THE URBANIZATION OF POVERTY IN INDIA: SPATIO- TEMPORAL DISPARITIES IN CONSUMPTION EXPENDITURES. KOMALI YENNETI, YEHUA DENNIS WEI, and WEN CHEN

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1 THE URBANIZATION OF POVERTY IN INDIA: SPATIO- TEMPORAL DISPARITIES IN CONSUMPTION EXPENDITURES KOMALI YENNETI, YEHUA DENNIS WEI, and WEN CHEN ABSTRACT. This paper identifies the spatial patterns of urban poverty and their change over time using state-level NSS data on consumer expenditures for the 61st ( ), 66th ( ), and 68th ( ) rounds and a class of decomposable poverty measures (Foster-Greer-Thorbecke Index). Further, state-specific new urban poverty lines (Tendulkar Methodology) based on consumption expenditures for both food and nonfood items have been employed to measure the incidence of urban poverty in each of the twenty-nine states. The analysis reveals that while urban poverty has declined considerably over the study periods both at national and state levels, there are distinct spatial concentrations, reflecting the phenomenon of urbanization of poverty and large spatial differences in consumption expenditures. There is clearly a heavy concentration of urban poverty in central and north-eastern states of India, such as Bihar, Chhattisgarh, Madhya Pradesh, Uttar Pradesh, Orissa, Assam, Arunachal Pradesh, and Manipur. Only the relatively prosperous north and north-western states have low urban-poverty levels in all periods. In the regression results, access to housing and sanitation emerge as the most important variables in explaining urban poverty in India over all study periods. Keywords: poverty, inequality, India. Over the past few decades, India s modernization process has led to rapid expansion of urban centers 1 and remarkable rise in urban population. What has been of particular concern is that rapid urbanization has been accompanied by a steady growth in the number of urban poor and the urbanization of poverty (Piel 1997). The numbers of urban poor have risen by 34.4 percent, from 60 million persons in to 80.8 million persons in (GoI and MoHUPA, 2009). In contrast, the numbers of rural poor have declined by 15.5 percent over the same period. Urban poverty has been a problem affecting the Indian society for the last few decades and is a serious challenge to the intellectuals, politicians, and planners (Hashim, 2009; Nath, 1994). Since the Sixth Five Year Plan, a sizeable reduction in the magnitude of urban poverty has been one of the major objectives of planning in India. The persistence of poverty in India has drawn more serious attention from scholars and policymakers alike in the past several decades, leading to a significant literature on the topic. However, most of the studies have been carried We would like to acknowledge the funding of the National Natural Science Foundation of China ( , , and ). k K. YENNETI, Post-doctoral Researcher, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, , China; [yenneti@niglas.ac.cn]. Y. DENNIS WEI, Professor and Corresponding Author, Department of Land Management, Zhejiang University, Hangzhou, , China, and Department of Geography, University of Utah, Salt Lake City, UT , USA; [wei@geog.utah.edu]. W. CHEN, Professor, Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, , China; [wchen@niglas.ac.cn]. Geographical Review 107 (2): , April 2017 Copyright 2016 by the American Geographical Society of New York

2 URBANIZATION OF POVERTY IN INDIA 361 out by development economists concerned with the analysis of trends in rural poverty over time and the widening gap between urban-rural poverty levels (Jha 2000; Deaton and Dreze 2002; Sen and Himanshu 2004). A few others have examined certain problematical aspects of urban poverty, such as employment, the relationship between urbanization and poverty, and poverty of slum dwellers (De Souza 1978; Kundu 2000). The methodology for measurement of poverty in India what poverty is, what the parameters of it are, and what distinguishes the poor from the nonpoor has also been the focus of much debate (Dandekar and Rath 1971; Ahluwalia 1978). Although few studies on the regional analysis of rural and food poverty (Dayal 1989, 1993) provide some illuminating insights to regional concentration of poverty in India, little literature is available focusing on the analysis of urban poverty in India. So far as studies on Indian poverty are concerned, the major focus of both the economic development and geographical literature has been on examining rural poverty, while paying little attention to the spatio-temporal variations of urban poverty, which are conspicuous in a large, rapidly urbanizing country like India. By overemphasizing food and nutrition, even the existing studies appear to have ignored several vital factors related to poverty, such as education, clothing, water, and sanitation. The objective of this study is to identify and interpret the spatio-temporal variations of urban poverty in India (considering both food and nonfood items) and its patterns of change over time. The spatial scale of this study includes the twenty-nine major states of India and the temporal period consists of the years , , and The study moves forward the literature on poverty in India, and urban poverty in the developing world, by identifying the spatio-temporal variations that underlie urban poverty in a rapidly urbanizing economy. The results of this research serve academic and scientific purposes, as well as provide insights for policymakers, local and national governments, and development institutions involved in designing and implementing policies to alleviate urban poverty and provide for sustainable urban development at national and subnational levels in India. MEASUREMENT OF POVERTY In the context of development, the minimal concept of poverty is the lack of income to satisfy minimum needs for physiological survival (Sen 1979, 1981). People with income below the level required to satisfy minimum needs often defined in terms of food consumption, especially caloric or nutritional requirements of a subsistence level are considered below the poverty line. Though the measure of absolute poverty is the typical procedure for estimating the poverty line, there are several problems with this approach. First, it is difficult to assess one s income accurately. Further, even with the attainment of a minimum income, if it is not spent, one will be living below the poverty line, as his minimum needs remain unfulfilled.

