The urbanization of poverty in India: Spatio‐temporal ... · the most important variables in...

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 changeover time using state-level NSS data on consumer expenditures for the 61st (2004–05),66th (2009–10), and 68th (2011–12) rounds and a class of decomposable povertymeasures (Foster-Greer-Thorbecke Index). Further, state-specific new urban povertylines (Tendulkar Methodology) based on consumption expenditures for both food andnonfood items have been employed to measure the incidence of urban poverty in eachof the twenty-nine states. The analysis reveals that while urban poverty has declinedconsiderably over the study periods both at national and state levels, there are distinctspatial concentrations, reflecting the phenomenon of urbanization of poverty and largespatial differences in consumption expenditures. There is clearly a heavy concentrationof urban poverty in central and north-eastern states of India, such as Bihar, Chhattis-garh, Madhya Pradesh, Uttar Pradesh, Orissa, Assam, Arunachal Pradesh, and Manipur.Only the relatively prosperous north and north-western states have low urban-povertylevels in all periods. In the regression results, access to housing and sanitation emerge asthe 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 rapidexpansion of urban centers1 and remarkable rise in urban population. Whathas been of particular concern is that rapid urbanization has been accompaniedby a steady growth in the number of urban poor and the “urbanization of pov-erty” (Piel 1997). The numbers of urban poor have risen by 34.4 percent, from60 million persons in 1973–74 to 80.8 million persons in 2004–05 (GoI andMoHUPA, 2009). In contrast, the numbers of rural poor have declined by 15.5percent over the same period. Urban poverty has been a problem affecting theIndian society for the last few decades and is a serious challenge to the intellec-tuals, politicians, and planners (Hashim, 2009; Nath, 1994). Since the Sixth FiveYear Plan, a sizeable reduction in the magnitude of urban poverty has beenone of the major objectives of planning in India.

The persistence of poverty in India has drawn more serious attention fromscholars and policymakers alike in the past several decades, leading to a signifi-cant 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 (41130750,41329001, and 41550110226).

k K. YENNETI, Post-doctoral Researcher, Nanjing Institute of Geography and Limnology, Chi-nese Academy of Sciences, Nanjing, 210008, China; [[email protected]]. Y. DENNIS WEI, Profes-sor and Corresponding Author, Department of Land Management, Zhejiang University,Hangzhou, 310029, China, and Department of Geography, University of Utah, Salt Lake City,UT84112-9155, USA; [[email protected]]. W. CHEN, Professor, Key Laboratory of WatershedGeographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy ofSciences, Nanjing, 210008, China; [[email protected]].

Geographical Review 107 (2): 360–383, April 2017Copyright © 2016 by the American Geographical Society of New York

out by development economists concerned with the analysis of trends in ruralpoverty over time and the widening gap between urban-rural poverty levels(Jha 2000; Deaton and Dreze 2002; Sen and Himanshu 2004). A few othershave examined certain problematical aspects of urban poverty, such as employ-ment, the relationship between urbanization and poverty, and poverty of slumdwellers (De Souza 1978; Kundu 2000). The methodology for measurement ofpoverty in India—what poverty is, what the parameters of it are, and what dis-tinguishes the poor from the nonpoor—has also been the focus of much debate(Dandekar and Rath 1971; Ahluwalia 1978). Although few studies on the regio-nal analysis of rural and food poverty (Dayal 1989, 1993) provide some illumi-nating insights to regional concentration of poverty in India, little literature isavailable focusing on the analysis of urban poverty in India.

So far as studies on Indian poverty are concerned, the major focus of boththe economic development and geographical literature has been on examiningrural poverty, while paying little attention to the spatio-temporal variations ofurban poverty, which are conspicuous in a large, rapidly urbanizing countrylike India. By overemphasizing food and nutrition, even the existing studiesappear to have ignored several vital factors related to poverty, such as educa-tion, clothing, water, and sanitation.

The objective of this study is to identify and interpret the spatio-temporalvariations of urban poverty in India (considering both food and nonfooditems) and its patterns of change over time. The spatial scale of this studyincludes the twenty-nine major states of India and the temporal period con-sists of the years 2004–05, 2009–10, and 2011–12. The study moves forwardthe literature on poverty in India, and urban poverty in the developingworld, by identifying the spatio-temporal variations that underlie urban pov-erty in a rapidly urbanizing economy. The results of this research serve aca-demic and scientific purposes, as well as provide insights for policymakers,local and national governments, and development institutions involved indesigning and implementing policies to alleviate urban poverty and providefor 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 ofincome to satisfy “minimum needs” for physiological survival (Sen 1979, 1981).People with income below the level required to satisfy minimum needs—oftendefined in terms of food consumption, especially caloric or nutritional require-ments of a subsistence level—are considered “below the poverty line.” Thoughthe measure of absolute poverty is the typical procedure for estimating the pov-erty line, there are several problems with this approach. First, it is difficult toassess one’s income accurately. Further, even with the attainment of a mini-mum income, if it is not spent, one will be living below the poverty line, as hisminimum needs remain unfulfilled.

URBANIZATION OF POVERTY IN INDIA 361

The problems in defining poverty in terms of income have made consumerexpenditure the proxy for income. According to the consumer-expenditureapproach, poverty is measured by the proportion of expenditure spent on cer-tain essential items of consumption, such as food (Ojha 1970). Here, the mini-mum expenditure necessary for meeting minimal needs, especially food, isascertained, and those people or households without an income to meet theseminimal needs are considered below the poverty line.

