita expenditure at ₹1,313 (SD=₹1,281) and average household wealth of ₹147,810 (SD=₹427,540), demonstrating significant variation (min = 0 to max=₹9,499,000). Caste composition reveals Hindu OBCs as the largest group (33%), followed by Hindu General (15%), Muslim OBC (18%), and Hindu Dalit (17%). Welfare program access remains non-limited, 79% having PDS availability and 78% utilising it, while rural-specific schemes like MGNREGA show 26% coverage. Educational attainment of the sample shows 32% lack formal education versus 10% with graduation degrees. The sample is predominantly rural (72%), with a high prevalence of social exclusion (85% experience untouchability) and economic vulnerability (40% relatively poor, 34% BPL poor). The sample distribution across socio-economic characteristics aligns with the population profile of the state as reported in the Census of India and other large-scale surveys. Table 1 Summary statistics of the study variables (N = 7101) Variables Observation Mean Std. Dev Min Max Monthly average per capita expenditure 7,101 1,313 1,281 0 27,748 The average wealth value of the households 7,101 147,810 427,540 0 9,499,000 Hindu General 7,101 0.15 0.36 0 1 Muslim General 7,101 0.09 0.28 0 1 Hindu OBC 7,101 0.33 0.47 0 1 Muslim OBC 7,101 0.18 0.38 0 1 Hindu Dalit 7,101 0.17 0.38 0 1 Dalit Muslims 7,101 0.08 0.28 0 1 PDS availability (Yes/No) 7,101 0.79 0.40 0 1 PDS utilisation (in the last 6 months) 7,101 0.78 0.41 0 1 Kisan @ Rural 5,086 0.18 0.38 0 1 MGNREGA @ Rural 5,086 0.26 0.44 0 1 ICDS @ rural 5,086 0.19 0.39 0 1 JSY 7,101 0.18 0.38 0 1 Old age pension 7,101 0.04 0.18 0 1 Widow pension 7,101 0.03 0.16 0 1 Household Age 7,101 44.00 12.00 44 86 Household Occupation 7,101 0.33 0.47 0 1 No Education 7,101 0.32 0.47 0 1 Below Primary 7,101 0.19 0.39 0 1 Below Secondary 7,101 0.38 0.49 0 1 Graduation & above 7,101 0.10 0.30 0 1 Relatively Poor 7,101 0.40 0.49 0 1 Untouchability & Social Exclusion 7,101 0.85 0.35 0 1 Source: Authors’ estimation based on GIDS survey on Social and Educational Status of OBC/Dalit Muslims in Uttar Pradesh 4.2. Poverty by castes This study employs three established Foster-Greer-Thorbecke measures of poverty metrics to analyse economic inequality across caste and sub-caste ( biradari ) groups: (1) poverty headcount ratio, (2) poverty gap index, and (3) squared poverty gap index. The subsequent sections present these findings through a caste-based analytical framework: 4.2.1. Headcounts Table 2 reveals pronounced socioeconomic inequalities in Uttar Pradesh through poverty headcount ratios, with the state average at 34%. The data demonstrate a clear caste gradient: Brahmins, occupying the highest social position, exhibit markedly lower poverty rates (9% overall), particularly in urban areas (2.1%), compared to their rural counterparts (11.7%). Conversely, historically marginalised groups face substantially higher consumption deprivation, with Chamars (44%) and Paasis (55%) bearing the heaviest burdens, especially in rural contexts. Furthermore, Religious dimensions compound these disparities as Hindu Dalits (44%) and Muslim Dalits (50%) experience high poverty, whereas Muslim OBCs (38%) and Hindu OBCs (35%) show significantly higher deprivation levels than Hindu General castes (8%). These findings underscore the intersectional nature of poverty, where caste hierarchy and religious identity collectively shape economic outcomes. 4.2.2. Poverty gap The poverty gap ratio quantifies the depth of poverty by measuring how far households fall below the poverty line. Our findings reveal stark disparities across social groups. Privileged castes like Thakur/Kshatriya and Brahmins show minimal poverty gaps (approximately 2%), indicating their relative economic security. In contrast, marginalised groups show substantially higher ratios: Kurmis (17% rural), Paasis (16% urban), Lodhs (15% rural), and Chamars (10% overall). Religious-caste intersections further exacerbate these inequalities. Urban Dalit Muslims face a particularly severe poverty gap (15%), surpassing their Hindu Dalit counterparts in rural areas (11%). Meanwhile, advantaged groups have significantly lower ratios: Hindu General (2%) and Muslim General (8%), highlighting the compounding effects of caste hierarchy and religious identity on economic deprivation. 4.2.3. Squared poverty gap The squared poverty gap ratio, which measures the severity of poverty by calculating the average squared distance below the poverty line, reveals significant disparities across social groups. Our findings demonstrate that historically disadvantaged communities endure the most severe poverty burdens: Hindu Dalits (4%) and Dalit Muslims (6%) show markedly higher ratios compared to other groups. This pattern persists among specific castes, with the Lodh community (4%) and other Hindu Dalit subgroups (6%) experiencing particularly acute deprivation. The data further highlights intergroup disparities within Muslim communities. While Ansari Muslims maintain a relatively moderate ratio (3%) and Dalit Muslims face double severity (6%), underscoring how caste-based marginalisation compounds poverty even within religious groups. These results emphasise that existing social hierarchies, with doubly disadvantaged groups (by both caste and religion), suffer the most intense economic deprivation. Table 2 Absolute poverty ratios by caste in Uttar Pradesh, India Headcounts Poverty Gap Squared Poverty Gap Caste Total Rural Urban Total Rural Urban Total Rural Urban Brahmin 9.04 11.72 2.11 2.47 3.35 0.21 1.30 1.79 2.90 Thakur/Kshatriya 6.08 7.06 0.00 2.14 2.49 0.00 1.34 1.56 0.00 Other Hindu General 8.87 16.18 2.55 2.25 4.56 0.24 1.02 2.16 0.03 Hindu General 8.20 10.95 2.06 2.32 3.28 0.20 1.2 1.78 0.02 Muslim General 32.28 35.32 25.41 7.91 8.46 6.67 3.13 3.05 3.29 Yadav 32.09 32.85 28.92 7.45 7.63 6.69 3.01 3.23 2.07 Kurmi 43.31 56.04 11.11 12.69 17.49 0.56 5.39 7.51 0.06 Jaat 14.75 15.34 12.20 3.39 3.23 4.07 1.39 1.29 1.83 Lodh 47.90 56.45 38.60 12.42 15.31 9.27 4.13 5.47 2.68 Other Hindu OBC 37.39 38.77 33.51 9.30 9.73 8.11 3.44 3.71 2.68 Hindu OBC 35.14 36.73 30.51 8.75 9.25 7.28 3.3 3.63 2.37 Ansari Muslims 36.38 37.99 34.03 9.24 9.43 8.98 3.27 3.36 3.16 Other Muslim OBC 39.00 36.97 44.88 9.33 8.84 10.75 3.37 3.04 4.34 Muslim OBC 38.03 37.30 39.65 9.30 9.03 9.90 3.3 3.14 3.77 Chamar 41.15 43.73 33.64 9.96 10.38 8.73 3.63 3.70 3.44 Paasi 54.29 54.64 50.00 14.45 14.33 15.92 5.49 5.41 6.44 Other Hindu Dalit 49.32 59.50 27.17 14.55 18.22 6.57 6.26 7.93 2.64 Hindu Dalit 44.19 48.31 32.05 11.44 12.51 8.32 4.4 4.80 3.30 Dalit Muslims 49.49 50.00 48.44 14.77 14.92 14.46 5.96 5.98 5.92 All Hindu 31.16 34.11 23.11 7.94 8.80 5.60 3.11 3.53 1.96 All Muslim 39.37 39.80 38.42 10.27 10.27 10.26 3.91 3.79 4.19 Uttar Pradesh 34.01 36.00 29.00 8.75 9.29 7.38 3.39 3.62 2.82 4.3. Economic inequality Figure 1 presents economic inequality through G coefficients across three dimensions: wealth (G = 0.72), land holdings (G = 0.67), and per capita consumption (G = 0.36). Wealth inequality is most severe, especially in rural areas (G = 0.75), while consumption shows minimal urban-rural variation (urban G = 0.35 vs. overall 0.36). Land inequality remains consistently high across both urban (G = 0.67) and rural (G = 0.66) contexts. These patterns reveal distinct layers of economic inequality that will be further investigated through sub-caste analysis, examining both vertical (inter-caste) and horizontal (intra-caste) dimensions of inequality. 4.3.1. Vertical economic inequality a) Consumption Consumption, defined as the utilisation of goods and services to meet household needs, shows significant disparities when analysed through G coefficients, with Hindu communities exhibiting higher overall consumption inequality (G = 0.38) compared to Muslims (G = 0.30). Among Hindu sub-castes, the Kurmi and Jaat groups display the most marked inequality (G = 0.42), followed by Brahmins (G = 0.37), particularly in rural areas. In contrast, Dalit communities like Chamaar (G = 0.34) and Paasi (G = 0.25) demonstrate more uniform consumption patterns, a trend consistent across urban and rural settings. Within Muslim groups, Ansari Muslims (G = 0.27) and Dalit Muslims (G = 0.29) exhibit the most equitable consumption distribution, while Muslim General (G = 0.35) and Muslim OBC (G = 0.27) sub-castes show moderate disparities, with slightly lower inequality in urban areas. These findings highlight how consumption inequality varies not only between religious groups but also across sub-castes, reflecting deeper socioeconomic stratification. b) Wealth Wealth, representing accumulated assets and resources that ensure financial security, exhibits deeper disparities than consumption when measured by G coefficients, with Hindus showing higher overall wealth inequality (G = 0.76 for OBCs) compared to Muslims. The Hindu OBC community demonstrates the most pronounced wealth concentration, especially in urban areas (G = 0.74), reflecting uneven access to economic opportunities, while Hindu Dalits face substantial rural-urban divides (G = 0.70 rural vs. 0.64 urban). In contrast, Brahmins display relatively lower inequality (G = 0.67), benefiting from historical advantages, and Thakur/Kshatriyas show moderate disparities (G = 0.62), with urban areas being slightly more equitable. Among Muslims, Dalit Muslims experience significant rural wealth inequality (G = 0.71), whereas Ansari Muslims and Muslim OBCs (G = 0.65) demonstrate more balanced distributions. Muslim Generals maintain moderate inequality (G = 0.67) across both rural and urban settings, reinforcing the broader trend that wealth disparities exceed consumption gaps across all groups. c) Land Land encompassing natural resources like soil, minerals, forests, and water used for production shows pronounced inequality in distribution. Muslims show higher overall land inequality (G = 0.70) compared to Hindus (G = 0.68). Among Hindus, the Other Hindu group reveal the most severe inequality (G = 0.72), with the Hindu General group in urban areas (G = 0.81) showing greater disparity than rural regions, reflecting concentrated ownership among upper castes. Thakur/Kshatriya sub-castes follow with significant inequality (G = 0.55) and stark urban-rural divides. Hindu Dalits (G = 0.67) and OBCs (G = 0.66) face comparable, though slightly lower disparities. Marginalised Muslim groups, like Ansari Muslims (G = 0.57) and Dalit Muslims (G = 0.60), demonstrate less equitable distribution. However, rural areas remain challenging for Hindu Dalits, Muslim Dalits, and Muslim OBCs, while urban disparities persist for Hindu OBCs, Hindu Generals, and Muslim Generals (G = 0.69). These patterns underscore how land ownership remains skewed along caste and religious lines, with urban-rural dimensions further compounding these inequities. Table 3 Vertical economic inequalities by castes Consumption Wealth Value Land Sub Caste Total Rural Urban Total Rural Urban Total Rural Urban Brahmin 0.37 0.39 0.30 0.67 0.70 0.56 0.65 0.58 0.75 Thakur/Kshatriya 0.29 0.34 0.34 0.62 0.62 0.55 0.55 0.54 0.84 Other Hindu General 0.34 0.45 0.30 0.57 0.78 0.59 0.72 0.56 0.69 Hindu General 0.35 0.34 0.31 0.64 0.66 0.57 0.65 0.55 0.81 Muslim General 0.35 0.36 0.34 0.67 0.68 0.65 0.69 0.62 0.66 Yadav 0.31 0.34 0.32 0.75 0.76 0.64 0.60 0.54 0.74 Kurmi 0.42 0.35 0.29 0.65 0.76 0.44 0.62 0.49 0.80 Jaat 0.42 0.28 0.35 0.67 0.68 0.73 0.52 0.50 0.61 Lodh 0.29 0.27 0.32 0.71 0.70 0.71 0.69 0.60 0.54 Other Hindu OBC 0.33 0.34 0.30 0.77 0.58 0.74 0.65 0.57 0.60 Hindu OBC 0.35 0.35 0.30 0.76 0.77 0.70 0.66 0.60 0.69 Ansari Muslim 0.27 0.27 0.23 0.65 0.68 0.65 0.57 0.63 0.60 Other Muslim OBC 0.27 0.32 0.28 0.65 0.55 0.46 0.65 0.68 0.10 Muslim OBC 0.27 0.33 0.25 0.67 0.68 0.71 0.63 0.66 0.22 Chamaar 0.34 0.29 0.27 0.68 0.62 0.64 0.69 0.68 0.35 Paasi 0.25 0.23 0.26 0.73 0.75 0.69 0.64 0.56 0.32 Other Hindu Dalit 0.30 0.30 0.24 0.63 0.68 0.53 0.61 0.63 0.27 Hindu Dalit 0.33 0.27 0.28 0.68 0.70 0.64 0.67 0.65 0.49 Dalit Muslim 0.29 0.27 0.31 0.71 0.65 0.62 0.60 0.64 0.10 All Hindus 0.38 0.38 0.35 0.73 0.75 0.66 0.68 0.62 0.75 All Muslims 0.30 0.30 0.30 0.69 0.68 0.66 0.70 0.70 0.40 4.3.2. Horizontal economic inequality Figures 2 – 4 collectively reveal stark poverty disparities across India's social hierarchy. Figure 2 demonstrates that Muslim General (1.6 times) and Hindu OBC (1.7 times) groups face substantially higher poverty than Hindu Generals, while Muslim OBCs, Hindu Dalits (2 times), and especially Dalit Muslims (2.3 times) experience the most severe deprivation. Figure 3 's sub-caste-wise analysis shows Yadavs (1.7 times), Lodh (2 times), and Paasi (2.5 times) experiencing worse poverty relative to Brahmins. The comparison of poverty and population share in Fig. 4 suggests an extreme disproportionality: Paasi caste leads with 37% poverty-to-population ratios, followed by Lodh (28.51%) and Kurmi (24.19%). By broader caste groups, Dalit groups show alarming poverty-to-population ratios (Hindu Dalits 12%, Dalit Muslims 5.96%). Privileged groups like Brahmins (1.26%) and Thakur/Kshatriyas (1.46%) maintain near-parity ratios, mirroring the aggregate Hindu advantage (0.48% population share) versus Muslims' disproportionate burden (1.11%). These intersecting verticals (between individuals within castes) and horizontal (between caste-groups) inequalities indicate how social stratification perpetuates economic marginalisation. 4.3.3 Decomposition of economic inequality: ‘Within’ and ‘Between’ caste group contributions Figure 5 presents Pyatt decomposition results quantifying vertical economic inequality across social groups and sub-castes. For social groups (Fig. 5 a), between-group disparities account for 37% (consumption), 20% (wealth), and 30% (land) of total inequality, while within-group variation explains 43%, 41%, and 35% respectively, with overlap components contributing 19%, 39%, and 36%. The sub-caste analysis (Fig. 5 b) reveals stronger between-group effects: 42% (consumption), 43% (wealth), and 53% (land) of inequality stems from sub-caste level between-group differences, compared to 50%, 47%, and 39% from intra-group variation, while overlap terms are notably smaller (9%, 9%, 8%). These patterns demonstrate how economic stratification operates differently across analytical levels - while social group inequality shows substantial overlap between components, sub-caste disparities are more sharply divided between group boundaries, particularly for land ownership, where between-group differences dominate. The consistently higher within-group components across all measures nevertheless confirm that significant economic heterogeneity exists within both broad social categories and finer sub-caste groupings. 