3 362 GEOGRAPHICAL REVIEW The problems in defining poverty in terms of income have made consumer expenditure the proxy for income. According to the consumer-expenditure approach, poverty is measured by the proportion of expenditure spent on certain essential items of consumption, such as food (Ojha 1970). Here, the minimum expenditure necessary for meeting minimal needs, especially food, is ascertained, and those people or households without an income to meet these minimal needs are considered below the poverty line. The traditional approaches to poverty characterized in terms of a shortfall in a monetary indicator (income or expenditure) to satisfy minimum needs for physiological survival have been highly influential in the past several decades. At the same time, a body of international literature, including the nonfood dimensions of minimum needs, such as drinking water, clothing, shelter, education, and health care, besides food, has also developed (Dercon and Krishnan 1996; Moser 1998; Ray and Lancaster 2005). This wider conception of poverty appears to be particularly well suited for international organizations, such as the World Bank and the United Nations Development Programme, which recognize low levels of attaining basic amenities and physical assets, along with food and nutrition, among the characteristics of poverty (World Bank 2001; UNDP 2009). It is rather surprising, however, that while studies on measuring poverty in India referencing consumer expenditure on food consumption and nutritive values exist in both development (Dandekar 1981; Sundaram and Tendulkar 2003) and geography (Dayal 1989, 1993) literatures, there is little work using both food and other vital nonfood items, such as clothing, education, and health expenses. We argue that to study poverty in India in its totality, one must take into account the whole gamut of consumption items and establish a minimum family budget in real terms, looking at the cost of living. This argument is specifically relevant for analyzing poverty in urban areas, where it should reflect the income needed not only to purchase sufficient food, but also to obtain a secure shelter with adequate water and sanitation, to pay for transport, to keep children at school, and to afford health care and medicine when needed. The nonfood monetary costs of avoiding poverty are generally higher in urban areas than in rural areas, as access to housing, resources, and services are monetized, and more expensive in larger or more prosperous cities in India. This study tries to fill the existing void in the literature by endeavoring to analyze urban poverty in India using both food and nonfood consumption-expenditure requirements. FACTORS AFFECTING POVERTY There have been a sheer quantum of international studies undertaken to explain the different factors affecting poverty, including income and wealth (Shaw 2010), basic needs (Wodon 1997), health and literacy (Ghosh 2010), and employment (Razavi 1999). In his seminal work on Poverty and Famines: An

4 URBANIZATION OF POVERTY IN INDIA 363 Essay on Entitlement and Deprivation, Amartya Sen provides two insights: people are prone to poverty when they lose their primary entitlements the wages earned from an occupation, crop or livestock raised, gifts, donations, investments, and inheritance to access commodities; and poverty occurs in a country where democracy, media, and social justice are subdued due to conditions that in which government allow poverty to worsen (1981). While Sen s exchange-entitlement theory provides some influential ideas for explaining poverty, it fails to recognize the environmental, political, and social determinants such as poor weather conditions and natural disasters, conflict and war, government economic policies and practices, interclass (or caste) marginalization that mark the onset of poverty. A number of studies concerned with the spatio-temporal variations of poverty have also tried to identify the factors affecting poverty in India (Bardhan 1985; Dayal,1989, 1993; Sundaram 2001). This ever-growing literature has been significantly contributing to the advancement of Sen s observations and poverty scholarship in general, by identifying a wide range of determinants to poverty, including inadequate nutrition, landownership, agricultural productivity, unemployment, labor-force participation, household size, and caste and gender discrimination. However, most studies are limited to rural areas and agricultural laborers, leaving out altogether an explanation for urban poverty. As well, existing studies are limited in their appreciation of influential factors such as education, health, shelter, and public services. Further, in a socioeconomically dynamic country like India, the determinants of poverty are likely to vary over time and across space, similar to poverty and inequality in many other countries (Petrakos and others 2005; Li and Wei 2010). This points to the need for identifying all the possible determinants of poverty at a more disaggregated level. Analyses should be extended to cover rural and urban areas separately, as there are significant differences between them in characteristics of the poor and the determinants of poverty. No systematic attempt has been made to examine the influence of these multiple factors on urban poverty in India, a gap this study makes an attempt to fill. THE URBAN POVERTY LINE IN INDIA Poverty has conventionally been estimated with reference to a poverty line, the cutoff between poor and nonpoor. While rural poverty in India has been a part of the policy debate since the pre-independence period, urban poverty was not recognized as a concern until the early 1960s. Owing to this, while the earliest estimates on rural poverty line can be traced to 1938 by the National Planning Committee, the first urban poverty line was formulated by the Planning Commission in Since that first official estimation, a series of urban poverty lines have been constructed by different committees constituted under the Planning Commission of India. Rounds of National Sample Survey (NSS) data on consumer

