POVERTY AND CALORIE DEPRIVATION ACROSS SOCIO-ECONOMIC GROUPS IN RURAL INDIA: A DISAGGREGATED ANALYSIS

Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 15

POVERTY AND CALORIE DEPRIVATION ACROSS SOCIO-ECONOMIC

GROUPS IN RURAL INDIA: A DISAGGREGATED ANALYSIS

Abha Gupta1 & Deepak K. Mishra2

This paper examines the linkages between calorie deprivation and poverty in rural India at a

disaggregated level. It aims to explore the trends and pattern in levels of nutrient intake across

social and economic groups. A spatial analysis at the state and NSS-region level unravels the

spatial distribution of calorie deprivation in rural India. The gap between incidence of poverty

and calorie deprivation has also been investigated. The paper also estimates the factors

influencing calorie deprivation in rural India. The study point out that nutritional deprivation is

high among marginalized social groups and regions. It is the poor, scheduled castes, scheduled

tribes, illiterate people, agricultural labourers and Muslims who are more likely to be calorie

deprived.

INTRODUCTION

Notwithstanding India’s relatively robust economic performance since the economic reforms in

early 1990’s, significant deficits in human development parameters, most notably in health and

nutrition standards, remain a cause of concern. India has the largest number of under-nourished

children in the world. Not only that prevalence of child under-nutrition in India (43 percent) much

higher than the world average (25 percent), its performance is worse than some of the poorest

economies of the world (World Food Programme 2009).This prevalence is even higher among

some socio-economic groups and regions. One of the WHO’s millennium development goal is to

reduce the number of stunted, wasted and underweight children by 2015. Only few years are left to

achieve this goal but in India still 38.4 percent children under the age of 3 are stunted, 19.1 percent

are wasted and 46 percent children are underweight (National Family Health Survey 2005-06).

There has been a sluggish decline in this percentage over a decade but this decline is unimpressive

when compared across states and different socio economic groups. Besides poor performance in

terms of some anthropometric measures, average per capita per day calorie and protein intake is

also showing a declining trend in the post economic reforms period. Consumption and expenditure

on cereal food items, which are a good source of energy has recorded a decline whereas other food

items (vegetables, fruits, meat/egg/fish, oil, milk) have shown a slightly increasing share in the

diet of the population. However, decline in calories is not seen as deterioration of health by some

researchers rather it is viewed as a sign of improvement resulted by an increase in income,

development of rural infrastructure, mechanization, urbanization, improvement in health and

change in taste and preferences (Deaton and Dreze 2009, 2010; Verma et al. 2008; Rao 2000).

Another group of scholars, however, links this with the increasing deterioration in health and

1 Research Scholar, Centre for the Study of Regional Development, School of Social Sciences, Jawaharlal

Nehru University, New Delhi-110067. E-mail: [email protected]

2 Associate Professor, Centre for the Study of Regional Development, School of Social Sciences, Jawaharlal

Nehru University, New Delhi-110067. E-mail: [email protected].

JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 16

nutrition standards of the population (Patnaik 2004, 2007, 2010; Nasurudeen et al. 2006; Ray

2005:10; Mehta and Venkatraman 2000; Shariff and Mallick 1999; Mehta 1982).

India’s growth ‘turn around’ has not resulted in remarkable improvements in health and nutrition

outcomes, and it has raised questions on the inclusiveness of the growth process (Radhakrishna et

al. 2004). The high level of undernourishment among children (46 percent, National Family

Health survey 2005), the relatively high infant mortality rate (47/000 live births, Sample

Registration System 2010) and signs of distress among marginalized sections of the society in a

country which has witnessed remarkable growth in recent decades has been a widely discussed

issue (Dubey and Thorat 2012; Reddy and Mishra 2010). However, India’s poverty measured in

terms of head count ratio, which is a measure based on minimum calorie norm, has seen consistent

decline during this period of growth. This evidence of declining poverty is not accepted by all and

it remains a contested question (Deaton and Dreze 2009, 2010; Patnaik 2007, 2010)1. The rising

gap between official head-count ratio and share of population having less than minimum calorie

intake that formed the basis of official poverty line has been a matter of wide public concern and

debate (Dev 2005; Sen 2005; Jones and Sen 2001). This debate surrounds over the method of

poverty measurement and the focus has been on whether the official poverty line is adequate to

account for rising expenditure on health and education, which, until recently, were being provided

by the state. Most of the studies on poverty deal with the level of rural and urban poverty at the all

India and state level. This paper attempts to unravel these issues at a more disaggregated level- at

the level of NSS (National Sample Survey) regions and also in terms of various socio-economic

groups.