The traditional approaches to poverty—characterized in terms of a shortfallin a monetary indicator (income or expenditure) to satisfy minimum needs forphysiological survival—have been highly influential in the past several decades.At the same time, a body of international literature, including the nonfooddimensions of minimum needs, such as drinking water, clothing, shelter, edu-cation, and health care, besides food, has also developed (Dercon and Krishnan1996; Moser 1998; Ray and Lancaster 2005). This wider conception of povertyappears to be particularly well suited for international organizations, such asthe World Bank and the United Nations Development Programme, which rec-ognize low levels of attaining basic amenities and physical assets, along withfood and nutrition, among the characteristics of poverty (World Bank 2001;UNDP 2009). It is rather surprising, however, that while studies on measuringpoverty in India referencing consumer expenditure on food consumption andnutritive values exist in both development (Dandekar 1981; Sundaram and Ten-dulkar 2003) and geography (Dayal 1989, 1993) literatures, there is little workusing 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 intoaccount the whole gamut of consumption items and establish a minimum fam-ily budget in real terms, looking at the cost of living. This argument is specifi-cally relevant for analyzing poverty in urban areas, where it should reflect theincome needed not only to purchase sufficient food, but also to obtain a secureshelter with adequate water and sanitation, to pay for transport, to keep chil-dren at school, and to afford health care and medicine when needed. The non-food monetary costs of avoiding poverty are generally higher in urban areasthan in rural areas, as access to housing, resources, and services are monetized,and more expensive in larger or more prosperous cities in India. This studytries to fill the existing void in the literature by endeavoring to analyze urbanpoverty in India using both food and nonfood consumption-expenditurerequirements.

FACTORS AFFECTING POVERTY

There have been a sheer quantum of international studies undertaken toexplain the different factors affecting poverty, including income and wealth(Shaw 2010), basic needs (Wodon 1997), health and literacy (Ghosh 2010), andemployment (Razavi 1999). In his seminal work on “Poverty and Famines: An

362 GEOGRAPH ICAL REV IEW

Essay on Entitlement and Deprivation,” Amartya Sen provides two insights:people are prone to poverty when they lose their primary entitlements—thewages earned from an occupation, crop or livestock raised, gifts, donations,investments, and inheritance—to access commodities; and poverty occurs in acountry where democracy, media, and social justice are subdued due to condi-tions that in which government allow poverty to worsen (1981). While Sen’sexchange-entitlement theory provides some influential ideas for explaining pov-erty, it fails to recognize the environmental, political, and social determinants—such as poor weather conditions and natural disasters, conflict and war, gov-ernment 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 pov-erty have also tried to identify the factors affecting poverty in India (Bardhan1985; Dayal,1989, 1993; Sundaram 2001). This ever-growing literature has beensignificantly contributing to the advancement of Sen’s observations and povertyscholarship in general, by identifying a wide range of determinants to poverty,including inadequate nutrition, landownership, agricultural productivity, unem-ployment, labor-force participation, household size, and caste and gender dis-crimination. However, most studies are limited to rural areas and agriculturallaborers, leaving out altogether an explanation for urban poverty. As well, exist-ing studies are limited in their appreciation of influential factors such as educa-tion, health, shelter, and public services. Further, in a socioeconomicallydynamic country like India, the determinants of poverty are likely to vary overtime and across space, similar to poverty and inequality in many other coun-tries (Petrakos and others 2005; Li and Wei 2010). This points to the need foridentifying all the possible determinants of poverty at a more disaggregatedlevel. Analyses should be extended to cover rural and urban areas separately, asthere are significant differences between them in characteristics of the poor andthe determinants of poverty. No systematic attempt has been made to examinethe influence of these multiple factors on urban poverty in India, a gap thisstudy makes an attempt to fill.

THE URBAN POVERTY LINE IN INDIA

Poverty has conventionally been estimated with reference to a poverty line, thecutoff between poor and nonpoor. While rural poverty in India has been a partof the policy debate since the pre-Independence period, urban poverty was notrecognized as a concern until the early 1960s. Owing to this, while the earliestestimates on “rural poverty line” can be traced to 1938 by the National Plan-ning Committee, the first “urban poverty line” was formulated by the PlanningCommission in 1962.

Since that first official estimation, a series of urban poverty lines have beenconstructed by different committees constituted under the Planning Commis-sion of India. Rounds of National Sample Survey (NSS) data on consumer


expenditure have been used in these estimates. Dandekar and Rath (1971) madethe first systematic assessment of urban poverty line at national level, based onNational Sample Survey (NSS) consumer expenditure data from 1960–61. Theyargued that the poverty line must be derived from what is necessary to provide2,250 calories per day in both rural and urban areas. This led to intense debateon minimum-caloric consumption norms and a new poverty line proposed bythe Alagh Expert Group Committee, defined as a monthly per capita consump-tion expenditure (MPCE) level of Rs.49.09 for rural areas and Rs.56.64 forurban areas at 1973–74 prices (28th round of NSS data) at national level. Thesefigures correspond to the monetary value of goods and services that wouldcover per capita daily caloric requirement of 2,400 in rural areas and 2,100 inurban areas, along with other nonfood items such as clothing, footwear, educa-tion, health, and transport (GoI 1979). Another group, chaired by Gaurav Lak-dawala, supported the poverty line estimation approach as a caloric norm anda fixed-consumption basket. For the first time, state-specific rural and urbanpoverty lines are recommended (GoI 1993).

The most recent methodology for establishing an estimation of povertylevels in India was proposed by the Tendulkar Expert Group Committee in2009 (GoI 2009). Their urban poverty lines are employed in this study for sev-eral reasons (Table 1). First, they consciously moved away from caloric normssince caloric intake could not be successfully correlated to nutritional income;second, it provided a uniform “poverty-line basket” (of food and nonfoodcomponents) to both rural and urban populations; third, it incorporated anexplicit provision for expenditure on health and education; fourth, it providedstate-level rural and urban poverty lines in 2000s; and most importantly, anumber of previous studies have employed this methodology.