5 364 GEOGRAPHICAL REVIEW expenditure have been used in these estimates. Dandekar and Rath (1971) made the first systematic assessment of urban poverty line at national level, based on National Sample Survey (NSS) consumer expenditure data from They argued that the poverty line must be derived from what is necessary to provide 2,250 calories per day in both rural and urban areas. This led to intense debate on minimum-caloric consumption norms and a new poverty line proposed by the Alagh Expert Group Committee, defined as a monthly per capita consumption expenditure (MPCE) level of Rs for rural areas and Rs for urban areas at prices (28th round of NSS data) at national level. These figures correspond to the monetary value of goods and services that would cover per capita daily caloric requirement of 2,400 in rural areas and 2,100 in urban areas, along with other nonfood items such as clothing, footwear, education, health, and transport (GoI 1979). Another group, chaired by Gaurav Lakdawala, supported the poverty line estimation approach as a caloric norm and a fixed-consumption basket. For the first time, state-specific rural and urban poverty lines are recommended (GoI 1993). The most recent methodology for establishing an estimation of poverty levels in India was proposed by the Tendulkar Expert Group Committee in 2009 (GoI 2009). Their urban poverty lines are employed in this study for several reasons (Table 1). First, they consciously moved away from caloric norms since caloric intake could not be successfully correlated to nutritional income; second, it provided a uniform poverty-line basket (of food and nonfood components) to both rural and urban populations; third, it incorporated an explicit provision for expenditure on health and education; fourth, it provided state-level rural and urban poverty lines in 2000s; and most importantly, a number of previous studies have employed this methodology. The urban poverty lines in this new methodology were computed based on the MPCE of a gamut of consumption items: cereal, pulses, milk, edible oil, meat, vegetables, fresh fruits, dry fruits, sugar, salt and spices, tobacco and intoxicants, fuel, clothing and bedding, footwear, education and medical expenses, and other foods, goods, and services. One limitation of this methodology is the lack of a specific urban center poverty line. In India, the prices of commodities and services may vary significantly across different size classes of urban centers, even within one state. However, as no official estimates in India provide either a unit-level urban poverty line or even unit-level data on urban MPCE, we have used state-specific urban poverty lines. DATA AND ANALYSIS The most commonly used tools to measure poverty are Headcount Ratio, Poverty Gap Index, Squared Poverty Gap Index, Sen Index, Foster-Greer-Thorbecke Poverty Index, Human Poverty Index, and Capability Poverty Index. Normally, poverty in India is measured by the Headcount Ratio. This is

6 URBANIZATION OF POVERTY IN INDIA 365 TABLE 1 STATE-LEVEL URBAN POVERTY LINES IN INDIA BASED ON MPCE (TENDULKAR COMMITTEE METHODOLOGY) S. NO STATES URBAN POVERTY LINE (RS.) Andhra Pradesh Arunachal Pradesh Assam Bihar Chhattisgarh Delhi Goa Gujarat Haryana Himachal Pradesh Jammu & Kashmir Jharkhand Karnataka Kerala Madhya Pradesh Maharashtra Manipur Meghalaya Mizoram Nagaland Orissa Punjab Rajasthan Sikkim Tamil Nadu Tripura Uttar Pradesh Uttaranchal West Bengal All India accomplished by counting the number of poor, defined in some specific way, and then expressing poverty as the ratio of the number of the poor to the total number of people in the society. This method has been used, explicitly or by implicitly, by the Planning Commission in India ever since the quantitative study and measurement of poverty began. In this study, urban poverty is measured by the Foster-Greer-Thorbecke (FGT) Poverty Index, while using MPCE as a measure of welfare. The Headcount Ratio (PHR), the Poverty Gap Ratio (PGR), and the Squared Poverty Gap Ratio (SPGR) belong to a family of decomposable poverty measures known as the Foster-Greer-Thorbecke Index. A poverty measure is said to be decomposable if the poverty measure of a

7 366 GEOGRAPHICAL REVIEW group is a weighted average of the poverty measures of the individuals in a group (Makoka and Kaplan 2005, 20). A generalized version of poverty indices considered by Foster and others (1984) is as follows: FGT ¼ P a ðx; x Þ¼ Xp 1¼1 ¼ PHR when a ¼ 0 ¼ PGR when a ¼ 1 ¼ SPGR when a ¼ 2 1 x a i x ð1þ where x*= urban poverty line; x i = monthly per capita consumption expenditure of i th individual; and P = number of persons with consumption expenditure less than x*. The parameter a can be viewed as a measure of poverty aversion: a larger a gives greater emphasis to the poorest poor. As a becomes very large, P a approaches a welfare measure, which considers only the position of the poorest household. The urban MPCE data used in this analysis comes from the 61st, 66th, and 68th rounds of the nationwide household consumer expenditure survey (CES) conducted by the National Sample Survey Organization (NSSO). The MPCE considers food items (beverages, cereals and substitutes, pulses, edible oil, milk and milk products, tobacco), and nonfood items (clothing and bedding, conveyance, durable goods, education, entertainment, footwear, rent, medical expenses, other goods and services, and household consumables). The NSSO collects CES data from different commodity groups for rural and urban sectors of the country, for states and Union Territories (UTs), and for different socioeconomic groups, by using the household-interview method from a randomly selected sample of households. The NSSO conducts MPCE surveys using three methods: Uniform Reference Period, Mixed Reference Period, and Modified Mixed Reference Period. 2 The Uniform Reference Period (URP) refers to the MPCE data collected using a reference period of last thirty days. The Mixed Reference Period (MRP) refers to the MPCE data collected using a reference period of last 365 days for five nonfood items, such as clothing and bedding, footwear, durable goods, education, and institutional medical expenses, and a reference period of last thirty days for the remaining items, including all food items. Modified Mixed Reference Period (MMRP) refers to the MPCE data collected using a reference period of last seven days for edible oil, egg, fish and meat, vegetables, fruits, spices, beverages, refreshments, processed food, pan, tobacco and intoxicants, and for all other items, while the reference periods used are the same as in case of MRP. Following the Tendulkar Expert Group s suggestion, MRP-based poverty estimation is considered over URP and MMRP, as MRP-based estimates cap-