The broad objectives of this paper are outlined as follows:

1) To examine changes in consumption of different food items in order to explain changes in

nutrition level.

2) To estimate changes in level of nutrients and deficiency of different nutrients from the

recommended dietary allowances (RDA) at disaggregated level and to show the gaps

between levels of poverty and levels of nutrition deficiency.

3) To estimate probability of being calorie deprived at disaggregated level using binary

logistic regression analysis.

From the policy perspective, the results of this paper have important implications for both the

methodology of poverty measurement and also for providing nutrition security to the vulnerable

sections of the population.

DATA AND METHODS

Data for this paper are obtained from National Sample Survey (NSS), 50th (1993-94), 61st (2004-

05) and 66th (2009-10) Consumer Expenditure Schedules. These rounds of the survey, by the NSS

are large scale sample surveys and provide information on consumer expenditure quinquennially

as part of its “rounds”. Consumer expenditure survey gives information on quantity and value of

different goods in a household with a reference period of last 30 days for each state/UT, all India

and separately for rural and urban areas. Among these goods, information on 142 items of food are

collected which can be converted into nutrition values2.


In this paper, average per capita per day 2400 kcal has been used to show calorie deprivation

which is also used by Planning Commission to indirectly estimate head-count ratio for rural areas3.

For converting monthly household food consumption into per capita monthly consumption,

monthly household consumption is divided by household size. To get the per capita per day

consumption, per capita monthly consumption is divided by number 30. In order to show

probability of being calorie deprived across socio-economic and demographic groups, a logit

model has been fitted which is

� =1

1 + ��

P = 1/1+e-z..……………. (1)

Where P is the estimated probability, z is the predictor variable and e is the base of natural

logarithm with a value of 2.7183. After simplification, we get

Log z = P/1-P…………… (2)

Where (P/1-P) is called odds and log (P/1-P) is called log odds or the logit of P. Thus, equation

(2) becomes

logit P = Z…………….. (3)

The multivariate logistic function involves ‘n’ predictor variables which is represented by

P = (1/1+e-b0 + b1

x1+

b2x2 +……… bn

xn) ………… (4)

Or, logit P = (bo + b1x1 + b2x2 +…… bnxn)…………. (5)

The coefficients b1 represents the additive effect of one unit change in the predictor variable x1 on

the log odds of the response variable. Whereas one unit increase in the x1, holding other predictor

variable constant, multiplies the odd by the factor eb1. For this reason the quantity eb

1 called the

odd ratio.

RESULTS AND DISCUSSION

Trends in Food Consumption in Rural India

Food is one of the basic needs for human survival. The variety of food that we consume

determines our nutrition behaviour in terms of calorie, protein, fat and other micronutrients. In

rural India, cereals have been the main constituents in people’s diet. Among cereals, rice recorded

an important share in total cereal consumption followed by wheat, coarse cereals, vegetables, milk

and fruits (Table 1).

During 1994-2005 the biggest decline was experienced by cereal consumption. This decline was

caused by fall particularly in coarse cereal consumption followed by rice and wheat consumption.

Pulse and milk consumption declined slightly. As far as change in consumption of ‘other food

items’ (vegetables, fruits, meat and edible oil) were concerned, highest increase was found in

vegetable consumption. Other food items recorded a slight increase in their consumption. A recent

round of NSS (66th Consumer Expenditure Survey, 2009-10) shows that cereals still hold the

highest place among all food items mainly because of higher rice consumption. However, cereal

consumption still continues to decline but the decline has been lesser during 2005-10 compared to

a decline during 1994-05. The consumption of wheat, rice and coarse cereals shows a marginal

decline. As far as consumption of ‘other food items’ (Vegetables, fruits, meat and edible oil) is

concerned, a marginal increase is seen in the consumption of these food items. From the analysis


above, it can be argued that last 15 years, often referred to as the ‘post economic reform period’,

rural India experienced a sharp decline in cereal consumption particularly coarse cereals, although

the precise linkages between economic reforms and calories deprivation needs to be examined

further. However, in recent five years (2005-2010) this decline has been minimal. The

consumption of other food items has been slightly increasing over the years but this increase is not

compensated by decline in cereals, as a result of which calorie and protein intakes are falling.