The urban poverty lines in this new methodology were computed based onthe MPCE of a gamut of consumption items: cereal, pulses, milk, edible oil,meat, vegetables, fresh fruits, dry fruits, sugar, salt and spices, tobacco andintoxicants, fuel, clothing and bedding, footwear, education and medicalexpenses, and other foods, goods, and services.

One limitation of this methodology is the lack of a specific urban centerpoverty line. In India, the prices of commodities and services may vary signifi-cantly across different size classes of urban centers, even within one state. How-ever, as no official estimates in India provide either a unit-level urban povertyline or even unit-level data on urban MPCE, we have used state-specific urbanpoverty 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-Thor-becke Poverty Index, Human Poverty Index, and Capability Poverty Index.Normally, poverty in India is measured by the Headcount Ratio. This is

364 GEOGRAPHICAL REV IEW

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 totalnumber of people in the society. This method has been used, explicitly or byimplicitly, by the Planning Commission in India ever since the quantitativestudy and measurement of poverty began. In this study, urban poverty is mea-sured by the Foster-Greer-Thorbecke (FGT) Poverty Index, while using MPCEas a measure of welfare. The Headcount Ratio (PHR), the Poverty Gap Ratio(PGR), and the Squared Poverty Gap Ratio (SPGR) belong to a family ofdecomposable poverty measures known as the Foster-Greer-Thorbecke Index.A poverty measure is said to be decomposable “if the poverty measure of a

TABLE 1—STATE-LEVEL URBAN POVERTY LINES IN INDIA BASED ON MPCE (TENDULKAR COMMITTEE

METHODOLOGY)

S. NO STATES

URBAN POVERTY LINE (RS.)

2004–05 2009–10 2011–12

1 Andhra Pradesh 563 926 1009

2 Arunachal Pradesh 618 925 1060

3 Assam 600 871 1008

4 Bihar 526 775 923

5 Chhattisgarh 514 807 849

6 Delhi 642 1040 1134

7 Goa 671 1025 1134

8 Gujarat 659 951 1152

9 Haryana 626 975 1169

10 Himachal Pradesh 606 888 1064

11 Jammu & Kashmir 603 845 988

12 Jharkhand 531 831 974

13 Karnataka 588 908 1089

14 Kerala 585 831 987

15 Madhya Pradesh 532 772 897

16 Maharashtra 632 961 1126

17 Manipur 641 955 1170

18 Meghalaya 746 990 1154

19 Mizoram 700 939 1155

20 Nagaland 783 1148 1302

21 Orissa 497 736 861

22 Punjab 643 961 1155

23 Rajasthan 568 846 1002

24 Sikkim 742 1035 1226

25 Tamil Nadu 560 801 937

26 Tripura 556 783 920

27 Uttar Pradesh 532 800 941

28 Uttaranchal 602 899 1082

29 West Bengal 573 831 981

All India 579 860 1000


group is a weighted average of the poverty measures of the individuals in agroup” (Makoka and Kaplan 2005, 20). A generalized version of poverty indicesconsidered by Foster and others (1984) is as follows:

FGT ¼ Paðx; x�Þ ¼Xp1¼1

1� xix�

� �a

¼ PHR when a ¼ 0

¼ PGR when a ¼ 1

¼ SPGR when a ¼ 2

ð1Þ

where x*= urban poverty line; xi = monthly per capita consumption expendi-ture of ith individual; and P = number of persons with consumption expendi-ture less than x*. The parameter a can be viewed as a measure of povertyaversion: a larger a gives greater emphasis to the poorest poor. As a becomesvery large, Pa approaches a welfare measure, which considers only the positionof the poorest household.

The urban MPCE data used in this analysis comes from the 61st, 66th, and68th rounds of the nationwide household consumer expenditure survey (CES)conducted by the National Sample Survey Organization (NSSO). The MPCEconsiders food items (beverages, cereals and substitutes, pulses, edible oil, milkand milk products, tobacco), and nonfood items (clothing and bedding, con-veyance, durable goods, education, entertainment, footwear, rent, medicalexpenses, other goods and services, and household consumables).

The NSSO collects CES data from different commodity groups for ruraland urban sectors of the country, for states and Union Territories (UTs), andfor different socioeconomic groups, by using the household-interview methodfrom a randomly selected sample of households. The NSSO conducts MPCEsurveys using three methods: Uniform Reference Period, Mixed Reference Per-iod, and Modified Mixed Reference Period.2 The Uniform Reference Period(URP) refers to the MPCE data collected using a reference period of “last thirtydays.” The Mixed Reference Period (MRP) refers to the MPCE data collectedusing a reference period of “last 365 days” for five nonfood items, such asclothing and bedding, footwear, durable goods, education, and institutionalmedical expenses, and a reference period of “last thirty days” for the remainingitems, 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, refresh-ments, 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 povertyestimation is considered over URP and MMRP, as MRP-based estimates cap-

366 GEOGRAPHICAL REV I EW

ture the household consumption expenditure by poor households for low-fre-quency items of purchase more satisfactorily than URP and MMRP. Further-more, while the URP or MRP based consumption data is available for all theperiods (that is, 61st, 66th, and 68th NSS Round survey on consumer expendi-ture), although MMRP-based consumption data is available only for 66th and68th 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-gapratio (PGR, which reflects the depth of poverty), and the squared poverty-gapratio (SPGR, which reflects severity of poverty) for urban populations at bothnational and state level over three periods of time: 2004–05, 2009–10, and2011–12. Of the three measures, PGR and SPGR are sensitive to the distributionof the population below the poverty line.

