Oct Dec 2000 Poverty

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Issues of Poverty in IndiaIssues of Poverty in IndiaIssues of Poverty in IndiaIssues of Poverty in IndiaIssues of Poverty in India

SHUBHASHIS GANGOPADHYAYSHUBHASHIS GANGOPADHYAYSHUBHASHIS GANGOPADHYAYSHUBHASHIS GANGOPADHYAYSHUBHASHIS GANGOPADHYAY

AbstractAbstractAbstractAbstractAbstract

Using the NSS consumption and employment surveys, this paper rais

some poverty related issues that have not been consistently researched in Indi

argues that close to one-third of all the poor live in only 8 of the 78 NSS regio

there is a gender bias in the incidence of poverty and bigger urban centres achi

higher labour productivity. The poor are poor because of low productivitynbecause they are without jobs. Generating human capital is the dominant polic

option in the war against poverty.

I. IntroductionI. IntroductionI. IntroductionI. IntroductionI. Introduction

In academic circles, as well as among policy-makers, poverty h

been a major issue for debate. The debate became politicised with the

garibi hatao campaign of the seventies and took it out of the confines

an economic problem. Not surprisingly, there seems to be no consensus

regarding either its extent, or the nature of the mechanisms necessary to

rid of it. Nevertheless, all agree that poverty continues to be an importissue that needs to be tackled. In this paper, I will try to steer clear of th

controversies and, concentrate on the nature, more than the extent of th

problem. In the process, hopefully, one will be able to generate some br

ideas on the policy directions that need to be followed.

At the very outset, I will like to mention that this paper is not a

comprehensive survey of issues relating to Indian poverty. If at all, this

paper should be read along with the huge, and excellent, literature that

already exists. Indeed, were I to paraphrase them for the readers of this

paper, I will not do justice to their effort. Instead, I will touch upon som

issues that I feel have not been seriously looked into in the study of Indi

poverty. Here I will concentrate on the research that I have undertaken

with my collaborators. Wherever necessary, however, I will refer to the

work of others.

For the sake of completeness, it is best to start with how povert

measured. Most official estimates, and the major part of the academic

literature, start with the definition of a poverty line. In simple terms, th

the money equivalent of a minimal set of goods and services that are

The author is on the faculty of the Indian Statistical Institute and SERFA


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considered essential for human existence. That part of the populace whose

money equivalent of consumption is below this value, are termed poor. It is

immediately obvious that the poverty line is a subjective norm. While

scientists can determine biological standards for food intakes, it is difficult

to fix the amounts and qualities of minimum clothing, housing, medical

facilities, etc., on a purely objective basis. Much of the debate on poverty

hinges on this particular aspect.

The next hurdle is the determination of the value of the given

basket of goods and services. Different households, at different places,

could buy similar baskets at different prices. Moreover, given the subjective

nature of the norm, and the fact that consumption patterns as well as the

availability of goods and services change over time, there is an additional

problem of determining the right price index for each region of the country.

To make matters worse, the poor consume commodity and service bundles,

which are quite different from that which is consumed by the average

person. And, finally, because the poor and non-poor operate in different

economic environments, the two groups pay different prices for the samebasket in the same region.

The best one can do is to fix a consistent methodology of measure-

ment and demonstrate how sensitive the results are to the assumptions

made in the exercise. Observe that the simplest measure of absolute

poverty, the HCR or the head-count ratio, which is the number of poor

people divided by the total population, can never be invariant to the

methodology used. It will certainly depend on the value of the poverty line.

However, one can check the robustness of some of the more qualitative

results the direction of change over time, relative ranking of a region vis-

-vis other regions in the country, relative incidence of poverty by genderof the head of the household, comparative proportions in rural versus urban

areas, etc. In Gangopadhyay, Dubey and Jain (1997) and Dubey and

Gangopadhyay (1998), such a comparison, with a common data set, was

carried out. It was found that the qualitative results were invariant to the

particular methodology used. Hence, throughout the rest of this paper, I

will use one particular methodology and not refer to the corresponding

results using other methodologies.

The data that will be referred to in this paper are the 1987-88 and

1993-94 National Sample Survey (NSS) household consumption and

employment surveys. The price indices used for different regions and the

two time points are explained in Dubey and Gangopadhyay (1998). For the

first use, and development of this methodology, see Minhas et. al. (1988).

In addition to different prices for different regions, this methodology takes

into account the consumption basket of the poor in deriving the weights for

the price index.