8 URBANIZATION OF POVERTY IN INDIA 367 ture the household consumption expenditure by poor households for low-frequency items of purchase more satisfactorily than URP and MMRP. Furthermore, while the URP or MRP based consumption data is available for all the periods (that is, 61st, 66th, and 68th NSS Round survey on consumer expenditure), although MMRP-based consumption data is available only for 66th and 68th NSS Rounds. Therefore, we have used MRP data. CHANGES IN URBAN POVERTY OVER TIME Table 2 presents the estimates of poverty-headcount ratio (PHR), poverty-gap ratio (PGR, which reflects the depth of poverty), and the squared poverty-gap ratio (SPGR, which reflects severity of poverty) for urban populations at both national and state level over three periods of time: , , and Of the three measures, PGR and SPGR are sensitive to the distribution of the population below the poverty line. We consider first the changes of PHR, PGR, and SPGR for urban population at national level. The incidence of poverty in India, measured by the PHR for urban population, declined from 25.8 percent in to 20.9 percent and percent in and , respectively. The results in terms of distribution-sensitive measures were in line with the changes in PHR. The declining trend (direction and magnitude) of all-india level urban poverty in this study can be closely related to the declining levels of urban poverty between and as observed by Kundu and Sarangi (2005). However, the figures in this study diverge from Kundu and Sarangi due to our different approach to measurement of urban poverty, different levels of urbanization in the country, and different time period employed in this study. The spatial changes in urban poverty were strikingly different from the national trend. In terms of PHR, while urban poverty declined in India as a whole between and (down 5 percent), in several states it increased. Of the twenty-nine states included in this study, PHR declined in twenty and increased in nine (Table 2). Likewise, the urban poverty, measured by PGR and SPGR, declined in twenty-one states and increased in eight, with exception to Haryana, which recorded an increase in PHR. A striking point revealed in the analysis for and is the drastically increased levels of urban poverty in several states of India, despite the increased urban consumption expenditures levels in all states. For example, in Delhi, Jammu and Kashmir, and Jharkhand, where the per capita consumption expenditure levels increased, the percentage of urban poor also increased. The northeast states Arunachal Pradesh, Assam, Manipur, Mizoram, and Nagaland joined the other states in recording a rise in PHR. Significant improvements in trends of state-level urban poverty were observed over the and periods. In terms of PHR, urban poverty had shown a distinct decline in all but only one of the twenty-nine states. Chhattisgarh, which recorded a significant increase in PHR (and, there-

9 368 GEOGRAPHICAL REVIEW TABLE 2 PHR, PGR, AND SPGR ON MIXED REFERENCE PERIODS (MRP): , , AND , URBAN POPULATION STATES POVERTY HEADCOUNT RATIO (PHR) (PERCENTAGE) POVERTY GAP RATIO (PGR) (PERCENTAGE) SQUARED POVERTY GAP RATIO (SPGR) (PERCENTAGE) Andhra Pradesh Arunachal Pradesh Assam Bihar Chhattisgarh Delhi Goa Gujarat Haryana Himachal Pradesh Jammu & Kashmir Jharkhand Karnataka Kerala Madhya Pradesh Maharashtra Manipur Meghalaya Mizoram Nagaland Orissa Punjab Rajasthan Sikkim Tamil Nadu Tripura Uttar Pradesh Uttarakhand West Bengal All India fore, also in the number of urban poor) between and (from 28 percent to 23 percent), had shown a decline in PGR and SPGR. A marginal increase in urban PHR in Chhattisgarh can be associated with a decline in the incidence of rural PHR (GoI 2014).