We consider first the changes of PHR, PGR, and SPGR for urban popula-tion at national level. The incidence of poverty in India, measured by the PHRfor urban population, declined from 25.8 percent in 2004–05 to 20.9 percentand 13.69 percent in 2009–10 and 2011—12, respectively. The results in terms ofdistribution-sensitive measures were in line with the changes in PHR. Thedeclining trend (direction and magnitude) of all-India level urban poverty inthis study can be closely related to the declining levels of urban povertybetween 1993–94 and 1999–2000 as observed by Kundu and Sarangi (2005).However, the figures in this study diverge from Kundu and Sarangi due to ourdifferent approach to measurement of urban poverty, different levels of urbani-zation in the country, and different time period employed in this study.

The spatial changes in urban poverty were strikingly different from thenational trend. In terms of PHR, while urban poverty declined in India as awhole between 2004–05 and 2009–10 (down 5 percent), in several states itincreased. Of the twenty-nine states included in this study, PHR declined intwenty and increased in nine (Table 2). Likewise, the urban poverty, measuredby PGR and SPGR, declined in twenty-one states and increased in eight, withexception to Haryana, which recorded an increase in PHR. A striking pointrevealed in the analysis for 2004–05 and 2009–10 is the drastically increasedlevels of urban poverty in several states of India, despite the increased urbanconsumption expenditures levels in all states. For example, in Delhi, Jammuand Kashmir, and Jharkhand, where the per capita consumption expenditurelevels increased, the percentage of urban poor also increased. The northeaststates—Arunachal Pradesh, Assam, Manipur, Mizoram, and Nagaland—joinedthe other states in recording a rise in PHR.

Significant improvements in trends of state-level urban poverty wereobserved over the 2009–10 and 2011–12 periods. In terms of PHR, urbanpoverty had shown a distinct decline in all but only one of the twenty-ninestates. Chhattisgarh, which recorded a significant increase in PHR (and, there-


fore, also in the number of urban poor) between 2004–05 and 2009–10 (from28 percent to 23 percent), had shown a decline in PGR and SPGR. A marginalincrease in urban PHR in Chhattisgarh can be associated with a decline in theincidence of rural PHR (GoI 2014).

TABLE 2—PHR, PGR, AND SPGR ON MIXED REFERENCE PERIODS (MRP): 2004–05, 2009–10, AND

2011–12, URBAN POPULATION

STATES

POVERTY HEADCOUNT RATIO

(PHR) (PERCENTAGE)POVERTY GAP RATIO (PGR)

(PERCENTAGE)SQUARED POVERTY GAP RATIO

(SPGR) (PERCENTAGE)