This paper will try and give a glimpse of some of the characteris-

tics of Indian poverty. It will not go into any detailed analysis, but will

refer the reader to the original research where the complete methodology is

worked out In Section II I will highlight the nature of the problem

The best one can d

is to fix a consiste

methodology

measurement a

demonstrate ho

sensitive the resu

are to the assum

tions made in t

exercis


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labour and poverty. Section IVdeals with gender bias and Section Vw

urban poverty. In Section VI, I conclude.

II. Attacking the ProblemII. Attacking the ProblemII. Attacking the ProblemII. Attacking the ProblemII. Attacking the Problem

Recently the debate on poverty has been bogged down in sema

tics. As we have already indicated, many argue about the value of the

poverty line. These have led to poverty estimates ranging from 40 per c

to 12 per cent. Some others have argued that the concept of using a nom

nal poverty line itself is flawed. In fact, a number of researchers and

organisations use deprivation indices, instead of the poverty line. In ma

cases this is a rough and ready measure that does no worse than the

poverty line, while in other cases it highlights the areas in which house

holds fall short of achieving a reasonable level of existence. For instanc

is not uncommon to find an urban slum where the majority of househol

have colour television sets, but no access to proper sanitation. Or, a

household in a village may be above the poverty line, but unable to sen

the children to school as the nearest functional school may be many miaway.

However, these examples do not necessarily mean that the clas

measure of poverty using the poverty line, is useless in any study of the

problem. This is because as a first step, the society must ensure that it i

potentially feasible for a household to command the basic amenities of

Once individuals are empowered with purchasing power, they will them

selves get what they need. Of course, this does not mean that all non-po

households will, for example, educate the girl child and, in reality, girls

may consistently be deprived of minimum education regardless of the

poverty status of the household. However, this is not a problem of povebut one of social awareness and has to be tackled differently from that

poverty alleviation.

The debate on the variables to use in the deprivation index, or

which of them are more significant than others, may be a perfectly val

academic pursuit, but waiting for the resolution of this debate will be

valuable time lost in the war against poverty. A better approach would

to ask oneself how different will be the outcomes, and hence their polic

implications, if one were to use deprivation indices as measures of pove

rather than the more traditional expenditure approach. Will regions tha

fare very badly in terms of one measure perform well in terms of anothe

In other words, is it possible that we will make a mistake in targeting if

choose a particular measure?

To understand the nature of the problem, let us first identify a

broad zone for targeting so that we can minimise on the leakage. Secon

let us establish that current measures of poverty are sufficient for the

immediate implementation of programmes. Third, the resources needed

have an impact on poverty are miniscule. In other words, if we so wan

should get going in right earnest instead of wasting unnecessary time in

quibbling about how to measure poverty

The debate on the

variables to use in

the deprivation

index, or which of

them are more

significant than

others, may be a

perfectly valid

academic pursuit,

but waiting for the

resolution of this

debate will be

valuable time lost in

the war against

poverty.


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Why are we interested in these regions? First, they are the worst

performing in terms of the incidence of poverty and should be the first

place to start any poverty alleviation programme. Second, they are geo-graphically contiguous and hence any spatial spillover of poverty subsidies

will go into other regions that are poor. And, third, we will argue that

when poverty is truly desperate, it does not matter what measurement is

adopted.

Let us examine how these regions fare in terms of food deprivation.

For that, we use the household food expenditure data in the NSS surveys.

The Expert Group (1993) uses a food weight of 0.81 in the total household

expenditure, while the weight derived from our calculations is 0.77. We

first identify the number of households whose food expenditure is less than

81 per cent of the poverty line, and consider them to be deprived of the

minimum amount of food. We repeat the exercise with food weights of 0.75

and 0.70 to check the sensitivity of the food weights. We can then compare

the household groupings by these alternative measures of poverty. The

results are given in Table-2.

If there was perfect correspondence between the poor by the

poverty line calculations and those that were deprived of food, then the

entries in the APL/BFPL and BPL/AFPL cells1 should be empty. For all the

TABLETABLETABLETABLETABLE 11111

Poverty Profile of Selected Regions, 1993-94Poverty Profile of Selected Regions, 1993-94Poverty Profile of Selected Regions, 1993-94Poverty Profile of Selected Regions, 1993-94Poverty Profile of Selected Regions, 1993-94

State Region Name Region HCR Food Contribution to All India

Number HCR HCR Food HCR

BiharBiharBiharBiharBihar Southern 5151515151 63.0563.0563.0563.0563.05 72.4272.4272.4272.4272.42 3.343.343.343.343.34 2.672.672.672.672.67

Northern 52 65.45 78.57 5.69 4.76

Central 53 59.66 69.14 3.80 3.07

UttarUttarUttarUttarUttar Western 252 29.79 53.08 4.23 5.25

PradeshPradeshPradeshPradeshPradesh Central 253 45.27 67.12 3.08 3.18Eastern 254 46.99 65.06 6.56 6.32

WWWWWestestestestest Eastern Plains 262 55.06 68.27 2.80 2.42

BengalBengalBengalBengalBengal Central Plains 263 31.66 47.81 2.66 2.79

TTTTTotal of Contributionotal of Contributionotal of Contributionotal of Contributionotal of Contribution 32.1732.1732.1732.1732.17 30.4630.4630.4630.4630.46

Note: The food poverty line has been defined as 81% of the overall poverty line.