Table 1 Food Consumption Pattern and its Change in Rural India: 1994-2010

(Monthly Per Capita in kg*)

Food Items Year

Kg Change (1994-2005)

Kg Change (2005-2010) 1993-94 2004-05 2009-10

Cereal 13.40 12.11 11.35 -1.29 -0.76

Wheat 4.32 4.19 4.34 -0.13 0.15

Rice 6.79 6.38 6.13 -0.41 -0.25

Coarse cereal 1.97 1.27 0.87 -0.70 -0.40

Pulses 0.76 0.71 0.66 -0.05 -0.05

Milk Liquid (litres) 3.94 3.87 4.08 -0.07 0.21

Vegetable 4.75 5.25 4.58 0.50 -0.67

Fruits 0.22 0.30 0.21 0.08 -0.09

Fruits (nos.) 2.71 2.84 2.66 0.13 -0.18

Meat 0.12 0.14 0.14 0.01 0.00

Egg (nos.) 0.64 1.01 0.95 0.37 -0.06

Fish 0.18 0.20 0.21 0.02 0.01

Edible Oil (litres) 0.37 0.48 0.56 0.11 0.08

Source: Authors' calculation from NSS 50th, 61st and 66th Consumer Expenditure Schedule.

Note: unit in kg unless otherwise specified in brackets after the food-item.

Change in Nutrient share of various Food Items and level of Poverty in Rural India

It is believed that food consumption in India has changed much which has caused overall decline

in calories. There are various factors which affect consumption of food items such as production,

availability and prices, lower level of unemployment, rise in per capita expenditure, change in

taste, climate, decline in physical activity, improvement in health status, urbanization, increased

awareness among consumers about food nutrients, access to safe drinking water, health care and

environmental hygiene for effective conversion of food into energy (Kumar et al. 2007; WHO

2003; Bansil 2003; Viswanathan 2001; Martorell and Ho 1984). A group of scholars considers this

decline in calories as a positive and anticipated development and for them this decline is not a

matter of serious concern (Radhakrishna 2005; Radhakrishna and Reddy 2004; Rao 2000). On the

other hand, Patnaik (2007) has argued that decline in calories leads to deterioration in health and

poverty and blames Planning Commission for using faulty prices to adjust poverty in India as the

reason for artificially lowering the estimates of poverty. The average per capita per day (PCPD)

calorie consumption declined from 2148 kcal to 2044 kcal between 1993/94 to 2004/05 in rural

India. On an average PCPD intake of protein also recorded a fall from 59.9 gm to 55.1 gm during

the same period (Table 2).


Table 2 Change in share of nutrients from different food items between 1993/94-2004/05

in rural India

Food Groups Average Per capita per day intake of

Calorie (kcal) Average Per capita per day intake of

Protein (gm)

1993-94 2004-05 Calorie Change

1993-94 2004-05 Protein Change

Rice 809 755 -55 17.5 16.3 -1.2 Wheat 500 487 -13 17.7 17.2 -0.4

Coarse cereals 220 140 -80 6.6 4.3 -2.3

Cereals and cereal

substitutes 1530 1382 -147 41.8 37.9 -4.0

Root and Tubers 57 60 3 1.0 1.1 0.1 Sugar and honey 103 98 -5 0.0 0.0 0.0

Pulses, nuts and

oilseeds 106 92 -14 6.5 5.2 -1.3

Vegetables and

fruits 44 53 10 1.9 1.7 -0.2

Meat, eggs and fish 15 16 1 2.2 2.3 0.1

Milk and milk

products 132 131 -1 5.3 5.3 0.0

Oils and fats 115 151 36

Misc. food, food

products and

beverages

47 61 14 1.1 1.5 0.4

Total 2148 2044 -104 59.9 55.1 -4.9

Source: Authors' calculation from NSS 50th and 61st Consumer Expenditure schedule.

As it has already been pointed out a sharp decline in cereal consumption and a slow rise in

consumption of other food items is observed from the analysis of secondary data. Table 2 clearly

shows that calorie decline has been accompanied by a decline in protein intake. The main reason

for this decline is fall in cereal calories particularly coarse cereals and pulse intake. Consumption

of oil and fat contributed in total calories but these food items are lacking in protein and are rich in

fat. As a result, all-India average fat intake has increased (Nutrition Intake, NSS 61st round

report). Besides oil & fat, miscellaneous food and beverages also contributed much in calorie and

protein consumption. Before discussing calorie deprivation and poverty at disaggregated level, it

would be appropriate first to talk about the trends at rural all-India level, which helps in

understanding the general situation of the poverty.