2004–05 2009–10 2011–12 2004–05 2009–10 2011–12 2004–05 2009–10 2011–12

Andhra

Pradesh

23.4 17.6 5.8 4.8 3.8 0.8 1.5 1.2 0.2

Arunachal

Pradesh

23.5 24.8 20.3 4.6 6.0 4.9 1.3 2.1 1.9

Assam 21.8 25.8 20.5 4.3 5.9 3.8 1.2 1.9 1.0

Bihar 43.7 39.3 31.2 11.4 10.3 6.8 3.9 3.7 2.1

Chhattisgarh 28.4 23.6 23.9 7.2 6.2 5.2 2.6 2.2 1.9

Delhi 12.9 14.3 9.8 2.0 2.9 1.6 0.5 0.9 0.3

Goa 22.2 6.4 4.0 4.3 1.2 0.7 1.5 0.3 0.2

Gujarat 20.1 17.6 10.2 3.9 3.6 1.6 1.1 1.0 0.4

Haryana 22.4 23.0 10.2 4.9 4.5 1.7 1.6 1.2 0.4

Himachal

Pradesh

4.6 12.4 4.3 1.1 2.3 0.7 0.4 0.6 0.2

Jammu &

Kashmir

8.9 12.7 7.2 1.7 1.9 0.9 0.5 0.4 0.2

Jharkhand 23.8 31.0 24.8 5.8 7.9 4.8 1.9 2.8 1.4

Karnataka 25.9 19.5 15.2 6.2 4.3 3.0 2.1 1.4 0.8

Kerala 18.4 12.0 4.9 4.0 2.1 0.8 1.3 0.6 0.2

Madhya

Pradesh

35.1 22.8 21.0 8.6 5.5 3.8 2.9 1.8 1.0

Maharashtra 25.6 18.2 9.1 6.5 3.9 1.5 2.3 1.2 0.4

Manipur 34.5 46.3 32.3 5.1 8.9 6.1 1.0 2.5 1.7

Meghalaya 24.7 23.9 9.2 2.8 4.9 1.4 0.5 1.2 0.2

Mizoram 7.9 11.5 6.3 1.0 1.8 0.6 0.2 0.4 0.1

Nagaland 4.3 24.9 16.4 0.5 3.1 1.7 0.1 0.5 0.3

Orissa 37.6 25.9 17.2 9.6 5.3 3.1 3.5 1.7 0.8

Punjab 18.5 18.0 9.2 3.2 3.7 1.5 0.8 1.1 0.4

Rajasthan 29.7 19.9 10.6 5.7 3.7 1.5 1.7 1.1 0.3

Sikkim 25.9 4.2 4.3 3.4 0.7 0.5 0.9 0.1 0.1

Tamil Nadu 19.7 12.7 6.5 4.1 2.1 1.1 1.3 0.5 0.3

Tripura 22.5 9.5 7.4 3.8 1.6 1.7 1.0 0.3 0.5

Uttar Pradesh 34.1 31.6 26.1 7.8 7.3 5.2 2.5 2.3 1.5

Uttarakhand 26.2 25.0 10.4 5.1 5.1 1.5 1.4 1.4 0.3

West Bengal 24.4 21.9 14.6 5.3 4.5 2.7 1.6 1.4 0.7

All India 25.8 20.8 13.6 6.1 4.6 2.7 2.0 1.5 0.8


Yet another important point observed in this study is the incidence of sev-ere urban poverty in many Indian states compared to rural areas. Several statesrecorded urban poverty levels higher than that of India as a whole in all thestudy periods. In 2004–05, eight states had shown PHR higher than that ofnational average. The number increased to fourteen in 2009–10 and thendeclined to twelve in 2011–12. The results in terms of the PGR were in line withthe SPGR, but with exception in the year 2009–10. In 2009–10, while the num-ber of states with PGR above the national average were eleven, in terms ofSPGR the number stood at nine. The results of the PGR and SPGR, takentogether, however, were in contrast with the figures of PHR.

The most important finding of this analysis is that during all periods, thefigures of the three urban poverty measures were much higher than thenational average in Bihar, Chhattisgarh, Madhya Pradesh, Orissa, and UttarPradesh. During the same periods, the urban consumption expenditure levelsin these states were also found to be much lower than the levels in India as awhole. Similarly, Jharkhand and Manipur also recorded higher the national-level PHR, PGR, and SPGR in 2009–10 and 2011–12.

SPATIAL DISTRIBUTION OF URBAN POVERTY

The average urban consumption expenditure levels in India increased fromRs.579.0 monthly per capita consumption expenditure in 2004–05 to Rs.860.0and Rs.1000.0 in 2009–10 and 2011—12, respectively. In all three periods, abouthalf of the states recorded average consumption expenditure levels belownational levels. A distinct spatial contrast in the distribution of consumptionexpenditure levels was also witnessed in the three study periods. In 2004–05,there was a heavy concentration of the lowest standard of living in the coastaland central states of India, with the exception of Tripura (a northeastern state)and Rajasthan (a northern state), where the urban population’s consumptionexpenditure was lower than Rs.579.0 (Table 1). This spatial pattern appeared toexist in other periods as well, with the addition of Jammu and Kashmir, anorthern state.

We now turn to a consideration of spatial contrasts in the distribution ofurban poverty, measured in terms of PHR, PGR, and SPGR in the twenty-ninestates over the study periods. Figures 1, 2, and 3 present urban poverty (fora=0, 1, 2) vary across states, with the darker color indicating higher povertyindex and thus higher rates of urban poverty.

With identical patterns of consumption expenditure, the PHR in 2004–05was highest in the central and eastern states of India, such as Bihar, Chhattis-garh, Madhya Pradesh, Uttar Pradesh, and Orissa. The southern states, AndhraPradesh and Tamil Nadu, are exceptions, witnessing a below national averageincidence of PHR, despite of low consumption expenditure levels. Anotherworthy observation for the same period was the high concentration of PHR inthe northeastern states, notwithstanding the high consumption expenditure


FIG. 1—Urban Poverty Levels, measured in terms of PHR or decomposition of the FGTindex (a=0), in India (in percentage)

FIG. 2—Urban Poverty Levels, measured in terms of PGR or decomposition of the FGTindex (a=1), in India (in percentage)


levels. Only the relatively prosperous north and northwestern states had lowconcentration of PHR in all periods. Similar spatial patterns were also observedin 2009–10 and 2011–12, with exception to some of the northeastern states, suchas 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. Thecoastal state of West Bengal, which recorded a high concentration of PHR inall three periods (and, therefore, also in the number of poor), had shown a lowPGR and SPGR. Similarly, the high concentration of PHR (for 2009–10 and2011–12) in Nagaland, a northeastern state, was in contrast to the low concen-tration of PGR and SPGR.

DETERMINANTS OF URBAN POVERTY

In the present study, a regression model based on the changing trends of atotal of eleven variables was employed to identify the variables associated withthe changes in urban poverty. Table 3 summarizes the definition, description,and data sources of all the variables used in this study. Previous studieshelped identify the variable selection (Dayal 1993; Mahajan and Kumar 2007;Dinesha and Jayasheela 2008; Sharma 2010). These studies, though largelyfocused on rural poverty, unambiguously emphasized the different factorsaffecting poverty, including annual growth rate, education and training,employment opportunities, and access to public services—all relevant forurban poverty as well.

FIG. 3—Urban Poverty Levels, measured in terms of decomposition of the FGT* index(a=2), in India (in percentage)


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 compo-nent analysis; the variable set is also well constructed to reflect the multipledeterminants of urban poverty over a period of time. Despite our efforts, bias

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

data of NSS 2004–05,2009–10, and 2011–12 on

consumer expenditure,

NSSO, GoI.

ii PGR (percentage)

iii SPGR (percentage)

iv Slum population Percentage of people in slums in

urban areas

State level data of IHDS

2004–05, Unit level dataof NSS 2008–09 and

2011–12 on Urban Slums,

NSSO, GoI.

v Annual urban

population growth

rate

Percentage State level data from

various NSS rounds

vi Inequality Inequality in urban consumption

expenditure (percentage)

Calculated from state level

data of NSS 2004–05,2009–10, and 2011–12 on

Consumer expenditure,

NSSO, GoI.

vii Access to water Percentage of population with no

access to safe drinking water to

total population

Extracted from Planning

Commission Data book

for 2004–05, state level

data of NSS 2008–09and 2011–12 on Housing

condition and amenities,

NSSO, GoI.

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

State level data of NSS

2004–05, 2009–10, and2011–12 on employment

and unemployment,

NSSO, GoI.

xi Literacy Percentage of population with

education atleast upto primary

school to total population

State level data of NSS

2004–05 on educational

and vocational training

and NSS 2009–10, and2011–12 on employment

and unemployment,

NSSO, GoI.