10 URBANIZATION OF POVERTY IN INDIA 369 Yet another important point observed in this study is the incidence of severe urban poverty in many Indian states compared to rural areas. Several states recorded urban poverty levels higher than that of India as a whole in all the study periods. In , eight states had shown PHR higher than that of national average. The number increased to fourteen in and then declined to twelve in The results in terms of the PGR were in line with the SPGR, but with exception in the year In , while the number of states with PGR above the national average were eleven, in terms of SPGR the number stood at nine. The results of the PGR and SPGR, taken together, however, were in contrast with the figures of PHR. The most important finding of this analysis is that during all periods, the figures of the three urban poverty measures were much higher than the national average in Bihar, Chhattisgarh, Madhya Pradesh, Orissa, and Uttar Pradesh. During the same periods, the urban consumption expenditure levels in these states were also found to be much lower than the levels in India as a whole. Similarly, Jharkhand and Manipur also recorded higher the nationallevel PHR, PGR, and SPGR in and SPATIAL DISTRIBUTION OF URBAN POVERTY The average urban consumption expenditure levels in India increased from Rs monthly per capita consumption expenditure in to Rs and Rs in and , respectively. In all three periods, about half of the states recorded average consumption expenditure levels below national levels. A distinct spatial contrast in the distribution of consumption expenditure levels was also witnessed in the three study periods. In , there was a heavy concentration of the lowest standard of living in the coastal and central states of India, with the exception of Tripura (a northeastern state) and Rajasthan (a northern state), where the urban population s consumption expenditure was lower than Rs (Table 1). This spatial pattern appeared to exist in other periods as well, with the addition of Jammu and Kashmir, a northern state. We now turn to a consideration of spatial contrasts in the distribution of urban poverty, measured in terms of PHR, PGR, and SPGR in the twenty-nine states over the study periods. Figures 1, 2, and 3 present urban poverty (for a=0, 1, 2) vary across states, with the darker color indicating higher poverty index and thus higher rates of urban poverty. With identical patterns of consumption expenditure, the PHR in was highest in the central and eastern states of India, such as Bihar, Chhattisgarh, Madhya Pradesh, Uttar Pradesh, and Orissa. The southern states, Andhra Pradesh and Tamil Nadu, are exceptions, witnessing a below national average incidence of PHR, despite of low consumption expenditure levels. Another worthy observation for the same period was the high concentration of PHR in the northeastern states, notwithstanding the high consumption expenditure

11 370 GEOGRAPHICAL REVIEW FIG. 1 Urban Poverty Levels, measured in terms of PHR or decomposition of the FGT index (a=0), in India (in percentage) FIG. 2 Urban Poverty Levels, measured in terms of PGR or decomposition of the FGT index (a=1), in India (in percentage)

12 URBANIZATION OF POVERTY IN INDIA 371 FIG. 3 Urban Poverty Levels, measured in terms of decomposition of the FGT* index (a=2), in India (in percentage) levels. Only the relatively prosperous north and northwestern states had low concentration of PHR in all periods. Similar spatial patterns were also observed in and , with exception to some of the northeastern states, such as Tripura, Sikkim, and Mizoram. The results in terms of the distributive-sensitive measures (PGR and SPGR) were in line with the spatial distribution of PHR, with two exceptions. The coastal state of West Bengal, which recorded a high concentration of PHR in all three periods (and, therefore, also in the number of poor), had shown a low PGR and SPGR. Similarly, the high concentration of PHR (for and ) in Nagaland, a northeastern state, was in contrast to the low concentration of PGR and SPGR. DETERMINANTS OF URBAN POVERTY In the present study, a regression model based on the changing trends of a total of eleven variables was employed to identify the variables associated with the changes in urban poverty. Table 3 summarizes the definition, description, and data sources of all the variables used in this study. Previous studies helped identify the variable selection (Dayal 1993; Mahajan and Kumar 2007; Dinesha and Jayasheela 2008; Sharma 2010). These studies, though largely focused on rural poverty, unambiguously emphasized the different factors affecting poverty, including annual growth rate, education and training, employment opportunities, and access to public services all relevant for urban poverty as well.

13 372 GEOGRAPHICAL REVIEW TABLE 3 DATA-BASED INDICATORS, GROUPED INTO COMPONENTS, THEIR DEFINITIONS AND MEASUREMENT S. NO. VARIABLE NAME DETAILS OF INDICATORS DATA SOURCE(S) i Urban poverty index PHR (percentage) Calculated from state level ii PGR (percentage) data of NSS , iii SPGR (percentage) , and on consumer expenditure, NSSO, GoI. iv Slum population Percentage of people in slums in urban areas State level data of IHDS , Unit level data of NSS and on Urban Slums, NSSO, GoI. v Annual urban population growth rate Percentage vi Inequality Inequality in urban consumption expenditure (percentage) vii Access to water Percentage of population with no access to safe drinking water to total population viii Access to sanitation Percentage of population with no access to toilets to total population ix Access to housing Percentage of population in weak housing structures (Kutcha and Semi-pucca) to total population x Unemployment Percentage of population not working but seeking work xi Literacy Percentage of population with education atleast upto primary school to total population State level data from various NSS rounds Calculated from state level data of NSS , , and on Consumer expenditure, NSSO, GoI. Extracted from Planning Commission Data book for , state level data of NSS and on Housing condition and amenities, NSSO, GoI. State level data of NSS , , and on employment and unemployment, NSSO, GoI. State level data of NSS on educational and vocational training and NSS , and on employment and unemployment, NSSO, GoI. The availability of public data also played a significant role in this research. In our study, the number of variables is sufficient to conduct principal component analysis; the variable set is also well constructed to reflect the multiple determinants of urban poverty over a period of time. Despite our efforts, bias