4.4. Factors explaining poverty and wealth gap Table 4 explains Blinder-Oaxaca decomposition results, analysing the wealth and consumption gap between absolute and relatively poor and non-poor households using the social safety net programme and the predominance of caste and land size. Caste alone explains about 30% of the overall gap in both wealth (19.47) and consumption (31.20), contributing more to the wealth gap in rural areas (20.73%) and to the consumption gap in urban areas (37.94%). The respondent’s occupation is a significant explanatory factor for the wealth gap (24% overall), particularly in rural areas (35%), while its contribution to the consumption gap is much smaller (9% overall). Household Head’s education also emerges as the major explanatory factor, explaining 23.26% of the wealth gap and a notably higher 32.16% of the consumption gap, with the effect being more pronounced in urban areas for both dimensions (wealth: 37.95%, consumption: 33.82%) The access to the Social Safety Programme accounts for a substantial share of the urban wealth gap (39.81%), and about 29.55% of the urban consumption gap. The land size contributes to the rural wealth and consumption gap of 39.90% and 18.65%. Additionally, Table 5 analyses the wealth gap disparities between Hindu-Muslim General and Dalit households, revealing significant household head education and intergenerational occupation. Land size and social safety net programmes emerge as the most significant explanatory factors, accounting for 48.47% in rural and 35.80% in urban gap. The role of intergenerational occupation contributes 26.41% to urban wealth gap. Grandfather’s Occupation contributes 23.66% overall, with a large effect in rural 29% and 19.64% in urban settings. Father’s occupation explains (-5.93) % of the wealth gap overall, (-15.29%) in rural and 5.88% in Urban. Respondent occupation explains 7.21% in total and 11.46% in rural areas, indicating changing intergenerational occupational mobility. Table 4 Oaxaca decomposition results: Relative contribution of selected factors to wealth gap Wealth Consumption Groups Total Rural Urban Total Rural Urban Poor 168061 175717 150646 1516 1374 1845 Non-poor 108517 112325 96579 1010 977 1103 Difference 59544 63392 54067 506 396 742 Explained 45002 45086 51909 332 273 484 Unexplained 14541 18306 2158 175 123 258 % Explained 75.58 71.12 96.01 65.52 68.93 65.25 % Unexplained 24.42 28.88 3.99 34.48 31.07 34.75 Explanatory Factors: Relative Proportional Contribution Land size 28.99 39.90 11.28 4.11 18.65 -3.68 Caste 19.47 20.73 8.63 31.20 25.44 37.94 Respondent Occupational Composition 13.27 17.15 3.19 8.90 9.92 3.48 Social Safety Programs 17.63 11.72 39.81 22.22 19.00 29.55 Household Head Education 17.87 10.32 34.87 30.22 26.00 33.64 Household Head of working age (15–49) 0.63 0.19 2.22 0.49 0.99 -0.93 Place of Residence 2.15 - - 2.87 - - Total Explained Part 100.00 100.00 100.00 100.00 100.00 100.00 Note: Poor and non-poor households are coded as 1 and 0. Wealth groups are classified using the consumption poverty line, while consumption groups are classified using wealth value score. Table 5 Oaxaca decomposition results: relative contribution of selected factors to wealth gap Groups Total Rural Urban Hindu Muslim General 195229 195505 194609 Hindu Muslim Dalits 77738 74220 86931 Difference 117491 121285 107677 Explained 65.36 62.95 80.84 Unexplained 34.64 37.05 19.16 Explanatory factors: Relative Proportional contribution Land Size 34.97 48.47 10.93 Social Safety Programs 4 12.37 3.49 35.80 Household Head Education 26.45 22.82 27.14 Respondent’s Occupation 7.21 11.46 0.62 Respondent’s Father’s Occupation -5.93 -15.29 5.88 Respondent’s Grandfather’s Occupation 23.66 29.05 19.64 Residence 1.27 NA NA Total Explained Part 100.00 100.00 100.00 4.5. Role of Social Exclusion & Untouchability in Poverty Figure 6 illustrates the relationship between poverty and social exclusion, specifically untouchability and marginalisation, across 14 districts in Uttar Pradesh, India. Kaushambi and Bahraich show the highest poverty ratios (0.53 and 0.52) alongside severe social exclusion (0.93 and 0.87), signifying a strong correlation between deep-rooted social inequalities amid economic hardship. Similar patterns appear in Balrampur (0.46 poverty, 0.85 exclusion), Mau (0.44, 0.81), and Sant Ravidas Nagar (0.44, 0.86), while Kanpur Nagar and Rae Bareli, with moderate poverty (0.31 and 0.30), show high social exclusion (0.92 and 0.93). Even districts with lower poverty, such as Bijnor, Meerut, and Muzaffar Nagar (0.24–0.29), report substantial exclusion (0.75–0.88), districts like Jhansi (poverty: 0.14, exclusion: 0.74) and Buland Shahar (0.17, 0.77), while economically better off, still experience significant caste-based exclusion. 4.7. Robustness Checks 4.7.1 Sensitivity Analyses of Poverty Estimates Table 6 presents a sensitivity analysis of poverty headcount ratios across sub-castes in Uttar Pradesh, using baseline poverty estimates and adjusting the poverty line by ± 50%, ± 25% and ± 15% revealing distinct vulnerability patterns. Forward caste groups such as Brahmins (baseline poverty: 9.04%) and Thakur/Kshatriya (6.08%) show moderate sensitivity, with poverty headcount ratios decreasing to about 1% when poverty line is lowered to 50%, and rising only to 27.70% and 24.32%, respectively when the line is raised by 50%. In contrast, several OBC groups show significantly higher vulnerability. Kurmi (baseline: 43.31%) and Yadav (32.09%) see poverty rates jump to 65.35% and 65.58%, respectively, when the poverty line is raised by 50%. The Lodh sub-caste is among the most affected, with poverty ratios surging from 48% to 78% under a + 50% poverty line adjustment. Muslim General displays heightened sensitivity to downward adjustments (32% to 2% at − 50%) but also significant increases under upward shifts (to 65% at + 50%). Ansari Muslim (36.38% baseline) and Other Muslim OBC (39.00%) both reach over 73% at the + 50% threshold. Muslim OBCs overall (38.03% baseline) also climb to 66.46%. Dalit groups exhibit the most acute vulnerability. Paasi (54.29% baseline) surges to 83.81% at + 50%, Chamaar (41.15%) rises to 73.38%, and Other Hindu Dalits (49.32%) to 77.74%. Hindu Dalits (44.19%) increased to 75.30%. The highest sensitivity is observed among Dalit Muslims—with a baseline poverty of 49.49%—who reach 80.51% at + 50%. The findings make clear that while forward castes are relatively resilient to poverty line shifts, OBCs and Dalits—particularly Muslim Dalits—are highly vulnerable, revealing entrenched structural inequalities that create deep and persistent economic precarity. Table 6 Sensitivity Analysis of Poverty Lines with Different Cut-Off Points. Moving Poverty Line Down by Moving Poverty Line up by Sub castes 50 25 15 15 25 50 Brahmin 0.79 4.32 5.50 13.95 17.68 27.70 Thakur/Kshatriya 1.01 3.38 4.39 10.81 14.19 24.32 Other Hindu General 1.02 3.41 5.46 14.33 17.06 26.62 Hindu General 0.91 3.83 5.19 13.21 16.58 26.50 Muslim General 2.32 14.57 20.03 45.36 52.15 64.90 Yadav 1.86 13.02 19.77 44.19 50.70 65.58 Kurmi 6.30 24.41 32.28 50.39 55.12 65.35 Jaat 1.84 4.15 8.76 20.74 27.19 41.01 Lodh 1.68 26.89 36.97 64.71 66.39 78.15 Other Hindu OBC 2.83 16.47 25.30 50.60 57.10 69.75 Hindu OBC 2.69 15.64 23.70 47.31 53.47 66.46 Ansari Muslim 2.55 18.09 25.96 49.79 59.15 73.62 Other Muslim OBC 2.75 17.25 26.13 53.13 61.12 73.38 Muslim OBC 2.68 17.56 26.06 51.89 60.39 66.46 Chamaar 3.72 18.35 26.86 53.00 60.19 73.38 Paasi 6.67 26.67 38.10 68.57 72.38 83.81 Other Hindu Dalit 6.16 27.05 36.30 61.64 67.12 77.74 Hindu Dalit 4.55 21.12 30.06 56.38 62.88 75.30 Dalit Muslim 7.12 29.49 38.31 62.03 68.81 80.51 All Hindu 2.76 14.30 21.00 41.64 47.23 59.35 All Muslim 3.65 19.68 27.52 52.72 60.39 73.05 UP 3.07 16.17 23.26 45.49 51.80 64.10 5. Conclusion This study provides a comprehensive examination of economic inequalities at the sub-caste level within Hindu and Muslim communities in Uttar Pradesh, India, challenging conventional approaches that analyse caste disparities through broad administrative categories. By incorporating contemporary wealth valuations and multiple economic indicators, including per capita household consumption, asset-based wealth, land ownership, food insecurity, social exclusion, untouchability practices, and access to social safety nets measured through the Human Opportunity Index, our analysis uncovers substantial within-group disparities across the broad caste categorisation that previous research has overlooked. These findings fundamentally reshape the discourse on caste in contemporary India, demonstrating how the inclusion of sub-caste analysis reveals a marked increase in within-caste contributions to overall inequality while reducing the overlap component, as evidenced by Pyatt's decomposition. This study highlights persistent inter-caste hierarchies and pronounced intra-caste economic disparities that shape poverty dynamics, with marginalised groups like Dalit Muslims facing compounded social and economic deprivation comparable to Hindu Dalits, despite remaining largely invisible in policy discussions. Even among relatively privileged groups like the Jats, significant wealth inequality persists, reflecting broader patterns where dominant castes exhibit anxiety about perceived declines in certain domains despite overall economic advantages. This complex landscape reveals a strong correlation between social discrimination and economic disadvantage, particularly evident in the connections between untouchability practices, poverty, and food insecurity. While social safety nets show potential, their effectiveness is uneven, with general castes across religious communities maintaining higher economic status and less internal inequality compared to the stark disparities within OBC and Dalit groups. The study's implications extend beyond Uttar Pradesh, as demonstrated by comparative cases from Punjab and Andhra Pradesh that reveal similar patterns of internal differentiation within scheduled castes (Jodhka & Kumar, 2007 ). These findings advocate the necessity of sub-caste level socioeconomic data to fully comprehend how caste operates in daily life and to design targeted interventions. The growing income and wealth disparities documented in this research demand urgent policy attention, including measures for wealth redistribution, poverty eradication, and the dismantling of caste-based discrimination. Future research should build on these findings by investigating the root causes of persistent poverty and inequality among the most deprived sub-castes and religious groups, particularly Muslims, while moving beyond cross-sectional designs to identify the specific factors that either enable escape from or reinforce entrenchment in poverty. This study ultimately calls for a national-level analysis of intra-group inequalities to inform more nuanced and effective policies for inclusive growth in 21st-century India. Declarations Clinical trial number Not Applicable. Ethics approval: Not Applicable. Competing interests: The authors declare no competing interests. Funding: None to Declare Author Contribution SG and MA Conceptualized the Study, MA compiled the data and conducted formal analyses, MA, SG and TR wrote the paper, All authors reviewed the manuscript and finalized. Acknowledgement Acknowledgement:We are grateful to the discussant, Shohei Nakamura (Senior Economist, World Bank) and the audiences at the IARIW 38th General Conference at King’s College London (2024), as well as Prof. Pawan Kumar (Centre for Economic Study and Planning, Jawahar Lal Nehru University), Winter School at Delhi School of Economics, 2024, for helpful suggestions. We also thank Giri Institute of Development Studies for providing access to Data from the project “Social and Educational Status of OBC/Dalit Muslims in Uttar Pradesh”, supported by the Indian Council of Social Science Research (ICSSR). 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Impact of slum formalisation on self-help housing construction: A case of slum notification in India. Urban Stud. 2014;51(16):3420–44. Nandwani B. Caste and Class. Rev Market Integr. 2016;8(3):135–51. https://doi.org/10.1177/0974929217706807 . Oaxaca R. (1973). Male-female wage differentials in urban labor markets. Int Econ Rev, 693–709. Piketty T. Capital in the 21st century. Inequality in the 21st century. Routledge; 2018. https://scholar.google.com/scholar?cluster=4160484229033938772&hl=en&oi=scholarr . Pyatt G. On the interpretation and disaggregation of Gini coefficients. Econ J. 1976;86(342):243–55. Rawal N. Social inclusion and exclusion: A review. Dhaulagiri J Sociol Anthropol. 2008;2:161–80. Satyanarayana K. Experience and Dalit Theory. Comp Stud South Asia Afr Middle East. 2013;33(3):398–402. Sen A. (1983). Development: Which Way Now? In Source: The Economic Journal (Vol. 93, Issue 372, pp. 745–762). https://www.jstor.org/stable/2232744 Sen A. Human Rights and Capabilities. J Hum Dev. 2005;6(2):151–66. https://doi.org/10.1080/14649880500120491 . Sen A. Development as Freedom: An India Perspective. Indian J Industrial Relations (Vol. 2006;42(2):157–69. Sen AK. Introduction—Development as Freedom. Development as Freedom; 1999. Sooryamoorthy R. Untouchability in Modern India. Int Sociol. 2008;23(2):283–93. https://doi.org/10.1177/0268580907086382 . Thorat A, Vanneman R, Desai S, Dubey A. Escaping and falling into poverty in India today. World Dev. 2017;93:413–26. Thorat S, Lee J. (2005). Caste discrimination and food security programmes. Economic Political Wkly, 4198–201. Tiwari C, Goli S, Siddiqui MZ, Salve PS. Poverty, wealth inequality and financial inclusion among castes in Hindu and Muslim communities in Uttar Pradesh, India. J Int Dev. 2022;34(6):1227–55. https://doi.org/10.1002/jid.3626 . Trivedi PK, Goli S, Kumar S. (2016a). Does Untouchability Exist among Muslims? 15 . Trivedi PK, Goli S, Kumar S. (2016b). Identity Equations and Electoral Politics . 53 . Wittenberg M, Leibbrandt M. Measuring Inequality by Asset Indices: A General Approach with Application to South Africa. Rev Income Wealth. 2017;63(4):706–30. Footnotes India's poverty headcount stood at 21.8% in 2024, measured at $ 3.20 per person per day (PPP-adjusted) using Tendulkar Poverty Line (Bhalla, 2024 ). India Human Development Survey [IHDS], National Sample Survey [NSS], and National Family Health Survey [NFHS] We used Suresh Tendulkar's poverty threshold ₹768 for rural areas and ₹941 for urban areas per person per month for 2011-12 Social safety programmes are defined as welfare policies provided by the Government of Uttar Pradesh. These include access to the Public Distribution System (and its utilisation in the last six months), any insurance or life insurance coverage (LIC), Integrated Child Development Services (ICDS) and Janani Suraksha Yojana (JSY), Kisan Credit Card (KCC), and schemes; Mahatma Gandhi National Rural Employment Act (MGNREGA). Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":39791,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEconomic Inequality in Uttar Pradesh\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7631505/v1/9ec790cde85453196a8536e4.png"},{"id":92584589,"identity":"61317762-4e88-4bb5-a12b-e82ccdb07bce","added_by":"auto","created_at":"2025-10-01 10:13:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":125641,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePoverty ratio across the social groups with reference to Hindu general\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7631505/v1/47fda9604ae553ba160a7d63.png"},{"id":92584590,"identity":"824ab9dc-15a1-4436-a8b5-cae86470edfa","added_by":"auto","created_at":"2025-10-01 10:13:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":131502,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe poverty ratio across the social groups with reference to Brahmins\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7631505/v1/491137f2f6ff10efdddb0eb3.png"},{"id":92584594,"identity":"a269ec72-b2fe-4233-bba3-257d4f9d8a56","added_by":"auto","created_at":"2025-10-01 10:13:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":171158,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePoverty ratio concerning their population share across the castes\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7631505/v1/cd52f6f326721bf83f503232.png"},{"id":92584598,"identity":"e8383bba-446a-4c5e-835a-9f25d3d9807d","added_by":"auto","created_at":"2025-10-01 10:13:29","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":152027,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTotal, between-group, overlap, and within-group inequality by caste groups based on Pyatt’s decomposition\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7631505/v1/6d538ed59c8e94c23314605e.png"},{"id":92584591,"identity":"2dc9d58d-50c5-4e26-a495-abd78cf07f43","added_by":"auto","created_at":"2025-10-01 10:13:29","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":59177,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between poverty and social exclusion \u0026amp; untouchability\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7631505/v1/cca23cea8ad0be789f380513.png"},{"id":92585885,"identity":"87b731bf-43bf-433d-a77e-805f8ee2fcad","added_by":"auto","created_at":"2025-10-01 10:37:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2581879,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7631505/v1/40d37e3a-877c-443d-9cd0-1543d05b7c28.pdf"},{"id":92584588,"identity":"ad8780ee-4928-444d-a0f0-0591b26a4212","added_by":"auto","created_at":"2025-10-01 10:13:29","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18004,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7631505/v1/b2d9333029b4d0d1c346390a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Lottery of Birth Remains: Poverty and Inequality Within and Between Socio-Religious Groups in Uttar Pradesh, India","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePoverty and inequality in India have long been studied in economic terms, and recent scholarship have increasingly emphasised their social foundations. Caste remains one of the most significant determinants of lifetime opportunities in education and employment, reinforcing both social and economic discrimination (Borooah et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Deshpande \u0026amp; Ramachandran, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mosse, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Scholars argue that caste should be integrated into global development policy debates with the same urgency as gender, race, or ethnicity (Desai \u0026amp; Dubey, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Mosse, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Extant literature has shown that socially marginalised groups, including Scheduled Castes (SCs), Scheduled Tribes (STs), Other Backwards Classes (OBCs), (Boorah 20005; Boorah et al., 2007; Bhaumik \u0026amp; Chakrabarty, 2006) and their counterparts within minority religions, experience disproportionately high levels of poverty (Azam, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e; Goli et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Trivedi et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016a\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003eb\u003c/span\u003e). What is under-explored is the sub-caste variations in economic and social indicators, as caste groups are viewed as uniform entities. The present study sets out to explore whether poverty emerges as a visible outcome of deep-rooted structural inequalities driven by sub-caste categories and social exclusion.\u003c/p\u003e\u003cp\u003eIndia\u0026rsquo;s caste system is a rigid social hierarchy based on birth, occupation, and wealth that provides a critical lens for examining intersecting inequalities (Jodhka, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mosse, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For centuries, it has marginalised lower castes, including Scheduled Castes (SCs), Scheduled Tribes (STs), and Other Backwards Classes (OBCs) (Mamgain, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These identities perpetuate vulnerabilities, exacerbating material deprivations and embedding long-term marginalisation (Anand \u0026amp; Moon, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Nakamura, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Recent studies have empirically documented that caste discrimination is not confined to Hindus but also exists in other religions, such as the acute exclusion faced by Dalits in the Muslim community due to intersecting caste and communal identities (Azam, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e; Goli et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Trivedi et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016a\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003eb\u003c/span\u003e). Untouchability persists as a brutal manifestation of caste-based oppression (Kumar et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), even now, the multi-dimensional disadvantages faced by Dalits across non-Hindu religions remain understudied in Indian scholarship.\u003c/p\u003e\u003cp\u003eIn this context, the study examines poverty and wealth inequality and their link to caste-based untouchability and social exclusion in India through a sub-caste lens, utilising a unique survey that enables granular data analysis, unavailable since the discontinuation of India\u0026rsquo;s caste after 1931 Census. This approach offers several advancements: first, sub-caste-level data results in a more precise understanding of caste disparities than broad categories like OBCs and SCs, which aggregate hundreds of distinct castes; second, it facilitates a \"reversal of the gaze\" (Satyanarayana, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) shifting focus from marginalised groups to upper-caste privilege and reframing caste as a system of power rather than just disadvantage; third, it allows for intersectional analysis of caste and economic inequality, opening avenues to study compounded discrimination; and finally, it examines untouchability with marginalisation and social exclusion. To strengthen our analysis, for the first time, we introduce an evidence-based index for measuring social inclusion and untouchability, enhancing the study\u0026rsquo;s analytical rigour. The evidence generated through this study can serve as a valuable tool to revitalise social welfare policies in India, particularly in the context of growing complexities in reservation demands, from the dominant, backwards classes to the upper castes in the country.\u003c/p\u003e\u003cp\u003eNote: Poor and non-poor households are coded as 1 and 0. Wealth groups are classified using the consumption poverty line, while consumption groups are classified using wealth value score.\u003c/p\u003e"},{"header":"2. Background and literature review","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Caste and poverty\u003c/h2\u003e\u003cp\u003ePoverty is defined by a severe lack of resources and financial means, which restricts individuals' freedoms and capabilities. It extends beyond low income to represent the deprivation of opportunities and choices that prevent people from living lives they value(Sen, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1983\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). This perspective underscores poverty\u0026rsquo;s broader impact on human development, where resource scarcity limits access to essential services, education, and healthcare, perpetuating a cycle of deprivation ( Sen, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). However, poverty\u003csup\u003e1\u003c/sup\u003e manifests unevenly across social groups, with empirical evidence indicating that lower-caste individuals face greater economic disadvantages compared to others (Deaton \u0026amp; Dr\u0026egrave;ze, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Kumar et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Despite a rapid decline in poverty rates from 53.6% to 21.9% in 2012, systemic disparities persist, leaving lower castes disproportionately affected and posing a persistent challenge to India\u0026rsquo;s development (Mosse, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Nandwani, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; A. Thorat et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Critically, poverty cannot be understood solely in economic terms as income and consumption, but also diminished human capabilities such as limited access to education, technical skills, healthcare, and nutrition, along with heightened vulnerability to violence, social exclusion, and the denial of dignity and basic rights (Alkire \u0026amp; Seth, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Caste and inequality\u003c/h2\u003e\u003cp\u003eIn India, the caste system operates as a vertical hierarchy founded on the \"principle of gradation and rank,\" which enforces unequal access to economic, educational, and civic rights across caste groups (Geetha, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This structure reinforces hierarchical dominance, systematically narrowing economic and social entitlements from upper to lower castes (Desai \u0026amp; Dubey, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Such inequalities reflect \"historically denied opportunities for lower castes,\" deeply rooted in India\u0026rsquo;s caste system for generations (Deshpande, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), still caste remains a persistent determinant of power (Aggarwal \u0026amp; Dreze, 2025). Traditionally, research on caste relations in India has been dominated by non-economists, anthropologists, sociologists, and historians. However, economic studies on caste and inequality have increasingly examined across social groups in consumption, income, education and occupations using large-scale surveys\u003csup\u003e2\u003c/sup\u003e and smaller primary surveys and the recent Caste Census of Bihar (Ansari \u0026amp; Dhar, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Deshpande, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Demirguc-Kunt et al, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Deshpande \u0026amp; Ramachandran, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Guilmoto, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Hasan \u0026amp; Mehta, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). A near-consensus emerges from both these types of studies: disadvantaged social groups consistently fare worse than others across measured indicators, though regional variations exist (Mosse, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For instance, NSS consumption data analysed by (Kijima \u0026amp; Lanjouw, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) reveal that lower-caste groups face economic disparities due to both lower endowments (physical and human capital) and structural differences in income generation (Dev, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Strikingly, these inequalities have persisted over extended periods, which highlights the enduring rigidity of caste-based disadvantage. Moreover, discrimination manifests across institutional spaces, labour markets, educational institutions, and even marriage practices, further entrenching caste divisions (Banerjee et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Hoff \u0026amp; Pandey, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Caste-based social exclusion, marginalisation, and untouchability\u003c/h2\u003e\u003cp\u003eDeprivation is a material or socio-cultural disadvantage experienced by an individual or group due to different forms of cultural separation and discrimination (Borooah et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Gang et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Untouchability is one such discriminatory process experienced by an individual or group based on their caste identity (Trivedi et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016a\u003c/span\u003e). It poses severe disadvantages regarding unequal distribution of resources and skills- education, training, health, employment, housing, financial resources, social security, and so on (Sooryamoorthy, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). When an individual or group is deprived of income, education, employment, or health opportunities, it is viewed through the basic needs approach (Hammill, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). However, when an individual or group is deprived of knowledge and awareness due to restricted social interactions and cultural participation, it is considered \u0026lsquo;marginalisation\u0026rsquo; within the sociocultural approach (Hasan \u0026amp; Mehta, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Nandwani, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Marginalisation is the discriminatory process that forces individuals or groups to the edge of the economic, political, social, cultural, and ideological system. Social exclusion is an umbrella concept linked with deprivation and marginalisation that extends beyond economic or material exclusion (Jungari \u0026amp; Bomble, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kabeer, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhile caste-based inequalities have been widely studied, most research overlooks sub-caste (\u0026lsquo;biradari\u0026rsquo;) variations, treating social groups as homogeneous rather than examining how internal differentiation shapes economic outcomes. Government of India classifications (SCs, STs, OBCs) often mask critical disparities in poverty and inequality with these broad social groups, particularly in a large and socially heterogeneous state like Uttar Pradesh. By analysing sub-caste-level data, this study provides a more nuanced understanding of deprivation, one that captures the escalating heterogeneity within caste groups. Against this backdrop, this study has two objectives: (1) to measure \u0026lsquo;multiple indices of poverty\u0026rsquo; and \u0026lsquo;inequality\u0026rsquo; in consumption, wealth, and landholding at the sub-caste level for the first time; (2) to assess the relationship between \u0026lsquo;poverty and inequality\u0026rsquo; and \u0026lsquo;processes of social discrimination, such as untouchability. With these objectives, we test the following hypotheses:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eH₁: Socially deprived castes experience higher poverty levels in Uttar Pradesh.