372 GEOGRAPHICAL REV I EW

may still exist due to data limitations regarding a lack of urban center specificpoverty line (section 4), which can be reduced by the availability of better datain future studies.

To understand the relationship between the eight independent variables andurban poverty measures (PHR, PGR, and SPGR), a simple correlation coeffi-cient analysis was carried out separately for all study periods (Table 4). Thestatistically significant and higher positive correlation between the distributive-sensitive measures and PHR, in all three study periods, support the hypothe-sized findings of Sundaram and Tendulkar (2003). The sign and magnitude ofpositive correlation between urban poverty measures (PHR, PGR, and SPGR)—no access to sanitation and the population living in poor to slum housingconditions—in all study periods confirm that unfit human habitations, due todilapidation or lack of sanitation facilities, can generally explain the variousurban poverty and slum population of India (Nath 1994; Chaplin 1999). Ashousing and sanitation make important considerations in explaining urbanpoverty in the regression model, they will be discussed in more detail later.

A higher positive correlation was recorded between inequality and distribu-tive-sensitive urban poverty measures in all study periods, and betweeninequality and PHR in 2004–05. This supports earlier studies that viewed pov-erty as a reflection of inequality, implying how the concentration of a nation’swealth in a few can throw large numbers into poverty and how redistributionof income, expenditure, or other resources can be an incentive for the reduc-tion of poverty (Jha 2000; Sen and Himanshu 2004). On the other hand, higherlevel of negative correlations were observed between literacy rate and the othervariables—urban poverty measures, slum population, inequality, access to sani-tation—in all study periods, with exception to 2004–05, when the correlationbetween literacy and urban poverty measures was statistically significant. Signif-icant positive correlation was recorded between literacy rate and urban popula-tion growth in all three periods, including literacy and unemployment, andliteracy and access to water in the 2009–10 and 2011–12 periods.

An important observation here is that correlation coefficients betweenunemployment and urban poverty were either negative or weak in all threeperiods, while positive correlation was observed between unemployment andurban population growth in 2004–05 and 2011–12. These observations contradictthe traditional arguments that indicate the incidence of unemployment inurban areas to migration of people from rural areas—that is, the employment-creating capacity of the urban centers is often too inadequate to absorb anever-growing labor force—and thereby the widespread unemployment to urbanpoverty (Kundu 2000; Kundu and Sarangi 2005). The value of correlationsbetween the independent variables does not show presence of multicollinearity.

We included four of eight variables, inequality, access to water, access to sani-tation, and access to safe housing, which recorded statistically significant andhigh correlation coefficients, in the multivariate regression model (Table 5).


TABLE4—

SIMPLE

CORRELATIO

NMATRIX

(a=0,1,2)

[N=29]

VARIABLES

2004–05

PHR

(I)

PGR

(II)

SPGR

(III)

SLUM

POPULATIO

N

(IV)

ANNUAL

URBAN

POPULATIO

N

GROWTH(V)

INEQUALITY

(VI)

ACCESS

TO

WATER(V

II)

ACCESS

TO

SANITATIO

N

(VIII)

ACCESS

TO

SAFE

HOUSING(IX)

UNEMPLOYMENT

(X)

LITERACY

(XI)

i1

ii0.92*

1

iii

0.84*

0.98*

1

iv0.21

0.19

0.18

1

v�0

.25

�0.29

�0.27

�0.36¤

1

vi0.25

0.45§

0.53¤

0.24

�0.22

1

vii

0.22

0.25

0.26

�0.01

�0.22

0.06

1

viii

0.23

0.35¤

0.36¤

0.17

�0.21

0.19

0.04

1

ix0.29

0.22

0.16

�0.04

�0.03

�0.39§

�0.06

0.06

1

x0.19

0.17

0.16

�0.19

0.25

0.15

�0.07

0.45§

0.14

1

xi�0

.50*

�0.57*

�0.57*

�0.16

0.28

�0.39§

�0.47§

�0.04

0.11

�0.04

1

2009–10

i1

ii0.95*

1

iii

0.88*

0.98*

1

iv�0

.16

�0.11

�0.08

1

v�0

.32¤

�0.29

�0.27

0.05

1

vi0.03

0.14

0.20

0.23

�0.41§

1

vii

0.02

�0.12

�0.18

0.00

�0.25

�0.37§

1

viii

0.46§

0.54*

0.57*

0.28

�0.18

0.16

�0.22

1

ix0.36¤

0.40§

0.57*

�0.24

�0.04

�0.12

0.01

0.13

1

x0.04

�0.04

�0.09

�0.20

�0.07

�0.11

0.16

0.14

0.03

1

(continued)

374 GEOGRAPH ICAL REV I EW

TABLE4—

CONTIN

UED

VARIABLES

2004–05

PHR

(I)

PGR

(II)

SPGR

(III)

SLUM

POPULATIO

N

(IV)

ANNUAL

URBAN

POPULATIO

N

GROWTH(V)

INEQUALITY

(VI)

ACCESS

TO

WATER(V

II)

ACCESS

TO

SANITATIO

N

(VIII)

ACCESS

TO

SAFE

HOUSING(IX)

UNEMPLOYMENT

(X)

LITERACY

(XI)

xi�0

.38§

�0.44§

�0.45§

�0.26

0.25

�0.44§

0.35§

�0.50*

�0.02

0.20

1

2011–12

i1

ii0.98*

1

iii

0.92*

0.98*

1

iv�0

.12

�0.12

�0.10

1

v0.11

0.08

0.06

�0.12

1

vi0.08

0.15

0.18

0.23

0.27

1

vii

0.16

0.12

0.08

�0.41§

0.32¤

�0.01

1

viii

0.44§

0.48*

0.48*

0.24

0.20

0.15

�0.05

1

ix0.35¤

0.43§

0.53*

0.07

0.00

�0.01

�0.05

0.16

1

x0.06

0.00

�0.02

�0.06

0.33¤

�0.26

0.12

0.07

�0.08

1

xi�0

.27

�0.28

�0.27

�0.38§

0.07

�0.37§

0.15

�0.59*

�0.17

0.28

1

(*),(§),and(¤)indicatestatisticalsignificance

at1%

,5%

,and10%

level,respectively.