The levels of calorie deprivation and poverty in rural India, as presented in Table 3, shows that

around 72 percent rural population was not getting required calories (per capita per day intake of

2400 Kcal) during 1993-94 and this percent has risen to 80, an increase of 8.4 percentage points in

2004-05, whereas level of poverty has declined if we consider Planning Commission’s estimate

accurate. In 1993-94, the level of poverty was 37 percent which has declined to 28.3 percent in

2004-05. The gap between calorie poverty level and planning commission’s poverty level has

increased from 35 percentage points in 1993-94 to 52 percentage points in 2004-05, a 17.1 points

increase. This mismatch between poverty and calorie intake continues to remain a contested issue

among researchers.


Table 3 Change in Calorie Deprivation and Poverty Level in Rural India

between 1993/94 and 2004/05 Method of estimating poverty

1993-94 2004-05 Change between 1993/94 &

2004/05

Percent Below 2400

Kcal 71.60 80.0 8.40

Percent Below Official

Poverty Line 37 28.3 -8.7

Gap between Calorie

Poverty and Official

Poverty Line

34.6 51.7 17.1

Source: Same as Table 2.

Change in level of Nutrients at disaggregated level

The Planning Commission of India has officially taken recommended calories4 of 2400 Kcal

PCPD for rural and 2100 Kcal PCPD for urban areas in order to estimate poverty5. Besides, 60

gms PCPD protein intake has also been recommended by ICMR for nutrition measurement4. Table

4 presents average PCPD intake of calories and protein and their change over a decade (1993/94-

2004/05) with emphasis on deficit from RDA across various sections of the society. From a

demographic point of view it is found that never married persons consume lower level of calories

and protein than the married persons. In fact, this demographic group also shows highest decline

in nutrition parameters whereas widow/divorced/separated group enjoys relatively better access to

nutrition. Deficiency of calories is highest among never married persons showing 305 kcal

deficiency in 1993/94 which increased to 400 kcal during 2004/05. On the other hand are

widow/divorced/separated group whose calorie deficiency is much lower than other marital

groups. As far as deficiency of protein among marital groups is concerned, it has been higher

among never married persons than married. In rural India, different social classes show distinct

nutrition level from one another.

If we analyze family size, it is found that it is the bigger households who are suffering from lower

level of nutrition. In fact as size of a family increases, deficiency of calories and protein from

recommended tends to rise. Family consisting of 7-8 members showed a higher increase in

deficiency of calories than smaller families. In fact protein intake is quite low in these families.

Small families (1-4 members) tended to show much lower fall of calories and protein than other

family sizes. Similarly lower consumption of nutrients is found among less educated persons and

as education level rises, average calorie and protein intake also increases. Less educated persons

show a major decline in their nutrition level. Protein deficiency was much high in this group. On

the other hand are higher educated people who recorded an addition of 117 kcal in 1993/94 and

lower deficit of 17 kcal during 2004/05. This group added more protein in their diet in both

periods.

As far as religious groups are concerned, deficiency of nutrients is high among Muslims and

Christians. Least deficiency of calorie and protein was shown by ‘other’ religious people as only

223 kcal were lesser than recommendation.


Table 4 Change in level of nutrients and deficiency from recommendation between 1993/94-2004/05

among socio-economic and demographic groups in rural India

Calorie Intake Deficit from

RDA, 2400 Kcal Protein Intake

Deficit from RDA, 60 gm

1993-94

2004-05

1993-94

2004-05

1993-94

2004-05

1993-94

2004-05

Marital Status Never married 2095 2000 305 400 59 54 1 6 Married 2194 2081 206 319 61 56 +1 4 Widowed/divorced/

separated 2236 2129 164 271 61 56 +1 4

Household Size 1-4 2312 2199 88 201 63 57 +3 3 5-6 2088 2005 312 395 58 54 2 6 7-8 2070 1954 330 446 58 54 2 6 Above 8 2091 1955 309 445 60 55 0 5

Education Group Not Literate 2089 1974 311 427 59 54 1 6 Primary or below 2162 2031 238 369 60 55 0 5 Secondary 2332 2184 68 217 64 58 +4 2 Higher 2517 2383 +117 17 70 65 +10 +5

