MEASURING POVERTY IN UTTAR PRADESH, INDIA: FROM UNIDIMENSIONAL TO MULTIDIMENSIONAL APPROACH

Abstract

Uttar Pradesh (U.P) is home to around twenty-two per cent of the poor in India, whereas it holds a share of sixteen per cent of the overall population of the country. Besides this disproportionality, it accounts for the largest proportion of Scheduled Castes (SCs), Muslims and the rural population of India. Looking at geographical differences and social realities within U.P, it is essential to analyse poverty across regions, districts and social and religious groups (SRGs) of the state. However, there is a dearth of studies based on regional and district level statistics and across SRGs in the state. Poverty across SRGs is critically significant to monitor, particularly in recent years, as earlier data could not include Other Backward Classes (OBCs) as a separate category due to the unavailability of classified data. Beyond the domain of analysis, there is conceptual and normative justification related to the notion of poverty that is largely neglected by earlier studies on poverty. As far as the Indian notion of poverty is concerned, identification of the poor is mainly limited to food-related dimensions, following prescribed norms of either minimum calories or subsistence nutritional requirements. However, interest demands a multidimensional assessment of poverty. The present study undertakes unidimensional and multidimensional notions of poverty to build a comprehensive scenario of poverty prevailing in U.P. Starting with overall state analysis, it proceeds by segregating populations across four economic regions—Western (WR), Central (CR), Southern (SR), and Eastern (ER)—and among three major social groups (SCs, OBCs and ‘others’ representing upper castes), and two major religions (Hindus and Muslims). The unidimensional notion of poverty is measured in terms of consumption expenditure, using the unit level records of four quinquennial Consumption Expenditure Survey of NSSO (38th, 50th, 61st, and 68th) by classifying the study period into three phases; first decade (1983 to 1993-94); second decade (1993-94 to 2004-05) and the contemporary period (2004-05 to 2011-12). District-wise analysis of unidimensional poverty can only be carried out for the contemporary period with the availability of unbiased estimates. Consumption poverty is aggregated in terms of the total number of poor, levels of poverty, and differences in poverty. The levels of poverty are assessed in terms of Headcount Ratio (HCR), Poverty Gap Ratio (PGR) and Square Poverty Gap Ratio (SPGR), which target nearly poor, moderately poor or poorer, and severely poor or ultra-poor populations, respectively. The differences in poverty have been analysed by absolute and relative poverty risks. It also II examines the proximate factors underlying poverty differences at the inter-regional and intergroup levels in rural and urban areas of the state during 2004-05 and 2011-12, using survey logistic regression. The second notion, Multidimensional Poverty, comprises the construction of the Uttar Pradesh Multidimensional Poverty Index (UP-MPI) that includes three dimensions— education, health, and standard of living (SOL), which are represented by ten indicators such as years of schooling, child school attendance, undernutrition, child mortality, electricity, safe drinking water sources, improved sanitation, safe cooking fuel, housing structure and assets. A household is considered deprived when no household member has completed at least six years of schooling or there exists a child aged 7 to 14 who is not attending the school. A household is also considered deprived if there exists an underweight woman (15–49 of age) or a stunted child or a child death within the last five years of the survey. Furthermore, a household is deprived if it has no access to electricity; clean/safe drinking water sources (or if a source of clean/safe drinking water is located at 30 minutes or more walk from home, roundtrip); improved sanitation(or if improved but shared); safe sources of cooking; or if the structure of the house is kachha type; or if household do not own at least one asset related to access to information (radio, TV, telephone) and one asset related to mobility (bike, motorbike, car, truck, animal cart, tractor) or at least one asset related to livelihood (refrigerator, arable land, livestock). These three dimensions are equally weighted (33.33% each), and that is distributed equally across indicators. Finally, any household whose total deprivation count is higher than or equal to poverty cut (k=33.3%) is considered an MD poor. After identification, the aggregation of MD poverty is estimated by way of Headcount (H) and Intensity (A) components of UP-MPI. The former defines the incidence (or proportion) of people that are MD poor and the latter refers to the intensity of poverty which is the average deprivation of the MD poor people. The UP-MPI in the form of an index is computed as a product of H and A. The value of the UP-MPI index represents the share of the population that is MD poor adjusted by the intensity of the deprivation suffered. Next is the decomposition of UP-MPI in the sense of contributions to overall poverty, first by dimensions and indicators, and then by population subgroups (including social groups, religious groups and four classified regions of rural and urban U.P). The estimation of UP-MPI spanning a period of more than two decades (1992-93 to 2015-16) based on four rounds of National Family Health Survey (NFHS), conducted III respectively during 1992-93 (NFHS-1), 1998-99 (NFHS-2), 2005-06 (NFHS-3), and the latest during 2015-16 (NFHS-4). MD poverty is also estimated for the same classification of subgroups that was done for consumption poverty so that both types of poverty can be compared meaningfully. However, the disaggregated and regional profile of MD poverty can only be analysed for the latest NFHS-4. After refining the sample, it is estimated that in all four NFHS, not less than 85 per cent of the original sample is being utilised in any case, except for WR (NFHS-4). Moreover, to balance the non-eligible and missing observations, an adjustment procedure for the re-sampling of weights suggested by Alkire and Santos (2015) and Kovacevic and Calderon (2014) has been followed. The precision of UP-MPI estimates is tested on grounds of mismatches in the identification of MD poor when equated with other notions of poverty (wealth and consumption), followed by correlation analysis and conditional probabilities associated with them. The robustness of UP-MPI estimates is determined by a sensitivity analysis of the changes in deprivation weights and poverty cutoffs (k=33.3%). The study foregrounds the grim poverty scenario prevailing across rural CR and urban ER of the state. These two regions have emerged out as critically poor on various grounds. For both consumption and MD poverty, contemporary changes in the levels of poverty, particularly in favour of SCs (rural), OBCs (urban) and Muslims (rural and urban) bring out shrinking inter-group differences in poverty. Besides this decline, the levels of poverty are still high among them. The majority of Muslims and SCs in the state have remained impoverished over a long period. Poor SCs and Muslims are relatively more intensely deprived across dimensions and indicators, which signal the historical roots of poverty or the chronic state of poverty among them. Regression estimates find that SCs and Muslims are poor largely on similar grounds such as illiteracy, casualisation of the workforce and the sudden increase in poverty in CR in general and low engagement in self-employment agricultural occupation, large household size, marginal land holdings and backwardness of rural ER in particular to rural households. The two most unfortunate facts observed among SCs are that even the Semi-medium landholders (more than two but less than or equal to four hectares) are poor, and for any given category of occupation, they experienced the highest poverty in both rural and urban areas during 2011-12. Traditionally, the poor in the state are mostly deprived among SOL indicators in general. Recently, in 2015-16, health deprivation played a significant role for almost all the IV population subgroups under consideration, due to the presence of at least a stunted child or an undernourished woman. Exceptions are the poor Muslims who have been relatively more deprived in both the indicators of education in both rural and urban areas of the state over the last two decades. In addition to undernutrition, poor people are most often deprived in sanitation, cooking fuel and electricity. In general, deprivation in water, child mortality, housing and assets is relatively low in the state.