\u003c/p\u003e\u003cp\u003eH₂: Untouchability and caste-based discrimination exacerbate poverty.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eBy focusing on sub-caste granularity, this study reveals hidden layers of inequality, offering critical insights into how social exclusion and economic disparities interact, dynamics often obscured by broad caste/social group classifications.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Data and Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1 Data\u003c/h2\u003e\n\u003cp\u003eThis study uses primary data from the Giri Institute of Development Studies (GIDS) under the project \u0026ldquo;Social and Educational Status of OBCs and Dalit Muslims in Uttar Pradesh.\u0026rdquo; The household survey was conducted from October 2014 to April 2015, employing a multi-stage stratified random sampling design for data collection. Fourteen districts were distributed according to the population share from all four state regions: six from Western, two from Central, five from Eastern, and two from the Bundelkhand region. Each district's primary sampling units (PSUs) comprised Gram Panchayats/Villages/Wards and were selected based on probability proportional to size (Goli et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Trivedi et al., \u003cspan class=\"CitationRef\"\u003e2016a\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003eb\u003c/span\u003e). When selecting villages, it was ensured that sample villages had a mix of castes in both religious groups. If there was no caste heterogeneity, the neighboring town was included in PSUs to cover the required caste distribution. Similar procedures were followed in the case of sample selection from the wards of urban areas.\u003c/p\u003e\n\u003cp\u003eThe sample frame for the study design and sampling is the Primary Census Abstract (PCA) of the Census of India, 2011 (Office of RGI and Census Commissioner, 2011). We have ensured a minimum sample of 150 households in a district to ensure a sufficient sample of the six socio-religious groups: Hindu General, Muslim General, Hindu OBCs, Muslim OBCs, Hindu Dalits, and Muslim Dalits. Furthermore, around 50 households from a village and 30 households from a ward in a town/city were selected for the survey. Therefore, the final cumulative sample size of the study was 7124 households from 240 PSUs spreading across 14 districts (for detailed sampling methodology, see Tiwari et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, after accounting for missing cases for some of the study variables, the analytical sample of the study accounts for 7101 households. The caste groups categorised here are based on the \u003cem\u003eVarna\u003c/em\u003e system and their historical dominance in terms of social and economic status. In addition, Data on caste, religion, and social group were collected from three sets of questions and matched with each other while classifying socio-religious groups (SRGs). The focus of this article is on the significant sub-castes (\u003cem\u003ejatis\u003c/em\u003e) from all six SRGs, which have sufficient statistical representation in the sample, \u003cem\u003eBrahmins\u003c/em\u003e and \u003cem\u003eThakurs\u003c/em\u003e from upper caste Hindus; \u003cem\u003eYadavs\u003c/em\u003e, \u003cem\u003eKurmis\u003c/em\u003e, \u003cem\u003eJats\u003c/em\u003e, \u003cem\u003eLodhs\u003c/em\u003e among OBCs; \u003cem\u003eJatav Chamars\u003c/em\u003e and \u003cem\u003ePasis\u003c/em\u003e among Dalits; \u003cem\u003eAnsaris\u003c/em\u003e among OBC Muslims; besides upper caste Muslims and \u003cem\u003eDalit\u003c/em\u003e Muslims. The article prefers the term \u0026ldquo;upper caste\u0026rdquo; over \u0026ldquo;general category\u0026rdquo;, which gives an illusion of a casteless group (Deshpande \u0026amp; Sharma, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2 Choice and measurement of economic variables\u003c/h2\u003e\n\u003cp\u003eThis study examines both social and economic dimensions of household well-being to comprehensively understand sub-caste inequalities in Uttar Pradesh, India. The economic analysis focuses on four key indicators: (1) poverty ratios, (2) consumption expenditure, (3) wealth valuation, and (4) land ownership. Social dimensions include discrimination (particularly untouchability practices), whereas household consumption expenditure remains a standard poverty metric in policy circles, it fails to capture critical aspects of economic status. First, it overlooks household investments in financial and real assets. Second, in Uttar Pradesh's agrarian context, land ownership serves as a distinct and crucial measure of economic standing and could reflect disparities in wealth accumulation.\u003c/p\u003e\n\u003cp\u003eIn this context, our study advances traditional measurement approaches in three significant ways: (1) We move beyond binary asset ownership measures to assess actual wealth value in rupees, addressing valuation limitations identified in prior research (Filmer \u0026amp; Pritchett, \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e; Hlasny \u0026amp; AlAzzawi, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). (2) Following (Piketty, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) emphasis on wealth as the fundamental inequality metric, we account for both physical assets (land, livestock and agricultural equipment) and financial assets (investments and savings). (3) We incorporate multidimensional poverty indicators, including housing quality, durable goods ownership, and access to utilities, recognising their direct relationship with income generation and mobility (Alkire et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). The constructed wealth index uses respondent-reported asset values rather than simple possession counts, providing a more precise measure of economic inequalities than conventional indices (Wittenberg \u0026amp; Leibbrandt, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). The following sections detail our econometric methodology and its application to these multidimensional measures.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003e3.3 Econometric methods\u003c/h2\u003e\n\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n\u003ch2\u003e\u003cstrong\u003e3.3.1 Foster-Greer-Thorbecke (FGT) measures\u003c/strong\u003e:\u003c/h2\u003e\n\u003cp\u003eWe used Foster-Greer-Thorbecke's measures (Foster et al., \u003cspan class=\"CitationRef\"\u003e1984\u003c/span\u003e; Haughton \u0026amp; Khandker, \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e) of poverty for a given population defined in discrete terms:\u003c/p\u003e\n\u003cp\u003eFGT\u003csub\u003e\u003cstrong\u003e(\u0026alpha;)\u003c/strong\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\:\\frac{1}{N}\\sum\\:_{i=1}^{N}{\\left(\\frac{{G}_{i}}{z}\\right)}^{\\alpha\\:}\\)\u003c/span\u003e\u003c/span\u003e\u0026alpha;\u0026gt;=0 (poverty aversion parameter and G\u003csub\u003ei\u003c/sub\u003e = (z-y\u003csub\u003ei\u003c/sub\u003e) (1)\u003c/p\u003e\n\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{G}_{i}\\:\\)\u003c/span\u003e\u003c/span\u003eis the deviation of household consumption from the poverty line, N is the total no of households, y is monthly per capita expenditure to measure poverty ratio, and z is the official poverty line or monetary cut-off for \u003cem\u003eUttar Pradesh\u003c/em\u003e\u003csup\u003e3\u003c/sup\u003e. If household consumption (y\u003csub\u003ei\u003c/sub\u003e) is less than the threshold value (z), then the household will be coded as \u0026lsquo;1\u0026rsquo; if poor; otherwise, \u0026lsquo;0\u0026rsquo; for those above the cut-off or threshold, equally for all the members of the household. All three poverty measures of the FGT family are calculated based on the aversion parameter value of \u0026alpha; concerning censored households.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ea) Head Count Index of Poverty FGT(\u0026alpha;)\u0026thinsp;=\u0026thinsp;0\u003c/em\u003e:\u003c/p\u003e\n\u003cp\u003eFGT \u003csub\u003e(0)\u003c/sub\u003e = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{q}{N}\\)\u003c/span\u003e\u003c/span\u003e, where q is no of poor households (1.1)\u003c/p\u003e\n\u003cp\u003eThe most widely used poverty indicator is the \u0026lsquo;headcount ratio\u0026rsquo; (\u003cem\u003ehereafter\u003c/em\u003e HCR), \u003cem\u003ei.e.\u003c/em\u003e, the proportion of the household population below the state poverty line, Uttar Pradesh. The headcount index is easy to compute and interpret, but has two limitations, which are the following:\u003c/p\u003e\n\u003col style=\"list-style-type: lower-roman;\"\u003e\n\u003cli\u003e\n\u003cp\u003eFirst, it does not capture the intensity of poverty concerning the transfer of income(using consumption as a proxy ) from rich to poor households as a measure of welfare; simply, it violates the transfer principle formulated by (Dalton, \u003cspan class=\"CitationRef\"\u003e1920\u003c/span\u003e).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSecond, the head-count index does not indicate how poor people are experiencing poverty, and hence will not change if households below the poverty line become poorer.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTherefore, we used our second measure: the poverty gap index (FGT\u003csub\u003e1\u003c/sub\u003e), given by the aggregate consumption shortfall of the poor households as a proportion of the poverty line and normalised by the population size.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eb) Poverty Gap Index FGT(\u003c/em\u003e\u003csub\u003e\u003cem\u003e\u0026alpha;\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e)\u0026thinsp;=\u0026thinsp;1\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFGT \u003csub\u003e(1)\u003c/sub\u003e =\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\:\\frac{1}{N}\\sum\\:_{i=1}^{N}{\\left(\\frac{{G}_{i}}{z}\\right)}^{1}\\)\u003c/span\u003e\u003c/span\u003e (1.2)\u003c/p\u003e\n\u003cp\u003eThe poverty gap index quantifies the average distance by which poor households fall below the poverty line, showing this shortfall as a percentage of the line (Haughton \u0026amp; Khandker, \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). This measure represents the mean proportionate poverty gap in the population (where the non-poor household has zero poverty gap). It shows how much \u003cem\u003eincome\u003c/em\u003e or other resources would need to be transferred to the poor to bring their incomes or expenditures up to the poverty line (as a proportion of the poverty line).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ec) Squared Poverty Gap Index FGT(\u0026alpha;)\u0026thinsp;=\u0026thinsp;2\u003c/em\u003e:\u003c/p\u003e\n\u003cp\u003eFGT \u003csub\u003e(2)\u003c/sub\u003e =\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\:\\frac{1}{N}\\sum\\:_{i=1}^{N}{\\left(\\frac{{G}_{i}}{z}\\right)}^{2}\\)\u003c/span\u003e\u003c/span\u003e (1.3)\u003c/p\u003e\n\u003cp\u003eThe squared poverty gap index is a weighted sum that squares households poverty gaps, thereby assigning greater weight to those furthest below the poverty line. This provides a measure of poverty severity, capturing the depth of hardship among the poorest. For a complete analysis, it is essential to use this measure alongside the headcount and poverty gap indices (Haughton \u0026amp; Khandker, \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n\u003ch2\u003e3.3.2 Blinder-Oaxaca decomposition model\u003c/h2\u003e\n\u003cp\u003eWe used the Blinder-Oaxaca linear decomposition model to systematically explain the relative contributions of various factors to the observed inequalities in wealth and consumption distribution across households categorised as poor and non-poor (Blinder, \u003cspan class=\"CitationRef\"\u003e1973\u003c/span\u003e; Oaxaca, \u003cspan class=\"CitationRef\"\u003e1973\u003c/span\u003e). The binary nature of the outcome variable, wherein households are classified as poor (coded as \u0026lsquo;1\u0026rsquo;) or non-poor (coded as \u0026lsquo;0\u0026rsquo;). The focal point of this investigation is the dependent variable, denoted as 'y,' representing the wealth index score. The following expression captures the disparity in mean outcomes between these two groups:\u003c/p\u003e\n\u003cp\u003eThe Blinder-Oaxaca decomposition for Eq.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3.1\u003c/span\u003e has been written in collapsed terms\u003c/p\u003e\n\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ1\" class=\"mathdisplay\"\u003e$$\\:\\varDelta\\:Y=\\left({\\stackrel{-}{X}}_{Poor}-{\\stackrel{-}{X}}_{non-poor}\\right){\\beta\\:}_{non-poor}\\:\\:+\\:\\left({\\stackrel{-}{X}}_{non-poor}\\right)({\\beta\\:}_{poor}\\:-\\:{\\beta\\:}_{non-poor})\\:\\:+\\:\\varDelta\\:\\epsilon\\:\\:\\:$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e3.1\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\stackrel{-}{X}}_{Poor}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\stackrel{-}{X}}_{non-Poor}\\)\u003c/span\u003e\u003c/span\u003e are vectors of explanatory variables estimated at the means of poor and non-poor combined, respectively. Further, we estimated how much of the overall gaps or the gap is specific to any one of the X\u0026rsquo;s (also called the explained component) and differences in \u0026beta;s (also called the unexplained component). It allows for a detailed examination of the extent to which the observed wealth gap is driven by differences in observable characteristics versus the differential impact of these characteristics across the two groups. Mathematically, it is expressed as follows:\u003c/p\u003e\n\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ2\" class=\"mathdisplay\"\u003e$$\\:\\underset{Explained\\:by\\:differecnes\\:in\\:Covariates}{\\underset{⏟}{\\left({\\stackrel{-}{X}}_{Poor}-{\\stackrel{-}{X}}_{non-poor}\\right){\\beta\\:}_{non-poor}}}\\:\\:+\\:\\:\\underset{Explained\\:by\\:differecnes\\:in\\:cofficients}{\\underset{⏟}{\\left({\\stackrel{-}{X}}_{non-poor}\\right)({\\beta\\:}_{poor}\\:-\\:{\\beta\\:}_{non-poor})\\:\\:}}\\:\\:\\:\\:\\:+\\:\\underset{Unexplainded\\:component}{\\underset{⏟}{\\varDelta\\:\\epsilon\\:}}$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e3.2\u003c/div\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n\u003ch2\u003e3.3.3 Social Exclusion and Untouchability Index\u003c/h2\u003e\n\u003cp\u003eSocial exclusion has been defined as \u0026lsquo;the process through which individuals or groups are completely or partially excluded from participation in the society within which they live (Rawal, \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e). Social exclusion has been used differently by scholars, who often use it simultaneously with poverty and deprivation (S. Thorat \u0026amp; Lee, \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e). Untouchability is a distinct Indian social institution that legitimises and enforces practices of discrimination against people born into particular castes and practices that are humiliating, exclusionary, and exploitative. It covers all spheres of life, including social, cultural, and economic, and derives its strength from the concept of purity, one of the essential aspects of the caste system. In its classical form, the caste system considers \u0026lsquo;untouchables\u0026rsquo; and keeps them outside the four-tier system (Jha, \u003cspan class=\"CitationRef\"\u003e1997\u003c/span\u003e). The practice is so vicious that mere touch or a shadow of an \u0026lsquo;untouchable\u0026rsquo; falling on someone else pollutes them. The social exclusion and untouchability index were conceptualised based on questions from socially excluded and untouchable households and their upper caste counterparts (Appendix Table\u0026nbsp;1).