14 URBANIZATION OF POVERTY IN INDIA 373 may still exist due to data limitations regarding a lack of urban center specific poverty line (section 4), which can be reduced by the availability of better data in future studies. To understand the relationship between the eight independent variables and urban poverty measures (PHR, PGR, and SPGR), a simple correlation coefficient analysis was carried out separately for all study periods (Table 4). The statistically significant and higher positive correlation between the distributivesensitive measures and PHR, in all three study periods, support the hypothesized findings of Sundaram and Tendulkar (2003). The sign and magnitude of positive correlation between urban poverty measures (PHR, PGR, and SPGR) no access to sanitation and the population living in poor to slum housing conditions in all study periods confirm that unfit human habitations, due to dilapidation or lack of sanitation facilities, can generally explain the various urban poverty and slum population of India (Nath 1994; Chaplin 1999). As housing and sanitation make important considerations in explaining urban poverty in the regression model, they will be discussed in more detail later. A higher positive correlation was recorded between inequality and distributive-sensitive urban poverty measures in all study periods, and between inequality and PHR in This supports earlier studies that viewed poverty as a reflection of inequality, implying how the concentration of a nation s wealth in a few can throw large numbers into poverty and how redistribution of income, expenditure, or other resources can be an incentive for the reduction of poverty (Jha 2000; Sen and Himanshu 2004). On the other hand, higher level of negative correlations were observed between literacy rate and the other variables urban poverty measures, slum population, inequality, access to sanitation in all study periods, with exception to , when the correlation between literacy and urban poverty measures was statistically significant. Significant positive correlation was recorded between literacy rate and urban population growth in all three periods, including literacy and unemployment, and literacy and access to water in the and periods. An important observation here is that correlation coefficients between unemployment and urban poverty were either negative or weak in all three periods, while positive correlation was observed between unemployment and urban population growth in and These observations contradict the traditional arguments that indicate the incidence of unemployment in urban areas to migration of people from rural areas that is, the employmentcreating capacity of the urban centers is often too inadequate to absorb an ever-growing labor force and thereby the widespread unemployment to urban poverty (Kundu 2000; Kundu and Sarangi 2005). The value of correlations between the independent variables does not show presence of multicollinearity. We included four of eight variables, inequality, access to water, access to sanitation, and access to safe housing, which recorded statistically significant and high correlation coefficients, in the multivariate regression model (Table 5).

15 374 GEOGRAPHICAL REVIEW TABLE 4 SIMPLE CORRELATION MATRIX (a=0, 1, 2) [N=29] VARIABLES PHR (I) PGR (II) SPGR (III) SLUM POPULATION (IV) ANNUAL URBAN POPULATION GROWTH (V) INEQUALITY (VI) ACCESS TO WATER (VII) ACCESS TO SANITATION (VIII) ACCESS TO SAFE HOUSING (IX) UNEMPLOYMENT (X) LITERACY (XI) i 1 ii 0.92* 1 iii 0.84* 0.98* 1 iv v vi vii viii ix x xi 0.50* 0.57* 0.57* i 1 ii 0.95* 1 iii 0.88* 0.98* 1 iv v vi vii viii * 0.57* ix * x (continued)

16 URBANIZATION OF POVERTY IN INDIA 375 TABLE 4 CONTINUED VARIABLES PHR (I) PGR (II) SPGR (III) SLUM POPULATION (IV) ANNUAL URBAN POPULATION GROWTH (V) INEQUALITY (VI) ACCESS TO WATER (VII) ACCESS TO SANITATION (VIII) ACCESS TO SAFE HOUSING (IX) UNEMPLOYMENT (X) LITERACY (XI) xi * i 1 ii 0.98* 1 iii 0.92* 0.98* 1 iv v vi vii viii * 0.48* ix * x xi * (*), ( ), and ( ) indicate statistical significance at 1%, 5%, and 10% level, respectively.

17 376 GEOGRAPHICAL REVIEW TABLE 5 REGRESSION COEFFICIENT OF SIGNIFICANT VARIABLES REGRESSED AGAINST URBAN POVERTY (a=0, 1, 2) [N=29] VARIABLES PHR PGR SPGR Inequality 0.65 (2.03) 0.07 (0.25) 0.03 (0.13) 0.04 (0.67) 0.04 (0.67) 0.03 (0.55) 0.11* (4.08) 0.02 (1.09) 0.01 (0.84) Access to 0.11 (1.32) 0.07 (0.71) 0.17 (1.13) 0.00 (0.13) 0.00 (0.13) 0.03 (0.97) 0.01 (1.72) 0.00 ( 0.05) 0.01 (0.86) water Access to 0.08 (0.71) 0.38 (2.50) 0.37 (2.27) 0.11* (2.95) 0.11 (2.95) 0.09 (2.50) 0.01 (1.38) 0.04* (3.25) 0.03 (2.58) sanitation Access to safe 0.89 (2.39) 0.70 (1.80) 1.24 (1.73) 0.20 (2.17) 0.20 (2.17) 0.35 (2.33) 0.08 (2.58) 0.08 (2.58) 0.15* (3.14) housing For all variables R² constant 5.17 ( 0.48) 9.69 (0.97) 5.65 (0.72) 0.80 (0.34) 0.80 (0.34) 0.03 ( 0.02) 2.78 ( 3.14) 0.24 ( 0.30) 0.27 ( 0.54) F Note: Figures in parentheses represent t-values. (*), ( ), and ( ) indicate statistical significance at 1%, 5%, and 10% level, respectively.