Religious Group Hindu 2159 2048 241 352 60 55 0 5 Muslim 2041 1979 359 421 57 53 3 7 Christian 1989 2075 411 325 52 53 8 7 Others 2307 2177 93 223 69 62 +9 +2

Social Group Scheduled Tribe 1993 1895 407 505 54 49 6 11 Scheduled Caste 2023 1948 377 452 57 53 3 7 Others 2212 2097 188 304 62 57 +2 3

MPCE Groups (Percentile) Lowest 5 1324 1369 1076 1031 38 36 22 24 10 1581 1571 819 829 44 42 16 18 20 1717 1676 683 724 48 45 12 15 30 1846 1796 554 604 51 49 9 11 40 1964 1881 436 519 54 51 6 9 50 2043 1958 357 442 56 52 4 8 60 2150 2038 250 362 60 55 0 5 70 2264 2154 136 246 63 58 +3 2 80 2405 2287 +5 113 67 61 +7 +1 90 2586 2378 +186 22 73 65 +13 +5 95 2798 2570 +398 +170 80 71 +20 +11 Highest 3253 3034 +853 +634 92 82 +32 +22

Poverty Line Below poverty Line 1737 1639 663 762 48 44 12 16 Above Poverty Line 2388 2202 12 198 67 59 +7 1

Occupation Type Self empl in non agr 2076 2042 324 358 57 55 3 5 Agricultural Labour 1923 1849 477 551 52 48 8 12 Other Labour 1958 1892 442 508 54 51 6 9 Self empl in agri 2347 2181 53 220 67 60 +7 0 Others 2233 2169 167 231 62 58 +2 2


Source: Same as Table 2 Notes: Intakes are Average per capita per day in Kcal and metric Gram

respectively.

This religious group, on an average, added average 2 gm protein in their diet. In rural India,

Scheduled tribes (ST) and Scheduled castes (SC) are worst affected as both social groups show

lower intake of calorie as well as protein and also higher decline in nutrient intake compared to

other class people. The worst affected are the ST people who recorded highest level of calorie and

protein deficiency followed by SC in 2004-05. Calorie and protein deficiency had been lower

among 'other' social groups.

In terms of expenditure classes, it is found that it is the higher consumption expenditure groups

who are consuming sufficient calories and protein. The bottom classes suffer badly from lower

nutrient intake as well as its sharp decline. As consumption expenditure level rises, there is more

probability of consuming sufficient calories and protein. The top 20 percent showed higher intake

of calorie and protein and bottom 30 percent experienced as much as more than 500 kcal and 11

gm calorie and protein deficiency respectively during 2004/05. In terms of occupation groups in

rural areas, it is found that it is the agricultural labourers and ‘other’ labourers among which

calorie and protein intake is quite low and in fact these occupation groups also show a sharp

decline in nutrient intake over a decade. Agricultural labour and ‘other’ labourers are worst

affected occupation groups as both these groups had been unable to consume recommended intake

of calories and protein. The deficiency in the level of nutrients is much higher among agricultural

labour followed by ‘other’ labourers during 2004/05. Self employed in agriculture enjoyed better

level of nutrient intake as deficiency of calorie and protein was quite low in the same period.

Thus, from the above discussion it is found that there is significant relation between lower nutrient

consumption and socio-economic marginalization and deprivation. Never married persons, less

educated, lower Monthly per capita expenditure (MPCE) classes, SC, ST, Muslims, Agriculture

and ‘other’ labourers, big households are those sections of the society where nutrient intake is

quite low and at the same time decline in nutrient intake is considerably high among these groups.

Thus, the disaggregated picture of nutrition deficiency does not fit well with the argument that the

observed decline in calorie intake could be attributed to the diversity in the food basket of the

people as result of broader changes associated with economic development.

Level of Calorie Deprivation and Poverty

For reasons discussed above, methods of poverty estimation have been a widely discussed issue.