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n\u003ch2\u003e3.3.4 Pyatt\u0026rsquo;s Gini decomposition model\u003c/h2\u003e\n\u003cp\u003ePyatt (\u003cspan class=\"CitationRef\"\u003e1976\u003c/span\u003e) developed the decomposition model using the Gini coefficient to calculate the change in inequality (measured using the Gini index [\u003cem\u003ehereafter\u003c/em\u003e, G]) for an economic variable attributable to \u0026lsquo;within\u0026rsquo;, \u0026lsquo;between\u0026rsquo;, and \u0026lsquo;overlapping\u0026rsquo; components of sub-caste groups. We used the same approach to decompose the economic inequality, or G, in consumption or wealth by sub-castes to derive the contribution of \u0026lsquo;between\u0026rsquo; and \u0026lsquo;within group\u0026rsquo; inequalities. The steps of the decomposition procedure are explained below:\u003c/p\u003e\n\u003cp\u003eLet a population of \u0026lsquo;n\u0026rsquo; individuals, with a given consumption or wealth vector (y1, y2, y3 \u0026hellip; y\u003csub\u003en\u003c/sub\u003e) and mean consumption or wealth \u0026lsquo;y\u0026rsquo; is desegregated into \u0026lsquo;k\u0026rsquo; sub-caste groups, with\u003c/p\u003e\n\u003cp\u003en= \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:_{j\\equiv\\:1}^{k}{n}_{j}\\:\\:\\text{a}\\text{n}\\text{d}\\:\\)\u003c/span\u003e\u003c/span\u003esub-caste groups mean is \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\stackrel{-}{y}}_{j}\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eThe G between sub-caste groups \u003cem\u003ej\u003c/em\u003e and \u003cem\u003eh\u003c/em\u003e can be expressed as:\u003c/p\u003e\n\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equa\" class=\"mathdisplay\"\u003e$$\\:{G}_{jh}=\\frac{1}{{n}_{j}{n}_{h}\\left({\\stackrel{-}{y}}_{j}+{\\stackrel{-}{y}}_{h}\\right)}+\\sum\\:_{i=1}^{{n}_{j}}{\\sum\\:}_{i=1}^{{n}_{h}}\\left|{y}_{ji}-{y}_{hr}\\right|\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(4.1\\right)$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eSuppose F(y) be the cumulative distribution function of consumption or wealth vector. In that case, the expected consumption or wealth difference between sub-caste groups \u003cem\u003ej\u003c/em\u003e and \u003cem\u003eh\u003c/em\u003e can be defined as:\u003c/p\u003e\n\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equb\" class=\"mathdisplay\"\u003e$$\\:{d}_{{j}_{h}}^{1}={\\int\\:}_{0}^{x}d{F}_{j}\\left(y\\right)\\:{\\int\\:}_{0}^{x}(y-x)\\:d{F}_{h}\\left(x\\right),\\:for\\:{y}_{ji}\u0026gt;{y}_{hr}\\:and\\:\\:\\:{\\stackrel{-}{y}}_{j}\u0026gt;{\\stackrel{-}{y}}_{h}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(4.2\\right)$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equc\" class=\"mathdisplay\"\u003e$$\\:{d}_{{j}_{h}}^{2}={\\int\\:}_{0}^{x}d{F}_{h}\\left(y\\right)\\:{\\int\\:}_{0}^{x}(y-x)\\:d{F}_{j}\\left(x\\right),\\:for\\:{y}_{ji}\u0026gt;{y}_{hr}\\:and\\:\\:\\:{\\stackrel{-}{y}}_{j}\u0026gt;{\\stackrel{-}{y}}_{h}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(4.3\\right)$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eRelative consumption or wealth affluence is defined as:\u003c/p\u003e\n\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equd\" class=\"mathdisplay\"\u003e$$\\:{D}_{jh}=\\frac{{d}_{{j}_{h}}^{1}-{d}_{{j}_{h}}^{2}}{{d}_{{j}_{h}}^{1}+{d}_{{j}_{h}}^{2}}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(4.4\\right)$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eIf the population shares in sub-caste group \u003cem\u003ej\u003c/em\u003e is \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{p}_{j}=\\frac{{n}_{j}}{n}\\)\u003c/span\u003e\u003c/span\u003e, and consumption or wealth share in sub-caste group \u003cem\u003ej\u003c/em\u003e is \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{s}_{j}=\\frac{{p}_{j}{\\stackrel{-}{y}}_{j}}{\\stackrel{-}{y}}\\:\\)\u003c/span\u003e\u003c/span\u003e. Then the contribution to total inequality attributable to the difference between the \u003cem\u003ek\u003c/em\u003e population sub-caste group is defined as:\u003c/p\u003e\n\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Eque\" class=\"mathdisplay\"\u003e$$\\:{G}_{b}=\\sum\\:_{j=1}^{k}{\\sum\\:}_{h=1\\:j\\ne\\:h}^{k}{G}_{jh}{D}_{jh}\\left({P}_{j}{S}_{h}+\\:{P}_{h}{S}_{j}\\right)\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(4.5\\right)$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eThe G for sub-caste group \u003cem\u003ej\u003c/em\u003e is given by:\u003c/p\u003e\n\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equf\" class=\"mathdisplay\"\u003e$$\\:{G}_{jj}=\\frac{{\\sum\\:}_{i=1}^{{n}_{j}}{\\sum\\:}_{r=1}^{{n}_{j}}{(y}_{ij}{-y}_{rj})}{2{n}_{j}^{2}{\\stackrel{-}{y}}_{j}}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(4.6\\right)$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eThe within-caste group inequality index is the sum of Gini indices for all sub-caste groups weighted by the product of population shares and consumption or wealth shares of the sub-caste groups.\u003c/p\u003e\n\u003cdiv id=\"Equg\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equg\" class=\"mathdisplay\"\u003e$$\\:{G}_{w}=\\:\\sum\\:_{j=1}^{k}{G}_{jj}{P}_{j}{S}_{j}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(4.7\\right)$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eIf sub-caste groups are not overlapping, total inequality can be expressed as the sum of within-caste group and between-caste group indices. However, if sub-caste groups are overlapping, we can add another component, a part of between-group disparities stemming from the overlap factor between the two distributions, which measures the contribution of the intensity of trans-variation between the sub-populations of G:\u003c/p\u003e\n\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ3\" class=\"mathdisplay\"\u003e$$\\:{G}_{t}=\\sum\\:_{j=1}^{k}{\\sum\\:}_{h=1\\:j\\ne\\:k}^{k}{G}_{jh}(1-{D}_{jh})\\:\\left({P}_{j}{S}_{h}+\\:{P}_{h}{S}_{j}\\right)$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e4.8\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eThus, G can be decomposed into three components: within-caste group inequality, between-caste group inequality and inequality due to overlapping factors across caste groups.\u003c/p\u003e\n\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ4\" class=\"mathdisplay\"\u003e$$\\:\\text{G}={G}_{w}+{G}_{b}+Gt$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e4.9\u003c/div\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Results and Discussion","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Descriptive statistics\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents descriptive statistics for 7,101 households across socioeconomic, demographic, and welfare indicators. The sample shows substantial economic disparities with mean monthly per capita expenditure at ₹1,313 (SD=₹1,281) and average household wealth of ₹147,810 (SD=₹427,540), demonstrating significant variation (min\u0026thinsp;=\u0026thinsp;0 to max=₹9,499,000). Caste composition reveals Hindu OBCs as the largest group (33%), followed by Hindu General (15%), Muslim OBC (18%), and Hindu Dalit (17%). Welfare program access remains non-limited, 79% having PDS availability and 78% utilising it, while rural-specific schemes like MGNREGA show 26% coverage. Educational attainment of the sample shows 32% lack formal education versus 10% with graduation degrees. The sample is predominantly rural (72%), with a high prevalence of social exclusion (85% experience untouchability) and economic vulnerability (40% relatively poor, 34% BPL poor). The sample distribution across socio-economic characteristics aligns with the population profile of the state as reported in the Census of India and other large-scale surveys.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary statistics of the study variables (N\u0026thinsp;=\u0026thinsp;7101)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eObservation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStd. Dev\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonthly average per capita expenditure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7,101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,313\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,281\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e27,748\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThe average wealth value of the households\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7,101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e147,810\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e427,540\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9,499,000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHindu General\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7,101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMuslim General\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7,101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHindu OBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7,101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMuslim OBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7,101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHindu Dalit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7,101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDalit Muslims\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7,101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePDS availability (Yes/No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7,101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePDS utilisation (in the last 6 months)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7,101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKisan @ Rural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5,086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMGNREGA @ Rural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5,086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICDS @ rural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5,086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJSY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7,101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOld age pension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7,101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWidow pension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7,101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold Age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7,101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold Occupation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7,101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7,101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBelow Primary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7,101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBelow Secondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7,101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGraduation \u0026amp; above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7,101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRelatively Poor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7,101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUntouchability \u0026amp; Social Exclusion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7,101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSource: Authors\u0026rsquo; estimation based on GIDS survey on Social and Educational Status of OBC/Dalit Muslims in Uttar Pradesh\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Poverty by castes\u003c/h2\u003e\u003cp\u003eThis study employs three established Foster-Greer-Thorbecke measures of poverty metrics to analyse economic inequality across caste and sub-caste (\u003cem\u003ebiradari\u003c/em\u003e) groups: (1) poverty headcount ratio, (2) poverty gap index, and (3) squared poverty gap index. The subsequent sections present these findings through a caste-based analytical framework:\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e4.2.1. Headcounts\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reveals pronounced socioeconomic inequalities in Uttar Pradesh through poverty headcount ratios, with the state average at 34%. The data demonstrate a clear caste gradient: Brahmins, occupying the highest social position, exhibit markedly lower poverty rates (9% overall), particularly in urban areas (2.1%), compared to their rural counterparts (11.7%). Conversely, historically marginalised groups face substantially higher consumption deprivation, with Chamars (44%) and Paasis (55%) bearing the heaviest burdens, especially in rural contexts. Furthermore, \u003cem\u003eReligious\u003c/em\u003e dimensions compound these disparities as Hindu Dalits (44%) and Muslim Dalits (50%) experience high poverty, whereas Muslim OBCs (38%) and Hindu OBCs (35%) show significantly higher deprivation levels than Hindu General castes (8%). These findings underscore the intersectional nature of poverty, where caste hierarchy and religious identity collectively shape economic outcomes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e4.2.2. Poverty gap\u003c/h2\u003e\u003cp\u003eThe poverty gap ratio quantifies the depth of poverty by measuring how far households fall below the poverty line. Our findings reveal stark disparities across social groups. Privileged castes like Thakur/Kshatriya and Brahmins show minimal poverty gaps (approximately 2%), indicating their relative economic security. In contrast, marginalised groups show substantially higher ratios: Kurmis (17% rural), Paasis (16% urban), Lodhs (15% rural), and Chamars (10% overall). Religious-caste intersections further exacerbate these inequalities. Urban Dalit Muslims face a particularly severe poverty gap (15%), surpassing their Hindu Dalit counterparts in rural areas (11%). Meanwhile, advantaged groups have significantly lower ratios: Hindu General (2%) and Muslim General (8%), highlighting the compounding effects of caste hierarchy and religious identity on economic deprivation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e4.2.3. Squared poverty gap\u003c/h2\u003e\u003cp\u003eThe squared poverty gap ratio, which measures the severity of poverty by calculating the average squared distance below the poverty line, reveals significant disparities across social groups. Our findings demonstrate that historically disadvantaged communities endure the most severe poverty burdens: Hindu Dalits (4%) and Dalit Muslims (6%) show markedly higher ratios compared to other groups. This pattern persists among specific castes, with the Lodh community (4%) and other Hindu Dalit subgroups (6%) experiencing particularly acute deprivation. The data further highlights intergroup disparities within Muslim communities. While Ansari Muslims maintain a relatively moderate ratio (3%) and Dalit Muslims face double severity (6%), underscoring how caste-based marginalisation compounds poverty even within religious groups. These results emphasise that existing social hierarchies, with doubly disadvantaged groups (by both caste and religion), suffer the most intense economic deprivation.