1 APL stands for Above Poverty Line; BPL for Below Poverty Line; BFPL

the poor people in India live in only 8 NSS regions (out of a total of 78).

We define a regions contribution to all India HCR by the ratio of the poor

in that region to the total number of poor in India. The 10 worst (largest

contributions to all India HCR) regions account for 37.69 per cent of all the

poor in India. Of these, 8 are contiguous and account for 32.17 per cent of

the total poor. These are listed in Table-1.

We will argue th

when poverty

truly desperate,

does not matt

what measureme

is adopte


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three food based poverty lines (FPL), the entries in the APL/BFPL cells a

larger than that in the BPL/AFPL cells. That is to say that there are hou

holds that are deprived of food, but not counted as being below the pov

line. However, these numbers are relatively small compared to those in

South West diagonals of Table-2. In other words, for a quick estimate o

whom to target, either method will capture a large amount of the intend

households.So, for our purposes, we will stick to the nominal poverty line

measure of poverty, though we will give corresponding FPL figures whe

ever necessary. Also, all our analysis is done using the 1993-94 NSS

consumption survey data, which is the latest largesample currently av

able.

What is the minimum amount of direct transfer needed to drive

poverty incidence down to zero in these regions? Table-3gives the tota

amount of money the poor households in these regions need to become

poor. We calculate this for both the poverty line and food deprivation

measures of poverty. In 1993-94, a total of Rs 7,691 crore was needed t

eradicate close to 33 per cent of the poverty. Interestingly, during that y

the government, in the name of helping the poor, spent Rs 12,864 crore

different (explicit) subsidies1.6 per cent of the GDP. If we assume tha

there has been no change in the all India HCR since 1993-94 and we ad

our expenditure figure for inflation between 1993-94 and 1998-99, the

expenditure needed in 1998-99 turns out to be Rs 10,888 crore. This is l

than half of the budgeted subsidy bill in 1998-99 and a mere 0.6 per ce

the GDP in that year. Even if poverty was stagnant between 1993-94 an

1998 99 as the World Bank would want us to believe the cost of remo


Food Poverty and Expenditure PovertyFood Poverty and Expenditure PovertyFood Poverty and Expenditure PovertyFood Poverty and Expenditure PovertyFood Poverty and Expenditure Poverty, 1993-94, 1993-94, 1993-94, 1993-94, 1993-94

Unit: Figures represent percentage of households.

BPL APL

FPL = 0.81*PLFPL = 0.81*PLFPL = 0.81*PLFPL = 0.81*PLFPL = 0.81*PL

BFPL 41.37 16.81

AFPL 0.45 41.36


BFPL 39.69 10.99

AFPL 2.14 47.19


BFPL 36.69 7.18

AFPL 5.13 51.00

Note: Abbreviations are as follows:

PL: poverty line;

BPL: below poverty line;

APL: above poverty line;

BFPL: below food poverty line;

AFPL: above food poverty line.

The cost of

removing poverty

here and now does

not require too

much resource.


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the food deprivation calculations, the total requirement in 1998-99 is

Rs 13,186 crore, 0.7 per cent of GDP in that year.

In terms of food deprivation, this group of 8 dominate other

regions. Together, they contribute 30.46 per cent of the all India food-HCR

(calculated as the proportion of persons whose food expenditure is below

0.81 times the poverty line). So, any policy action in these regions will

affect close to one-third of all poor people, by whichever criterion one uses.The added advantage, in terms of policy, is that, they are contiguous. This

will make it that much easier for policy makers to move resources and

personnel, through economies of scale in transportation, etc. Consequently,

it is possible for the government to create a Big Bangin poverty reduction!

There is one thing we must guard against. Given Indias vastness

and the academics eye for details, we have spent considerable amount of

time and effort in studying various regional aspects of poverty. Unfortu-

nately, this has facilitated the politicisation of the problem as every group

can fall back on the output of a serious researcher to highlight a particular

aspect of the poverty profile. This means that any broadly defined, and

hence easier to implement, poverty alleviation programme becomes

burdened with special provisions to take into account the particular charac-

teristics of a particular set of people in particular regions.