TABLE5—

REGRESSIO

NCOEFFICIENT

OFSIGNIFICANT

VARIABLESREGRESSED

AGAIN

STURBAN

POVERTY(a=0,1,2)

[N=29]

VARIABLES

PHR

PGR

SPGR

2004–05

2009–10

2011–12

2004–05

2009–10

2011–12

2004–05

2009–10

2011–12

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)

Accessto

water

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)

Accessto

sanitation

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)

Accessto

safe

housing

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)

For

allvariables

R²

0.30

0.32

0.31

0.41

0.41

0.39

0.53

0.49

0.47

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)

F2.62

2.79

2.68

4.16

4.16

3.81

6.76

5.66

5.35

Note:Figuresin

parentheses

representt-values.(*),(§),and(¤)indicatestatisticalsignificance

at1%

,5%

,and10%

level,respectively.


In all study periods, population living in weak housing conditions (kutcha, whichmeans a house made up of wood, mud, straw and dry leaves and semi-pucca, whichmeans a house whose walls is made up of brick, cement and mortar but roof ismade up of wood, mud, straw, and dry leaves.) and inadequate access to sanita-tion emerged as the most important variables in explaining urban poverty inIndia, accounting for a large portion of variance. Adding the variable of access toclean water further improves the explanatory power of the regression model.Access to clean water is positively related to all urban poverty measures, and therelationship is statistically significant at 0.05 and 0.1 levels in 2004–05 and2011–12. The importance of access to clean water, as an explanatory variable forurban poverty, highly correlates to access to sanitation. These regression results,therefore, imply that lack of access to basic services, such as shelter, water, andsanitation, at an affordable cost could increase the monetized burden on theurban poor. States with lower access to these basic services also had a higherincidence of urban poverty. For example, high concentrations of urban povertywere recorded over central and eastern states, such as Bihar, Chhattisgarh, Jhark-hand, and Orissa, where a large percentage of the total urban population existswithout adequate access to water and sanitation facilities. Massive problems haveemerged in these states due to the rapid growth of urban population without acorresponding increase in urban infrastructure, such as safe drinking water, sani-tation facility, and housing.

The regression results on factors affecting urban poverty also support ourhypothesis that, in urban areas of India, the expenditures on nonfood costs ofavoiding poverty—securing a safe and secure shelter with adequate sanitation—are usually higher than in rural areas. It is well recognized that as housingprices becomes more expensive in big cities and affordable housing becomesinadequate, a large percentage of migrant population and urban poor end upliving in slums or squatter settlements where vacant state-owned or privateland is occupied illegally. According to the Tenth Five Year Plan, the nationneeded twenty-two million additional houses. City slums are visible pockets ofpoverty, with miserable access to basic services, such as water and sanitation.Housing, combined with sanitation and water, emerged as important determi-nants affecting upward mobility, in terms of consumption expenditures, inurban areas.

The problems of housing and access to public services will worsen, unlessconcerted measures are taken. The living conditions of the vulnerable sectionsof the urban society—slum dwellers/urban poor—will lead to the serious crip-pling of productive capacity for a growing number of people. Any significantdecline in urban poverty in India over the past years can be attributed to theseveral national schemes—Rajiv Awas Yojana, Urban Basic Services for thePoor, Improvement Of Civic Amenities through Improvement in Water Supplyand Sanitation—,initiated by the central government, especially since theSeventh Five-Year Plan, for providing housing and basic services to the urban


poor. Likewise, the relative urban prosperity in north, northwest, and some ofthe 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 ofurban poverty alleviation schemes. There is growing empirical evidence to sug-gest that affordable and safe housing, water, and sanitation provide incentivesfor 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 urbanpoverty is taken into account, the next conspicuous variable is inequality. Theregression coefficient for inequality was positive for all periods and statisticallysignificant in 2004–05, indicating that inequality in urban areas increases theincidence of urban poverty. Several studies confirm that reducing inequality iscrucial for ameliorating living conditions of the poor (Jha 2000; Sen andHimanshu 2004). Other studies suggest that poverty and inequality are differ-ent actors (Dayal 1993). We considered inequality because it was positively cor-related with urban poverty. Significant inequality or large disparities inconsumption expenditures between urban populations bring in its trail imper-fections in the labor market as well as social structure, which erect a barrierbetween the urban poor and the more affluent urban dwellers, constrainingupward economic mobility of the urban poor. Inequality is an important deter-minant of urban poverty, though its influence is very complex and may vary indirection and magnitude over time and space. Inequality in income or con-sumption can also lead to inequality in access to basic services, including safedrinking water and sanitation.

These four variables account for more than 40 percent of the variation inurban poverty in almost all study periods (Table 5). The model leaves morethan 50 percent of the variance unexplained, which may partly be due toaggregation of data for states—altogether too large for an accurate spatial anal-ysis.