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAbsolute poverty ratios by caste in Uttar Pradesh, India\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eHeadcounts\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003ePoverty Gap\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003eSquared Poverty Gap\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCaste\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBrahmin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThakur/Kshatriya\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Hindu General\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHindu General\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e8.20\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e10.95\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e2.06\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e2.32\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003e3.28\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e0.20\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003e1.2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003e1.78\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003e0.02\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMuslim General\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e32.28\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e35.32\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e25.41\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e7.91\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003e8.46\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e6.67\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003e3.13\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003e3.05\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003e3.29\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYadav\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKurmi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJaat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLodh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Hindu OBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHindu OBC\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e35.14\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e36.73\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e30.51\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e8.75\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003e9.25\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e7.28\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003e3.3\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003e3.63\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003e2.37\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnsari Muslims\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e3.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Muslim OBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e4.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMuslim OBC\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e38.03\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e37.30\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e39.65\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e9.30\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003e9.03\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e9.90\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003e3.3\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003e3.14\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003e3.77\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChamar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e3.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePaasi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e15.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e6.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Hindu Dalit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e18.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHindu Dalit\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e44.19\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e48.31\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e32.05\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e11.44\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003e12.51\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e8.32\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003e4.4\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003e4.80\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003e3.30\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDalit Muslims\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e49.49\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e50.00\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e48.44\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e14.77\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003e14.92\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e14.46\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003e5.96\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003e5.98\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003e5.92\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAll Hindu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAll Muslim\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e4.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eUttar Pradesh\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e34.01\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e36.00\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e29.00\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e8.75\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003e9.29\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e7.38\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003e3.39\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003e3.62\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003e2.82\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Economic inequality\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents economic inequality through G coefficients across three dimensions: wealth (G\u0026thinsp;=\u0026thinsp;0.72), land holdings (G\u0026thinsp;=\u0026thinsp;0.67), and per capita consumption (G\u0026thinsp;=\u0026thinsp;0.36). Wealth inequality is most severe, especially in rural areas (G\u0026thinsp;=\u0026thinsp;0.75), while consumption shows minimal urban-rural variation (urban G\u0026thinsp;=\u0026thinsp;0.35 vs. overall 0.36). Land inequality remains consistently high across both urban (G\u0026thinsp;=\u0026thinsp;0.67) and rural (G\u0026thinsp;=\u0026thinsp;0.66) contexts. These patterns reveal distinct layers of economic inequality that will be further investigated through sub-caste analysis, examining both vertical (inter-caste) and horizontal (intra-caste) dimensions of inequality.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003e4.3.1. Vertical economic inequality\u003c/h2\u003e\u003cp\u003e\u003cem\u003ea) Consumption\u003c/em\u003e\u003c/p\u003e\u003cp\u003eConsumption, defined as the utilisation of goods and services to meet household needs, shows significant disparities when analysed through G coefficients, with Hindu communities exhibiting higher overall consumption inequality (G\u0026thinsp;=\u0026thinsp;0.38) compared to Muslims (G\u0026thinsp;=\u0026thinsp;0.30). Among Hindu sub-castes, the Kurmi and Jaat groups display the most marked inequality (G\u0026thinsp;=\u0026thinsp;0.42), followed by Brahmins (G\u0026thinsp;=\u0026thinsp;0.37), particularly in rural areas. In contrast, Dalit communities like Chamaar (G\u0026thinsp;=\u0026thinsp;0.34) and Paasi (G\u0026thinsp;=\u0026thinsp;0.25) demonstrate more uniform consumption patterns, a trend consistent across urban and rural settings. Within Muslim groups, Ansari Muslims (G\u0026thinsp;=\u0026thinsp;0.27) and Dalit Muslims (G\u0026thinsp;=\u0026thinsp;0.29) exhibit the most equitable consumption distribution, while Muslim General (G\u0026thinsp;=\u0026thinsp;0.35) and Muslim OBC (G\u0026thinsp;=\u0026thinsp;0.27) sub-castes show moderate disparities, with slightly lower inequality in urban areas. These findings highlight how consumption inequality varies not only between religious groups but also across sub-castes, reflecting deeper socioeconomic stratification.\u003c/p\u003e\u003cp\u003e\u003cem\u003eb) Wealth\u003c/em\u003e\u003c/p\u003e\u003cp\u003eWealth, representing accumulated assets and resources that ensure financial security, exhibits deeper disparities than consumption when measured by G coefficients, with Hindus showing higher overall wealth inequality (G\u0026thinsp;=\u0026thinsp;0.76 for OBCs) compared to Muslims. The Hindu OBC community demonstrates the most pronounced wealth concentration, especially in urban areas (G\u0026thinsp;=\u0026thinsp;0.74), reflecting uneven access to economic opportunities, while Hindu Dalits face substantial rural-urban divides (G\u0026thinsp;=\u0026thinsp;0.70 rural vs. 0.64 urban). In contrast, Brahmins display relatively lower inequality (G\u0026thinsp;=\u0026thinsp;0.67), benefiting from historical advantages, and Thakur/Kshatriyas show moderate disparities (G\u0026thinsp;=\u0026thinsp;0.62), with urban areas being slightly more equitable. Among Muslims, Dalit Muslims experience significant rural wealth inequality (G\u0026thinsp;=\u0026thinsp;0.71), whereas Ansari Muslims and Muslim OBCs (G\u0026thinsp;=\u0026thinsp;0.65) demonstrate more balanced distributions. Muslim Generals maintain moderate inequality (G\u0026thinsp;=\u0026thinsp;0.67) across both rural and urban settings, reinforcing the broader trend that wealth disparities exceed consumption gaps across all groups.\u003c/p\u003e\u003cp\u003ec) \u003cem\u003eLand\u003c/em\u003e\u003c/p\u003e\u003cp\u003eLand encompassing natural resources like soil, minerals, forests, and water used for production shows pronounced inequality in distribution. Muslims show higher overall land inequality (G\u0026thinsp;=\u0026thinsp;0.70) compared to Hindus (G\u0026thinsp;=\u0026thinsp;0.68). Among Hindus, the Other Hindu group reveal the most severe inequality (G\u0026thinsp;=\u0026thinsp;0.72), with the Hindu General group in urban areas (G\u0026thinsp;=\u0026thinsp;0.81) showing greater disparity than rural regions, reflecting concentrated ownership among upper castes. Thakur/Kshatriya sub-castes follow with significant inequality (G\u0026thinsp;=\u0026thinsp;0.55) and stark urban-rural divides. Hindu Dalits (G\u0026thinsp;=\u0026thinsp;0.67) and OBCs (G\u0026thinsp;=\u0026thinsp;0.66) face comparable, though slightly lower disparities. Marginalised Muslim groups, like Ansari Muslims (G\u0026thinsp;=\u0026thinsp;0.57) and Dalit Muslims (G\u0026thinsp;=\u0026thinsp;0.60), demonstrate less equitable distribution. However, rural areas remain challenging for Hindu Dalits, Muslim Dalits, and Muslim OBCs, while urban disparities persist for Hindu OBCs, Hindu Generals, and Muslim Generals (G\u0026thinsp;=\u0026thinsp;0.69). These patterns underscore how land ownership remains skewed along caste and religious lines, with urban-rural dimensions further compounding these inequities.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eVertical economic inequalities by castes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eConsumption\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eWealth Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003eLand\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSub Caste\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBrahmin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThakur/Kshatriya\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Hindu General\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHindu General\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMuslim General\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYadav\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKurmi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJaat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLodh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Hindu OBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHindu OBC\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnsari Muslim\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Muslim OBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMuslim OBC\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChamaar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePaasi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Hindu Dalit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHindu Dalit\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDalit Muslim\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAll Hindus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAll Muslims\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\u003ch2\u003e4.3.2. Horizontal economic inequality\u003c/h2\u003e\u003cp\u003eFigures \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e collectively reveal stark poverty disparities across India's social hierarchy. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e demonstrates that Muslim General (1.6 times) and Hindu OBC (1.7 times) groups face substantially higher poverty than Hindu Generals, while Muslim OBCs, Hindu Dalits (2 times), and especially Dalit Muslims (2.3 times) experience the most severe deprivation. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e's sub-caste-wise analysis shows Yadavs (1.7 times), Lodh (2 times), and Paasi (2.5 times) experiencing worse poverty relative to Brahmins. The comparison of poverty and population share in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e suggests an extreme disproportionality: Paasi caste leads with 37% poverty-to-population ratios, followed by Lodh (28.51%) and Kurmi (24.19%). By broader caste groups, Dalit groups show alarming poverty-to-population ratios (Hindu Dalits 12%, Dalit Muslims 5.96%). Privileged groups like Brahmins (1.26%) and Thakur/Kshatriyas (1.46%) maintain near-parity ratios, mirroring the aggregate Hindu advantage (0.48% population share) versus Muslims' disproportionate burden (1.11%). These intersecting verticals (between individuals within castes) and horizontal (between caste-groups) inequalities indicate how social stratification perpetuates economic marginalisation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\u003ch2\u003e4.3.3 Decomposition of economic inequality: \u0026lsquo;Within\u0026rsquo; and \u0026lsquo;Between\u0026rsquo; caste group contributions\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents Pyatt decomposition results quantifying vertical economic inequality across social groups and sub-castes. For social groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea), between-group disparities account for 37% (consumption), 20% (wealth), and 30% (land) of total inequality, while within-group variation explains 43%, 41%, and 35% respectively, with overlap components contributing 19%, 39%, and 36%. The sub-caste analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb) reveals stronger between-group effects: 42% (consumption), 43% (wealth), and 53% (land) of inequality stems from sub-caste level between-group differences, compared to 50%, 47%, and 39% from intra-group variation, while overlap terms are notably smaller (9%, 9%, 8%). These patterns demonstrate how economic stratification operates differently across analytical levels - while social group inequality shows substantial overlap between components, sub-caste disparities are more sharply divided between group boundaries, particularly for land ownership, where between-group differences dominate. The consistently higher within-group components across all measures nevertheless confirm that significant economic heterogeneity exists within both broad social categories and finer sub-caste groupings.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e4.4. Factors explaining poverty and wealth gap\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e explains Blinder-Oaxaca decomposition results, analysing the wealth and consumption gap between absolute and relatively poor and non-poor households using the social safety net programme and the predominance of caste and land size. Caste alone explains about 30% of the overall gap in both wealth (19.47) and consumption (31.20), contributing more to the wealth gap in rural areas (20.73%) and to the consumption gap in urban areas (37.94%). The respondent\u0026rsquo;s occupation is a significant explanatory factor for the wealth gap (24% overall), particularly in rural areas (35%), while its contribution to the consumption gap is much smaller (9% overall). Household Head\u0026rsquo;s education also emerges as the major explanatory factor, explaining 23.26% of the wealth gap and a notably higher 32.16% of the consumption gap, with the effect being more pronounced in urban areas for both dimensions (wealth: 37.95%, consumption: 33.82%) The access to the Social Safety Programme accounts for a substantial share of the urban wealth gap (39.81%), and about 29.55% of the urban consumption gap. The land size contributes to the rural wealth and consumption gap of 39.90% and 18.65%.\u003c/p\u003e\u003cp\u003eAdditionally, Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e analyses the wealth gap disparities between Hindu-Muslim General and Dalit households, revealing significant household head education and intergenerational occupation. Land size and social safety net programmes emerge as the most significant explanatory factors, accounting for 48.47% in rural and 35.80% in urban gap. The role of intergenerational occupation contributes 26.41% to urban wealth gap. Grandfather\u0026rsquo;s Occupation contributes 23.66% overall, with a large effect in rural\u003c/p\u003e\u003cp\u003e29% and 19.64% in urban settings. Father\u0026rsquo;s occupation explains (-5.93) % of the wealth gap overall, (-15.29%) in rural and 5.88% in Urban. Respondent occupation explains 7.21% in total and 11.46% in rural areas, indicating changing intergenerational occupational mobility.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOaxaca decomposition results: Relative contribution of selected factors to wealth gap\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eWealth\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eConsumption\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroups\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e168061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e175717\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e150646\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1374\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1845\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-poor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e108517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e112325\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e96579\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e977\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1103\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDifference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59544\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63392\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e396\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e742\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExplained\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51909\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e332\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e273\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e484\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnexplained\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18306\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e258\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e% Explained\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e96.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e65.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e68.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e65.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e% Unexplained\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e34.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e31.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e34.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eExplanatory Factors: Relative Proportional Contribution\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLand size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e18.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-3.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCaste\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e31.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e25.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e37.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespondent Occupational Composition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial Safety Programs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e19.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e29.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold Head Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e30.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e26.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e33.64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold Head of working age (15\u0026ndash;49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlace of Residence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Explained Part\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e100.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e100.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: Poor and non-poor households are coded as 1 and 0. Wealth groups are classified using the consumption poverty line, while consumption groups are classified using wealth value score.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOaxaca decomposition results: relative contribution of selected factors to wealth gap\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroups\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHindu Muslim General\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e195229\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e195505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e194609\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHindu Muslim Dalits\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e77738\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86931\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDifference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e117491\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e121285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e107677\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExplained\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnexplained\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eExplanatory factors: Relative Proportional contribution\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLand Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial Safety Programs\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold Head Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespondent\u0026rsquo;s Occupation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespondent\u0026rsquo;s Father\u0026rsquo;s Occupation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-5.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-15.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespondent\u0026rsquo;s Grandfather\u0026rsquo;s Occupation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Explained Part\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e4.5. Role of Social Exclusion \u0026amp; Untouchability in Poverty\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e illustrates the relationship between poverty and social exclusion, specifically untouchability and marginalisation, across 14 districts in Uttar Pradesh, India. Kaushambi and Bahraich show the highest poverty ratios (0.53 and 0.52) alongside severe social exclusion (0.93 and 0.87), signifying a strong correlation between deep-rooted social inequalities amid economic hardship. Similar patterns appear in Balrampur (0.46 poverty, 0.85 exclusion), Mau (0.44, 0.81), and Sant Ravidas Nagar (0.44, 0.86), while Kanpur Nagar and Rae Bareli, with moderate poverty (0.31 and 0.30), show high social exclusion (0.92 and 0.93). Even districts with lower poverty, such as Bijnor, Meerut, and Muzaffar Nagar (0.24\u0026ndash;0.29), report substantial exclusion (0.75\u0026ndash;0.88), districts like Jhansi (poverty: 0.14, exclusion: 0.74) and Buland Shahar (0.17, 0.77), while economically better off, still experience significant caste-based exclusion.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e4.7. Robustness Checks\u003c/h2\u003e\u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\u003ch2\u003e4.7.1 Sensitivity Analyses of Poverty Estimates\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents a sensitivity analysis of poverty headcount ratios across sub-castes in Uttar Pradesh, using baseline poverty estimates and adjusting the poverty line by \u0026plusmn;\u0026thinsp;50%, \u0026plusmn;\u0026thinsp;25% and \u0026plusmn;\u0026thinsp;15% revealing distinct vulnerability patterns. Forward caste groups such as Brahmins (baseline poverty: 9.04%) and Thakur/Kshatriya (6.08%) show moderate sensitivity, with poverty headcount ratios decreasing to about 1% when poverty line is lowered to 50%, and rising only to 27.70% and 24.32%, respectively when the line is raised by 50%. In contrast, several OBC groups show significantly higher vulnerability. Kurmi (baseline: 43.31%) and Yadav (32.09%) see poverty rates jump to 65.35% and 65.58%, respectively, when the poverty line is raised by 50%. The Lodh sub-caste is among the most affected, with poverty ratios surging from 48% to 78% under a\u0026thinsp;+\u0026thinsp;50% poverty line adjustment.\u003c/p\u003e\u003cp\u003eMuslim General displays heightened sensitivity to downward adjustments (32% to 2% at \u0026minus;\u0026thinsp;50%) but also significant increases under upward shifts (to 65% at +\u0026thinsp;50%). Ansari Muslim (36.38% baseline) and Other Muslim OBC (39.00%) both reach over 73% at the +\u0026thinsp;50% threshold. Muslim OBCs overall (38.03% baseline) also climb to 66.46%. Dalit groups exhibit the most acute vulnerability. Paasi (54.29% baseline) surges to 83.81% at +\u0026thinsp;50%, Chamaar (41.15%) rises to 73.38%, and Other Hindu Dalits (49.32%) to 77.74%. Hindu Dalits (44.19%) increased to 75.30%. The highest sensitivity is observed among Dalit Muslims\u0026mdash;with a baseline poverty of 49.49%\u0026mdash;who reach 80.51% at +\u0026thinsp;50%. The findings make clear that while forward castes are relatively resilient to poverty line shifts, OBCs and Dalits\u0026mdash;particularly Muslim Dalits\u0026mdash;are highly vulnerable, revealing entrenched structural inequalities that create deep and persistent economic precarity.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSensitivity Analysis of Poverty Lines with Different Cut-Off Points.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eMoving Poverty Line Down by\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eMoving Poverty Line up by\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSub castes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBrahmin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e27.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThakur/Kshatriya\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e24.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Hindu General\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e26.