18 URBANIZATION OF POVERTY IN INDIA 377 In all study periods, population living in weak housing conditions (kutcha, which means a house made up of wood, mud, straw and dry leaves and semi-pucca, which means a house whose walls is made up of brick, cement and mortar but roof is made up of wood, mud, straw, and dry leaves.) and inadequate access to sanitation emerged as the most important variables in explaining urban poverty in India, accounting for a large portion of variance. Adding the variable of access to clean water further improves the explanatory power of the regression model. Access to clean water is positively related to all urban poverty measures, and the relationship is statistically significant at 0.05 and 0.1 levels in and The importance of access to clean water, as an explanatory variable for urban poverty, highly correlates to access to sanitation. These regression results, therefore, imply that lack of access to basic services, such as shelter, water, and sanitation, at an affordable cost could increase the monetized burden on the urban poor. States with lower access to these basic services also had a higher incidence of urban poverty. For example, high concentrations of urban poverty were recorded over central and eastern states, such as Bihar, Chhattisgarh, Jharkhand, and Orissa, where a large percentage of the total urban population exists without adequate access to water and sanitation facilities. Massive problems have emerged in these states due to the rapid growth of urban population without a corresponding increase in urban infrastructure, such as safe drinking water, sanitation facility, and housing. The regression results on factors affecting urban poverty also support our hypothesis that, in urban areas of India, the expenditures on nonfood costs of avoiding poverty securing a safe and secure shelter with adequate sanitation are usually higher than in rural areas. It is well recognized that as housing prices becomes more expensive in big cities and affordable housing becomes inadequate, a large percentage of migrant population and urban poor end up living in slums or squatter settlements where vacant state-owned or private land is occupied illegally. According to the Tenth Five Year Plan, the nation needed twenty-two million additional houses. City slums are visible pockets of poverty, with miserable access to basic services, such as water and sanitation. Housing, combined with sanitation and water, emerged as important determinants affecting upward mobility, in terms of consumption expenditures, in urban areas. The problems of housing and access to public services will worsen, unless concerted measures are taken. The living conditions of the vulnerable sections of the urban society slum dwellers/urban poor will lead to the serious crippling of productive capacity for a growing number of people. Any significant decline in urban poverty in India over the past years can be attributed to the several national schemes Rajiv Awas Yojana, Urban Basic Services for the Poor, Improvement Of Civic Amenities through Improvement in Water Supply and Sanitation,initiated by the central government, especially since the Seventh Five-Year Plan, for providing housing and basic services to the urban

19 378 GEOGRAPHICAL REVIEW poor. Likewise, the relative urban prosperity in north, northwest, and some of the southern states, such as Gujarat (Ummeed, which means hope), Delhi, Andhra Pradesh (Mission for Elimination of Poverty in Municipal Areas, MEMPA), and Karnataka, are all examples of the successful implementation of urban poverty alleviation schemes. There is growing empirical evidence to suggest that affordable and safe housing, water, and sanitation provide incentives for the urban poor to improve their quality of life and wellbeing (Nath 1994; Chaplin 1999). Once the influence of access to housing, water, and sanitation on urban poverty is taken into account, the next conspicuous variable is inequality. The regression coefficient for inequality was positive for all periods and statistically significant in , indicating that inequality in urban areas increases the incidence of urban poverty. Several studies confirm that reducing inequality is crucial for ameliorating living conditions of the poor (Jha 2000; Sen and Himanshu 2004). Other studies suggest that poverty and inequality are different actors (Dayal 1993). We considered inequality because it was positively correlated with urban poverty. Significant inequality or large disparities in consumption expenditures between urban populations bring in its trail imperfections in the labor market as well as social structure, which erect a barrier between the urban poor and the more affluent urban dwellers, constraining upward economic mobility of the urban poor. Inequality is an important determinant of urban poverty, though its influence is very complex and may vary in direction and magnitude over time and space. Inequality in income or consumption can also lead to inequality in access to basic services, including safe drinking water and sanitation. These four variables account for more than 40 percent of the variation in urban poverty in almost all study periods (Table 5). The model leaves more than 50 percent of the variance unexplained, which may partly be due to aggregation of data for states altogether too large for an accurate spatial analysis. CONCLUSIONS In this paper an attempt has been made to identify regional patterns of urban poverty over three periods, using state-level NSS data on consumption expenditures for the 61st ( ), 66th ( ), and 68th ( ) rounds, and a class of decomposable poverty measures. So far as studies on Indian poverty are concerned, the major focus was on examining rural poverty, especially during the 2000s. Here, state-specific new urban poverty lines (Tendulkar Methodology) based on consumption expenditures for both food and nonfood items were employed to measure the incidence of urban poverty in each of the twenty-nine major states. Most of the previous studies were limited to the sixteen major states of India and the use of outdated caloric-norm methodologies. The employment of new urban poverty lines was an important development in