To give a simple example, consider the following. The Integrated

Rural Development Programme (IRDP) is a programme specifically

targeted at the rural poor. However, in its implementation there is a clause

that says a minimum proportion of the funding in each region must target

poor women. A statistic that is often used by many is that put out by some

international organisations This says that the sections most vulnerable to


Expenditure Needed for Poverty Reduction: 1993-94Expenditure Needed for Poverty Reduction: 1993-94Expenditure Needed for Poverty Reduction: 1993-94Expenditure Needed for Poverty Reduction: 1993-94Expenditure Needed for Poverty Reduction: 1993-94

Unit: Rs Million for Poverty/Food GapUnit: Rs Million for Poverty/Food GapUnit: Rs Million for Poverty/Food GapUnit: Rs Million for Poverty/Food GapUnit: Rs Million for Poverty/Food Gap

State Region Name Region Poverty Gap Food Gap

Number

Rs in Million

Bihar SouthernSouthernSouthernSouthernSouthern 5151515151 742.20742.20742.20742.20742.20 739.48739.48739.48739.48739.48

Northern 52 1,258.19 1,316.66

Central 53 807.78 845.39

Uttar Pradesh WWWWWesternesternesternesternestern 252252252252252 708.56708.56708.56708.56708.56 1,246.831,246.831,246.831,246.831,246.83

Central 253 626.27 866.02

Eastern 254 1,179.07 1,510.14

West Bengal Eastern PlainsEastern PlainsEastern PlainsEastern PlainsEastern Plains 262262262262262 582.52582.52582.52582.52582.52 611.19611.19611.19611.19611.19

Central Plains 263 504.52 626.47

TTTTTotal Expenditureotal Expenditureotal Expenditureotal Expenditureotal Expenditure 6,409.136,409.136,409.136,409.136,409.13 7,762.187,762.187,762.187,762.187,762.18

Note: (i) Food PL has been defined as 0.81*PL(ii) Figures represent monthly expenditures

Consequently, it

possible for t

government

create a Big Bang

poverty reductio


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India, poverty is a household characteristic. There is very little data tha

allows us to consistently argue that women are poorer than men. Yet, th

IRDP has a special provision for women.

This is not to say that there is no gender bias against women.

Indeed, in a later section we will address this issue. But, as we will dem

strate, poverty and gender bias operate in different ways. More impor-

tantly, gender bias is quite independent of the poverty status. Poverty is

major issue by itself. The best way to tackle it is to attack it directly an

not use programmes specifically designed for poverty alleviation as a

panacea for all socio-economic ills.

III. Employment and PovertyIII. Employment and PovertyIII. Employment and PovertyIII. Employment and PovertyIII. Employment and Poverty

In the earlier section we showed how at least 30 per cent of all

poor could be targeted. From now on, we will look at the problem mor

generally and, concentrate on some of the characteristics of the Indian

poor. Most of the issues touched upon in the rest of this paper have an

already existing literature. However, to the best of my knowledge, thesare not as exhaustive, and systematic, as what is described below.

The classical definition of poverty concerns the inability of a

person to achieve certain minimum basic level of consumption. The ab

to consume, in a market economy, depends on the nominal expenditure

the commodity prices. The level of expenditure depends on the purchas

power, which, to a large extent depends on the income earned. Incomes

earned if jobs are held and, hence, the relationship between employmen

and the incidence of poverty.

It is not surprising that, in the presence of insufficient subsidies

low levels of wealth, unemployment will be correlated with high degreepoverty. However, employment alone may not guarantee a non-poor sta

This is because wages or incomes earned in jobs may not be sufficient t

buy the minimum consumption basket. It is important, as a policy matt

to know whether poverty is a result of a lack of employment opportunit

or due to low wages. If the former, i.e., all employed persons get enoug

wages to stay above the poverty line but not all persons are employed,

requisite approach is one of employment generating policies. If, on the

other hand, people are employed but have very low productivity (and,

hence, earn low incomes), then the policy prescription is one of increas

the productivity of labour.

We want to investigate whether the employment status can be u

to characterise the poor. In India, the actual poverty calculation is done

follows. The consumption of the entire household is obtained and divide

by the household size. This gives the per capita consumption in the hou

hold. If this is below the given poverty line, then the entire household is

termed poor. Poverty is, thus, a household characteristic. It is probably

more precise to talk about poor households rather than poor persons.

Employment characteristics, on the other hand, are surveyed for each a

every member of the NSS household In other words unlike the poverty

Employment alone

may not guarantee a

non-poor status.