CONCLUSIONS

In this paper an attempt has been made to identify regional patterns of urbanpoverty over three periods, using state-level NSS data on consumption expendi-tures for the 61st (2004–05), 66th (2009–10), and 68th (2011–12) rounds, and aclass of decomposable poverty measures. So far as studies on Indian povertyare concerned, the major focus was on examining rural poverty, especially dur-ing the 2000s. Here, state-specific new urban poverty lines (Tendulkar Method-ology) based on consumption expenditures for both food and nonfood itemswere employed to measure the incidence of urban poverty in each of thetwenty-nine major states. Most of the previous studies were limited to the six-teen major states of India and the use of outdated caloric-norm methodologies.The employment of new urban poverty lines was an important development in


two ways: eliminating discrepancies in the estimation of urban poverty arisingfrom insufficient income to purchase sufficient food and secure shelter withadequate water and sanitation, and to reveal a more accurate and current pic-ture of urban poverty in India.

The analysis revealed that, at the national scale, urban poverty, measured bypoverty- headcount ratio (PHR) and two distributive-sensitive indicators,namely poverty-gap ratio (PGR) and squared poverty-gap ratio (SPGR), consis-tently declined over the three study periods (2004–05, 2009–10, and 2011–12).The changes in urban poverty at state-level were rather different. Between2004–05 and 2009–10, PHR declined in twenty states and increased in nine,while distributive-sensitive measures (PGR and SPGR) declined in twenty-onestates and increased in eight, with exception to Haryana, which recorded anincrease in PHR. Over the next two periods (2009–10 and 2011–12), all but oneof the twenty-nine states recorded decline in PHR. Chhattisgarh, whichrecorded an increase in PHR, had shown a decline in PGR and SPGR. In sup-port to the findings of Sundaram and Tendulkar (2003), the results of thisstudy revealed that the distribution-sensitive measures are proportional to thePHR and hence usually (though not always) move in the same direction asPHR, with some notable exceptions, as noted.

An in-depth analysis indicated that consistent urban poverty reductionfailed in large central, eastern and northeastern states, such as Bihar, Chhattis-garh, Madhya Pradesh, Uttar Pradesh, Orissa, Manipur, Assam, and ArunachalPradesh, because of a backlog of social housing and public infrastructure. It isinteresting to note that urban poverty in some of these states was higher thanthat of national average, leading to the phenomenon of “urbanization of pov-erty” (Piel, 1997). The waning of public services in these states created new andunbearable costs to their urban citizens.

Despite the recent economic growth and reduction in poverty levels over thestudy periods, the central region of India is still frequently referred to asBIMARU, which means sick in Hindi. BIMARU—an acronym for Bihar, Mad-hya Pradesh, Rajasthan and Uttar Pradesh, though also Chhattisgarh andJharkhand3 —refers to the states that had traditionally been lagging behind inIndia’s development. On the other hand, the northeast region India—”The Landof Seven Sisters”—comprises the hill-states of Assam, Arunachal Pradesh,Meghalaya, Nagaland, Manipur, Mizoram, and Tripura. Regardless of the rich-ness in diversity of culture, ethnic composition, languages, and natural resources,the region, both urban and rural, remains largely underdeveloped. Due to isola-tionist policies, differential treatment, and internal conflicts, some the northeaststates have witnessed high levels of urban poverty over the past several decades.

Overall, the different urban poverty-alleviation programs implementedunder the national Five Year plans, especially since the Seventh Five Year Plan,did contribute to reducing urban poverty in India by raising the income andconsumption of numerous people, and opening access to many others. Liberal-


izing the economy—along with the increasing investments in urban basic ser-vices and affordable housing through national programs such as the PrimeMinister’s Integrated Urban Poverty Eradication Programme, National SlumDevelopment Programme, Jawaharlal Nehru National Urban Renewal Mission,and Rajiv Awas Yojana—has contributed to the rise of the middle class andurban poverty reduction in India.

The failure of poverty reduction at a more disaggregated level can be partlydue to the adoption of a uniform strategy for the whole country, withoutregard to the regional differences, the nature of the environment in the inci-dence of poverty, and the conditions in which the poverty-eradication pro-grams were implemented. On the bright side, when antipoverty programs didwork at the regional level, they have had a great influence on the economicstructure, helping people move up the economic ladder (for instance, inGujarat, Delhi, and Andhra Pradesh). In states with high urban poverty levels,reforms were conservative and incomplete, while an approach that penetratedto all layers of society was needed. Considering the scale of the country and thewidespread spatial concentrations of urban poverty, identification of regionalfactors might prove useful in urban poverty-eradication programs.

Through capturing temporal and spatial differentiation, our study provideda rich narrative and novel perspectives to the debates on urban poverty inIndia. It provides new depth to the poverty-measurement literature, as well asempirical figures to help design and implement policies and programs aimed aturban poverty reduction and the promotion of sustainable urban developmentin India. Importantly, policymakers can use the decomposition results to for-mulate a workable urban poverty-reduction policy. Our research suggests, forinstance, that the introduction of subsidy programs for goods that are largelyconsumed by poor, urban households and a progressive income tax structureshould result in a significant reduction of total urban poverty in India.

The Indian government needs to produce substantial city-level data on con-sumption expenditures to better analyze and provide for policy prescriptions atsubnational and subregional levels to reduce poverty.

NOTES

1 According to the Census of India, an urban area is defined as all places with a municipal-ity, corporation cantonment board or notified towns area committee, etc., and all other placeswith a minimum population of 5,000, have at least 75 percent of male working populationengaged in nonagricultural pursuits, and have a density of population of at least, 400 person persq. 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 aflow over time. The time period (a day, a month, or a year) for which consumption is recordedis called the reference period. It may vary from item to item.

3 Chhattisgarh and Jharkhand were formed in 2000 by partitioning Madhya Pradesh andBihar respectively. Hence, these two states are also referred on par with the other BIMARUstates.


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