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHindu General\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e0.91\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e3.83\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e5.19\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e13.21\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003e16.58\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e26.50\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMuslim General\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e2.32\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e14.57\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e20.03\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e45.36\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003e52.15\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e64.90\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYadav\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e44.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e50.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e65.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKurmi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003e55.12\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e65.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJaat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e27.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e41.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLodh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e64.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e66.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e78.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Hindu OBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e57.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e69.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHindu OBC\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e2.69\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e15.64\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e23.70\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e47.31\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003e53.47\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e66.46\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnsari Muslim\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e49.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e59.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e73.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Muslim OBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e53.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e61.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e73.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMuslim OBC\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e2.68\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e17.56\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e26.06\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e51.89\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003e60.39\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e66.46\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChamaar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e53.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e60.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e73.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePaasi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e68.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e72.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e83.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Hindu Dalit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e61.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e67.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e77.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHindu Dalit\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e4.55\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e21.12\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e30.06\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e56.38\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003e62.88\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e75.30\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDalit Muslim\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e7.12\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e29.49\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e38.31\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e62.03\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003e68.81\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e80.51\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAll Hindu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e47.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e59.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAll Muslim\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e52.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e60.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e73.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e51.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e64.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study provides a comprehensive examination of economic inequalities at the sub-caste level within Hindu and Muslim communities in Uttar Pradesh, India, challenging conventional approaches that analyse caste disparities through broad administrative categories. By incorporating contemporary wealth valuations and multiple economic indicators, including per capita household consumption, asset-based wealth, land ownership, food insecurity, social exclusion, untouchability practices, and access to social safety nets measured through the Human Opportunity Index, our analysis uncovers substantial within-group disparities across the broad caste categorisation that previous research has overlooked. These findings fundamentally reshape the discourse on caste in contemporary India, demonstrating how the inclusion of sub-caste analysis reveals a marked increase in within-caste contributions to overall inequality while reducing the overlap component, as evidenced by Pyatt's decomposition.\u003c/p\u003e\u003cp\u003eThis study highlights persistent inter-caste hierarchies and pronounced intra-caste economic disparities that shape poverty dynamics, with marginalised groups like Dalit Muslims facing compounded social and economic deprivation comparable to Hindu Dalits, despite remaining largely invisible in policy discussions. Even among relatively privileged groups like the Jats, significant wealth inequality persists, reflecting broader patterns where dominant castes exhibit anxiety about perceived declines in certain domains despite overall economic advantages. This complex landscape reveals a strong correlation between social discrimination and economic disadvantage, particularly evident in the connections between untouchability practices, poverty, and food insecurity. While social safety nets show potential, their effectiveness is uneven, with general castes across religious communities maintaining higher economic status and less internal inequality compared to the stark disparities within OBC and Dalit groups. The study's implications extend beyond Uttar Pradesh, as demonstrated by comparative cases from Punjab and Andhra Pradesh that reveal similar patterns of internal differentiation within scheduled castes (Jodhka \u0026amp; Kumar, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThese findings advocate the necessity of sub-caste level socioeconomic data to fully comprehend how caste operates in daily life and to design targeted interventions. The growing income and wealth disparities documented in this research demand urgent policy attention, including measures for wealth redistribution, poverty eradication, and the dismantling of caste-based discrimination. Future research should build on these findings by investigating the root causes of persistent poverty and inequality among the most deprived sub-castes and religious groups, particularly Muslims, while moving beyond cross-sectional designs to identify the specific factors that either enable escape from or reinforce entrenchment in poverty. This study ultimately calls for a national-level analysis of intra-group inequalities to inform more nuanced and effective policies for inclusive growth in 21st-century India.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eClinical trial number\u003c/h2\u003e\u003cp\u003eNot Applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthics approval:\u003c/strong\u003e\u003cp\u003eNot Applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eNone to Declare\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSG and MA Conceptualized the Study, MA compiled the data and conducted formal analyses, MA, SG and TR wrote the paper, All authors reviewed the manuscript and finalized.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eAcknowledgement:We are grateful to the discussant, Shohei Nakamura (Senior Economist, World Bank) and the audiences at the IARIW 38th General Conference at King\u0026rsquo;s College London (2024), as well as Prof. Pawan Kumar (Centre for Economic Study and Planning, Jawahar Lal Nehru University), Winter School at Delhi School of Economics, 2024, for helpful suggestions. We also thank Giri Institute of Development Studies for providing access to Data from the project \u0026ldquo;Social and Educational Status of OBC/Dalit Muslims in Uttar Pradesh\u0026rdquo;, supported by the Indian Council of Social Science Research (ICSSR).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analysed for the current study are publicly available at the Indian Council for Social Science Research (ICSSR) data repository and can be obtained by registering to their portal.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAggarwal A, Dr\u0026egrave;ze J, Gupta A. (2015). Caste and the power elite in Allahabad. Economic Political Wkly, 45\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlkire S, Foster JE, Seth S, Santos ME, Roche J, Ballon P. 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Rev Income Wealth. 2017;63(4):706\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e India's poverty headcount stood at 21.8% in 2024, measured at \u003cspan\u003e$\u003c/span\u003e3.20 per person per day (PPP-adjusted) using \u003cem\u003eTendulkar Poverty Line\u003c/em\u003e (Bhalla, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e India Human Development Survey [IHDS], National Sample Survey [NSS], and National Family Health Survey [NFHS]\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWe used Suresh Tendulkar's poverty threshold ₹768 for rural areas and ₹941 for urban areas per person per month for 2011-12\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Social safety programmes are defined as welfare policies provided by the Government of Uttar Pradesh. These include access to the Public Distribution System (and its utilisation in the last six months), any insurance or life insurance coverage (LIC), Integrated Child Development Services (ICDS) and Janani Suraksha Yojana (JSY), Kisan Credit Card (KCC), and schemes; Mahatma Gandhi National Rural Employment Act (MGNREGA).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-economics-race-and-policy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jerp","sideBox":"Learn more about [Journal of Economics, Race, and Policy](https://link.springer.com/journal/41996)","snPcode":"41996","submissionUrl":"https://submission.nature.com/new-submission/41996/3","title":"Journal of Economics, Race, and Policy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Castes, Poverty, Wealth, Inequality, Social Exclusion, Untouchability","lastPublishedDoi":"10.21203/rs.3.rs-7631505/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7631505/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study utilises a unique survey of 7,101 households from Uttar Pradesh, India, to conduct the first comprehensive measurement of poverty and inequality across consumption, wealth, and landholding at the sub-caste (\u003cem\u003ebiradari\u003c/em\u003e) level. Our findings reveal prominent disparities in poverty incidence, ranging from 6% among \u003cem\u003eBrahmins\u003c/em\u003e to 55% among \u003cem\u003ePaasis\u003c/em\u003e. The analysis also demonstrates substantial vertical economic inequality within broad social-religious groups, with Gini coefficients of 0.36 for consumption, 0.72 for wealth, and 0.66 for landholding across sub-castes. The results highlight significant variations between poverty and inequality by sub-caste. We further identify a positive correlation between social exclusion/untouchability practices and poverty across districts. The role of caste, land size and Intergenerational occupation attainment and household head education status emerge as significant predictors of sub-caste inequality in household consumption and wealth value. This study contributes significantly to the economic literature on caste stratification, discrimination, and human development.\u003c/p\u003e","manuscriptTitle":"The Lottery of Birth Remains: Poverty and Inequality Within and Between Socio-Religious Groups in Uttar Pradesh, India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-01 10:13:25","doi":"10.21203/rs.3.rs-7631505/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-20T19:01:39+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-20T16:36:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-13T19:44:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-10T03:05:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"201785189053626988675973776056243924275","date":"2025-09-29T22:18:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"15313784456298291506245168810893250086","date":"2025-09-26T05:08:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4404391953390889540520703263729477891","date":"2025-09-21T02:03:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-20T14:22:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-18T15:18:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-18T11:47:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Economics, Race, and Policy","date":"2025-09-16T14:19:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-economics-race-and-policy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jerp","sideBox":"Learn more about [Journal of Economics, Race, and Policy](https://link.springer.com/journal/41996)","snPcode":"41996","submissionUrl":"https://submission.nature.com/new-submission/41996/3","title":"Journal of Economics, Race, and Policy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"6c55c7c2-0659-4187-b3f2-7e0660862fff","owner":[],"postedDate":"October 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-07T11:38:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-01 10:13:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7631505","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7631505","identity":"rs-7631505","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}