This is because

wages or incomes

earned in jobs may

not be sufficient to

buy the minimum

consumption

basket.


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ask questions of the following type: Is the unemployed more likely to be in

a poor household?

If one looks at the employment trends and their characteristics, one

sees a fair degree of stability in the patterns, over the last three decades.

Given that poverty in India continues to be large, one is forced to conclude

that employment related programs have not been very significant in

reducing poverty. This is not to say that employment programs do not

reduce poverty in the short run or, during calamities like drought and

flood. However, for massive reductions in poverty one needs major struc-

tural changes in employment at both the macro and micro levels. At the

macro level, the role of agriculture in employment generation has to give

way to a larger degree of importance of the manufacturing and the services

sector. At a micro level, labour productivity has to increase significantly,

regardless of the sector in which they are employed.

The first major observation from an analysis of past trends is the

relatively low incidence of open unemployment, especially among the poor

(Table-4). This is independent of the method of estimation, of which theNSS uses threeusual status, current weekly status and current daily

status. The general pattern in these three measures is that, the unemploy-

ment rates by the usual status is lower than that by the current weekly

status and the latter rate is lower than that by the daily status (excepting

for urban females in 1977-78). This suggests that some people who are

otherwise employed, or out of the labour force, get classified as unem-


Different Measures of UnemploymentDifferent Measures of UnemploymentDifferent Measures of UnemploymentDifferent Measures of UnemploymentDifferent Measures of Unemployment

Unit: per centUnit: per centUnit: per centUnit: per centUnit: per cent

Year All India Rural Urban

Female Male Person Female Male Person Female Male Person

Usual Status

1972-731972-731972-731972-731972-73 1.01.01.01.01.0 1.91.91.91.91.9 1.61.61.61.61.6 0.50.50.50.50.5 1.21.21.21.21.2 0.90.90.90.90.9 6.06.06.06.06.0 4.84.84.84.84.8 5.15.15.15.15.1

1977-781977-781977-781977-781977-78 3.33.33.33.33.3 2.22.22.22.22.2 2.62.62.62.62.6 2.02.02.02.02.0 1.31.31.31.31.3 1.51.51.51.51.5 12.412.412.412.412.4 5.45.45.45.45.4 7.17.17.17.17.1

19831983198319831983 1.21.21.21.21.2 2.32.32.32.32.3 1.91.91.91.91.9 0.70.70.70.70.7 1.41.41.41.41.4 1.11.11.11.11.1 4.94.94.94.94.9 5.15.15.15.15.1 5.05.05.05.05.0

1987-881987-881987-881987-881987-88 2.92.92.92.92.9 2.62.62.62.62.6 2.72.72.72.72.7 2.42.42.42.42.4 1.81.81.81.81.8 2.02.02.02.02.0 6.26.26.26.26.2 5.25.25.25.25.2 5.45.45.45.45.4

1993-941993-941993-941993-941993-94 1.41.41.41.41.4 2.22.22.22.22.2 1.91.91.91.91.9 0.80.80.80.80.8 1.41.41.41.41.4 1.11.11.11.11.1 6.26.26.26.26.2 4.04.04.04.04.0 4.44.44.44.44.4

Weekly Status1972-731972-731972-731972-731972-73 5.95.95.95.95.9 3.73.73.73.73.7 4.34.34.34.34.3 5.55.55.55.55.5 3.03.03.03.03.0 3.93.93.93.93.9 9.29.29.29.29.2 6.06.06.06.06.0 6.66.66.66.66.6

1977-781977-781977-781977-781977-78 5.05.05.05.05.0 4.44.44.44.44.4 4.54.54.54.54.5 4.04.04.04.04.0 3.63.63.63.63.6 3.73.73.73.73.7 10.910.910.910.910.9 7.17.17.17.17.1 7.87.87.87.87.8

19831983198319831983 4.84.84.84.84.8 4.44.44.44.44.4 4.54.54.54.54.5 4.34.34.34.34.3 3.73.73.73.73.7 3.93.93.93.93.9 7.57.57.57.57.5 6.76.76.76.76.7 6.86.86.86.86.8

1987-881987-881987-881987-881987-88 5.05.05.05.05.0 4.84.84.84.84.8 4.84.84.84.84.8 4.34.34.34.34.3 4.24.24.24.24.2 4.24.24.24.24.2 9.29.29.29.29.2 6.66.66.66.66.6 7.07.07.07.07.0