20 URBANIZATION OF POVERTY IN INDIA 379 two ways: eliminating discrepancies in the estimation of urban poverty arising from insufficient income to purchase sufficient food and secure shelter with adequate water and sanitation, and to reveal a more accurate and current picture of urban poverty in India. The analysis revealed that, at the national scale, urban poverty, measured by poverty- headcount ratio (PHR) and two distributive-sensitive indicators, namely poverty-gap ratio (PGR) and squared poverty-gap ratio (SPGR), consistently declined over the three study periods ( , , and ). The changes in urban poverty at state-level were rather different. Between and , PHR declined in twenty states and increased in nine, while distributive-sensitive measures (PGR and SPGR) declined in twenty-one states and increased in eight, with exception to Haryana, which recorded an increase in PHR. Over the next two periods ( and ), all but one of the twenty-nine states recorded decline in PHR. Chhattisgarh, which recorded an increase in PHR, had shown a decline in PGR and SPGR. In support to the findings of Sundaram and Tendulkar (2003), the results of this study revealed that the distribution-sensitive measures are proportional to the PHR and hence usually (though not always) move in the same direction as PHR, with some notable exceptions, as noted. An in-depth analysis indicated that consistent urban poverty reduction failed in large central, eastern and northeastern states, such as Bihar, Chhattisgarh, Madhya Pradesh, Uttar Pradesh, Orissa, Manipur, Assam, and Arunachal Pradesh, because of a backlog of social housing and public infrastructure. It is interesting to note that urban poverty in some of these states was higher than that of national average, leading to the phenomenon of urbanization of poverty (Piel, 1997). The waning of public services in these states created new and unbearable costs to their urban citizens. Despite the recent economic growth and reduction in poverty levels over the study periods, the central region of India is still frequently referred to as BIMARU, which means sick in Hindi. BIMARU an acronym for Bihar, Madhya Pradesh, Rajasthan and Uttar Pradesh, though also Chhattisgarh and Jharkhand 3 refers to the states that had traditionally been lagging behind in India s development. On the other hand, the northeast region India The Land of Seven Sisters comprises the hill-states of Assam, Arunachal Pradesh, Meghalaya, Nagaland, Manipur, Mizoram, and Tripura. Regardless of the richness in diversity of culture, ethnic composition, languages, and natural resources, the region, both urban and rural, remains largely underdeveloped. Due to isolationist policies, differential treatment, and internal conflicts, some the northeast states have witnessed high levels of urban poverty over the past several decades. Overall, the different urban poverty-alleviation programs implemented under the national Five Year plans, especially since the Seventh Five Year Plan, did contribute to reducing urban poverty in India by raising the income and consumption of numerous people, and opening access to many others. Liberal-

21 380 GEOGRAPHICAL REVIEW izing the economy along with the increasing investments in urban basic services and affordable housing through national programs such as the Prime Minister s Integrated Urban Poverty Eradication Programme, National Slum Development Programme, Jawaharlal Nehru National Urban Renewal Mission, and Rajiv Awas Yojana has contributed to the rise of the middle class and urban poverty reduction in India. The failure of poverty reduction at a more disaggregated level can be partly due to the adoption of a uniform strategy for the whole country, without regard to the regional differences, the nature of the environment in the incidence of poverty, and the conditions in which the poverty-eradication programs were implemented. On the bright side, when antipoverty programs did work at the regional level, they have had a great influence on the economic structure, helping people move up the economic ladder (for instance, in Gujarat, Delhi, and Andhra Pradesh). In states with high urban poverty levels, reforms were conservative and incomplete, while an approach that penetrated to all layers of society was needed. Considering the scale of the country and the widespread spatial concentrations of urban poverty, identification of regional factors might prove useful in urban poverty-eradication programs. Through capturing temporal and spatial differentiation, our study provided a rich narrative and novel perspectives to the debates on urban poverty in India. It provides new depth to the poverty-measurement literature, as well as empirical figures to help design and implement policies and programs aimed at urban poverty reduction and the promotion of sustainable urban development in India. Importantly, policymakers can use the decomposition results to formulate a workable urban poverty-reduction policy. Our research suggests, for instance, that the introduction of subsidy programs for goods that are largely consumed by poor, urban households and a progressive income tax structure should result in a significant reduction of total urban poverty in India. The Indian government needs to produce substantial city-level data on consumption expenditures to better analyze and provide for policy prescriptions at subnational and subregional levels to reduce poverty. NOTES 1 According to the Census of India, an urban area is defined as all places with a municipality, corporation cantonment board or notified towns area committee, etc., and all other places with a minimum population of 5,000, have at least 75 percent of male working population engaged in nonagricultural pursuits, and have a density of population of at least, 400 person per sq. km. (1,000 persons per sq. mile). This definition has been considered in all the Censuses (1971, 1981, 1991, 2001, and 2011). 2 The consumption of any good or service by a household or person occurs in the form of a flow over time. The time period (a day, a month, or a year) for which consumption is recorded is called the reference period. It may vary from item to item. 3 Chhattisgarh and Jharkhand were formed in 2000 by partitioning Madhya Pradesh and Bihar respectively. Hence, these two states are also referred on par with the other BIMARU states.