1993-941993-941993-941993-941993-94 3.83.83.83.83.8 3.53.53.53.53.5 3.63.63.63.63.6 3.03.03.03.03.0 3.03.03.03.03.0 3.03.03.03.03.0 8.48.48.48.48.4 5.25.25.25.25.2 5.85.85.85.85.8

Daily Status

1972-731972-731972-731972-731972-73 11.511.511.511.511.5 7.07.07.07.07.0 8.38.38.38.38.3 11.211.211.211.211.2 6.86.86.86.86.8 8.28.28.28.28.2 13.713.713.713.713.7 8.08.08.08.08.0 9.09.09.09.09.0

1977-78 10.0 7.6 8.2 9.2 7.1 7.7 14.5 9.4 10.3

1983 9.3 8.0 8.3 9.0 7.5 7.9 11.0 9.2 9.6

1987-88 7.5 5.6 6.1 6.7 4.6 5.2 12.0 8.8 9.4

1993-94 6.3 5.9 6.0 5.6 5.6 5.6 10.5 6.7 7.4

The first maj

observation from

analysis of pa

trends is t

relatively lo

incidence of op

unemploymen

especially amo

the poo


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ployed by the two current status measures. Of course, it is possible that

current status measures pick up those who are not actively seeking wor

because they feel that they will not get any regular employment (Visari

and Minhas, 1991).

The literature on employment issues is huge. However, not all

them use data from the same source, and hence, do not use similar sam

pling methodologies. Consequently, comparing the findings from all the

various studies could result in inconsistent analyses. We will, therefore,

concentrate only on the NSS employment surveys, since then, one has

comparable data sets over the years. Even within the NSS surveys, we

stick to the large sample data, an exercise conducted every five years, a

ignore the data from the thin samples carried out every year.

Gangopadhyay and Wadhwa (1999) studied the relationship

between employment and poverty in India. Their first major finding wa

that the poor cannot afford to be unemployed, i.e., most of the poor are

already employed. This is true in both the rural and the urban sectors.

the other hand, much of the unemployment is in the non-poor householdOne way of explaining this apparent anomaly is to remember that it is

work, but work with sufficient pay that alleviates poverty.

To check this, we did a simple analysis on the data. We conside

individuals who are employed and yet are either engaged in a subsidia

activity and/or seeking additional work. These we termed as the undere

ployed. Given our definition, rural underemployment is higher than urb

underemployment across all employment groups. In the rural sector,

underemployment is as high as 58 per cent for casual labourers. The

corresponding figure for the urban sector is 30 per cent. Importantly, on

we allow for underemployment (according to our definition), one gets apositive relationship between underemployment and poverty.

This could be explained by the following story. The poor try to

earn as much as they can. However, they get the low paying jobs and

hence, to get their minimum consumption basket, they have to work at

more than one job. This suggests that employment creation is not suffi-

cientone needs high productivity jobs. This would imply that alleviat

of poverty requires a better trained/skilled work force. Indeed, in our

empirical analysis we find that basic education and technical or other

vocational skills reduces the incidence of poverty across both the sector

There is a huge gap in human capital acquisition between the p

and the non-poor. Therefore, if the poor are poor because of low produ

ity and, human capital leads to higher levels of productivity, then the o

way out is to educate and train the poor. The answer, however, does no

in higher education. In fact, a large proportion of the unemployed has

graduate or higher degrees, which raises serious issues about the qualit

our higher education programs. Instead, what is needed is basic formal

education along with some vocational training.

Before finishing this section, we will briefly touch upon an issu

that has become very important these days child labour What we hav

It is not work, but

work with sufficient

pay that alleviates

poverty.


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more than 15 years old. What about the workers in the age group of 5 to

14 years?

Gangopadhyay and Wadhwa (1999) looked at the determinants of

child labour. The first major finding was that while economic status is a

significant determinant of the incidence of child labour, the poverty status

is not. In other words, there is a threshold of per capita household con-

sumption below which child labour is more prevalent, but this threshold

does not coincide with the poverty line, in either of the two sectors. This

clearly suggests a difference in perception, in what constitutes economic

distress, between researchers and decision-making households. However, in

both sectors the threshold is lower for girls. We have only considered

economic activities outside household chores, consistent with the NSS

definition of work. If we allow for household chores the incidence of child

labour for girls increases.

The second major finding was that the education of parents

significantly lowers the incidence of child labour. Moreover, the mothers

education continues to have a significant impact even when controlling forthe fathers education. In general, parents will send their children to work

as a last resort. This is consistent with the hypothesis that parents are

altruistic towards their children.

A common thread in the analysis of adult and child labour is the

importance of human capital, though they work in different ways for the

two groups. Human capital, either in the form of formal education or

vocational skills, reduces the incidence of poverty, by increasing the

income earning capabilities of the educated adults in the household. For

children, it is the educational qualifications of the parents, which acts as a

deterrent to child labour. Being educated, parents earn enough not to sendtheir children to work or, they are more aware of the positive effects of

education on a childs future welfare.

IV.IV.IV.IV.IV. GenGenGenGenGender Biasder Biasder Biasder Biasder Bias

Since poverty is a household characteristic, and the NSS does not

give the individual consumption of household members, it is difficult to

directly calculate the gender bias in the incidence of poverty. However, the

gender of the head of the household is available in the survey data. It must

be stated that in the Indian literature, the head of the household has always

been taken as a mere reference point. In Dubey, Gangopadhyay and

Wadhwa (1999), we look closely at this hypothesis and find no evidence in

support of it. If the head is someone with income earning responsibility, or

holds decision-making powers within the household, then the gender of the

head can be used as a determinant of gender bias, if any.

Gender bias can operate in (one or both of) two different ways.

First, women may be discriminated against in the workplace; discriminat-

ing employers may prefer males to female applicants. Alternatively,

women may not be hired in well-paying jobs, not because the employer

discriminates against them but because they are not found suitable for such

If women are le

skilled than male

then t

responsibility f

this kind

discrimination li

within t

household, whe

the parents train,

educate, the b

child more than t

girl chi


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skilled than males. This will get reflected in lower incomes among fema

If women are less skilled than males, then the responsibility for

kind of discrimination lies within the household, where the parents trai

educate, the boy child more than the girl child. While less schooling me

less of human capital, there is another reason why females may earn les

income. Females may also own less of income generating physical cap

For urban areas, the NSS does not give data on the household ownersh

physical assets. In rural areas, however, the data reports the amount of

cultivable land owned by each household. Cultivable land is obviously

of the most important income generating assets in rural India.

At first glance, it was difficult to find any significant difference

the incidence of poverty among female headed households (FHHs) and

male headed households (MHHs) in the rural sector, though there was a

significant difference in the urban sector. However, when we dropped th

FHHs where the heads were currently married, the poverty incidence o

remaining FHHs was significantly different from that of the MHHs, in

both rural and urban sectors. The reason for dropping the currently maried female headed households was a simple one, and consistent with th

survey method followed by the NSS. In the male dominated Indian soc

it is difficult to comprehend a situation where a female member is the h

of the household in the presence of an adult male. For the currently ma

female heads, it is fair to assume that the husband is away from the

household, say as a migrant labourer. This hypothesis is consistent with

definition of an NSS householdthose who share a common kitchen. T

while a migrant male labourer will not be counted as a member of the

household, his income will augment the purchasing power of the house

hold.Once we do this, we observe a marked difference in the inciden

of poverty between MHHs and FHHs where the head is not currently

married. This suggests that there is a gender bias. We then looked at ho

the bias works, given the two possible routes I have referred to above. A

direct empirical test of these gender issues will be to compare the incom

earned by similarly trained males and females. However, data on incom

are hard to come by in India. Also, poverty is calculated using actual

consumption. Hence, we tested whether female headed households were

more vulnerable to poverty than male headed ones, and how much of t

difference could be explained by female education and land holding.

Our major conclusions are the following. Households headed b

widows or divorced/separated women are the most vulnerable to pover

In the urban sector, this differential poverty incidence can be partially

explained by the fact that female heads are less educated than the male

household heads. In the rural sector, in addition to this educational diffe

ence, FHHs also have smaller land holdings. This also contributes to a

higher poverty incidence in rural FHHs. However, even after controllin

for these factors, FHHs remain more vulnerable to poverty as compare

MHHs This implies a clear gender bias in the incidence of poverty


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V. Urban PovertyV. Urban PovertyV. Urban PovertyV. Urban PovertyV. Urban Poverty

I will deliberately not deal with rural poverty. This is because there

is a rich literature that does precisely this. Instead, I will touch upon urban

poverty, as the literature here is far smaller (Pernia, 1994; Vashishtha,

1993; Nath, 1994). Studies on urban poverty are of two types. One group

is based on surveys of slums in particular metropolitan cities. The other

group uses the NSS surveys to study urban poverty. The literature is

lacking in two respects. In the first group of studies, all slum dwellers are

assumed to be poor and poverty lines are not used to determine the poor.

Couple to this the fact that different researchers conducted these surveys in

different cities. These studies do not follow a common methodology and

are non-comparable, across cities and over time. A time profile of urban

poverty can be obtained from the NSS surveys as they follow more or less

the same methodology in each survey. However, these studies look at urban

India as a whole, clubbing together cities of various types. They implicitly

assume that the characteristics of urban poverty are uniform across differ-

ent town sizes.The urban system in India consists of various sizes of towns and

cities. The smallest towns have less than 10,000 people, while a metropoli-

tan centre like Delhi, or Mumbai has more than 10 million people. Empiri-

cal evidence suggests that, as the city size increases, the productivity of

factors increases (Sviekauskas, 1975). Towns of larger size have higher

concentrations of population, allowing greater specialisation and econo-

mies of scope, compared to smaller towns. Moreover, small towns may

function as local area markets, while the larger ones could be the seats of

local or federal governments. Consequently, the structure of employment

and earnings are different in different sized towns. An aggregate measure ofurban poverty may, therefore, hide more than it reveals.

Dubey, Gangopadhyay and Wadhwa (2001), studied the character-

istics and determinants of poverty in different sized towns. They demon-

strate that smaller towns have higher levels of poverty. One hypothesis

could be that larger cities have more educated labour, which has greater

productivity and, hence, are less poor. Indeed, education plays a positive

role in reducing poverty, regardless of the city size.

However, while it is true that larger cities have higher educational

levels, this factor by itself does not explain the differing poverty incidence

in towns of different sizes. One explanation could be that larger cities tend

to have better social and economic infrastructure. While the economic

infrastructure may affect poverty incidence through greater income earning

potentials, the social infrastructure may directly help in reducing poverty

by allowing greater access to poverty reducing transfers.

VI. ConclusionVI. ConclusionVI. ConclusionVI. ConclusionVI. Conclusion

There are two important lessons one learns from the brief observa-

tions made here. First, a massive reduction in poverty is not a difficult issue

in the short run It is easy to identify a third of the Indian poor by their

While it is true th

larger cities ha

higher education

levels, this factor

itself does n

explain the differi

poverty incidence

towns of differe

size


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OCT.DEC. 2000

line are meagre compared to the resources we apparently spend on them

Also, it is unimportant how we measure povertythe same people turn

as being poor regardless of the methodology for classifying them as suc

The second observation is that, in all the issues related to pove

the importance of human capital cannot be over-stressed. India, in spite

its lofty egalitarian goals, has singularly failed to educate its people. B

gender bias, child labour or low adult productivity, the lack of training

stands out as a major determinant of these ills.

The time has therefore come to stop discussing irrelevant issues

like reforms, growth and poverty. Government policy has got to concen

trate on providing basic social infrastructurethe job they are expected

do. Everything else is a waste of time, and most importantly, of resourc

ReferencesReferencesReferencesReferencesReferences

Dubey, Amaresh and Shubhashis Gangopadhyay (1998): Counting the Poor: Where

the Poor in India?, Central Statistical Organisation, Delhi.

Dubey, Amaresh, Shubhashis Gangopadhyay and Wilima Wadhwa (1999): Female

Headed Households In India: Incidence, Poverty And Socioeconomic Chateristics, mimeo.

(2001): Occupational Structure and Incidence of Poverty in Indian Town

Different Sizes, Review of Development Economics5, 49-59.

Gangopadhyay, Shubhashis, L.R. Jain and Amaresh Dubey (1997): Poverty Measu

and Socioeconomic Characteristics: 1987-88 and 1993-94, Central Statis

Organisation Report, Delhi.

Gangopadhyay, Shubhashis and Wilima Wadhwa (1999): Employment and Poverty

ILO, Delhi.

Minhas, B.S., L.R. Jain, S.M. Kansal and M.R. Saluja (1988): Measurement of Ge

Cost Of Living for Urban India, All-India and Different States, Sarveksh

12, 1-23.Nath, V. (1994): Poverty in Metropolitan Cities of India, in Ashok K. Dutt, Frank

J. Costa, Surinder Aggarwal and Allen G. Noble (ed.), The Asian City: Pro

of Development, Characteristics and Planning, Kluwer Academic Press,

Dordrecht.

Pernia, E.M. (ed.): (1994), Urban Poverty in Asia: A Survey of Critical Issues, Asian

Development Bank, Manila.

Sveikauskas, Leo (1975): The Productivity of Cities, Quarterly Journal of Econom

89, 393-413.

Vashishtha, P.S. (1993): Regional Variation in Urban Poverty in India, Margin26

483-524.

Visaria, P. (1996): Structure of the Indian Workforce, 1961-1994, Indian Journal

Labour Economics39, 725-40.

Visaria, P. and B.S. Minhas (1991), Evolving an Employment Policy for the 1990s:

What Do the Data Tell Us?, Economic and Political Weekly26, 969-79.


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