Even though the poverty line ensured the consumption of the normative calorie intake in 1973-74,

the rupee value of the poverty line at current prices is not sufficient for meeting the normative

requirements after other essential expenditures are taken into account (Sen 2005). As against this,

some scholars most notably Patnaik, have argued in favour of a ‘nutrition-invariant’ or ‘direct’

poverty estimate, by calculating the number of people not consuming the recommended daily

calorie intake. Some studies criticize direct method of poverty measurement through calorie and

deprivation (Deaton and Dreze, 2009; Verma et al. 2008; Dev 2005; Sen 2005; Rao, 2000). They

have highlighted the absurd results that it throws up when state level poverty estimates are carried

out. While the calorie-based approach has been termed as 'calorie fundamentalism' and has been

criticized for its narrow focus, the official poverty line based approach has been criticized for

being inconsistent with figures of calorie deprivation and malnutrition. One way of moving ahead


is to carry forward this comparison between percentage of population not having minimum

calories (on which the poverty line was based) and the official poverty estimates to a more

disaggregated level. This is what we have attempted here.

Table 5 Level of Calorie Deprivation and Poverty (Percentage) among Socio-Economic &

Demographic groups during 2004/05 Socio-Economic Demographic Groups Calorie deprivation Population Below Poverty line

Marital Status Never Married 82.30 31.3 Currently Married 78.10 25.4 Widow/Divorced/Separated 75.40 25.2

Household Size 1-4 70.60 17.0 5-6 82.30 29.1 7-8 85.30 37.4 Above 8 85.80 36.4

Social Group Scheduled Tribe 88.50 47.6 Scheduled Caste 85.10 36.8 Others 77.10 22.7

Religious Group Hindu 79.70 28.9 Muslim 84.40 29.3 Christian 80.90 16.2 Others 69.70 15.2

Education Group Not Literate 83.50 36.5 Primary or below 81.10 27.1 Secondary 72.60 14.7 Graduate or above 59.70 5.0

MPCE Groups (Rs.) 0-235 99.70 100.0 235-270 99.00 100.0 270-320 98.40 100.0 320-365(poverty line Rs.356.30) 95.90 80.9 365-410 92.70 Nil 410-455 89.30 Nil 455-510 83.80 Nil 510-580 77.20 Nil 580-690 67.60 Nil 690-890 57.40 Nil 890-1155 43.00 Nil 1155 & more 32.80 Nil

Occupation Type Self employed in non agriculture 81.60 23.5 Agricultural Labour 88.90 46.4 Other Labour 87.40 30.4 Self employed in agriculture 73.10 21.5 Others 73.80 14.0

Source: Authors' calculation from NSS 61st Consumer Expenditure Schedule

Table 5 clearly shows that during 2004-05 among all groups where calorie deprivation level is

high, poverty level has also been higher. This analysis is based on gross effects and hence no

causalities are implied. It is found that never married persons report both relatively higher levels


of poverty and calorie deprivation compared to their group categories. In case of family size,

bigger the household higher is the level of calorie deprivation and poverty. Small households

covering 1-4 members experience lowest poverty and calorie deprivation level. Bigger households

(more than 7 members) perform worse on both counts. As far as social groups are concerned, it is

found that lower social groups such as ST and SC tend to have higher concentration of poverty

and calorie deprivation level, whereas the reverse is true for the 'other social group'. STs are worst

affected as poverty and calorie deprivation level is highest among them, followed by the SCs. If

we see deprivation and poverty level among the religious groups, we find that particularly

Muslims are in a worse condition as both calorie deprivation (84.4 percent) and poverty level (33

percent) are much higher among them in comparison to others. Education wise analysis shows that

it is the lower educated persons who are living in poverty and consuming lower calories than

standard norm. Higher is the education level lower is the levels of poverty and hunger. Illiterate

persons experience a highest level of poverty (36.5 percent) and calorie deprivation (83.5 percent)

level while educated people (with graduation and above) recorded lowest level of poverty (5

percent) and calorie deprivation (59.7 percent) level.

Similarly, lower the MPCE class, higher is the level of poverty and calorie deprivation. Thus,

bottom MPCE classes are unable to feed themselves even the standard calories and are living in

poverty. In terms of occupation groups, agricultural labourers perform worst on both counts

followed by ‘other’ labourer. Thus, while the official poverty measures and calorie deprivation

might show different levels of deprivation, there is a close correspondence among the two so far as

the pattern of deprivation across different groups are concerned.

INTERSTATE AND REGIONAL ANALYSIS

Inter-state variations in levels of deprivation has been one of the persistent themes in the poverty

debate in India (Deaton and Dreze 2010; Patnaik, 2007; Dev 2005). Specific to the divergence

between poverty estimates and calorie deprivation is the wide difference between the two

estimates in India's southern states. Many of the southern states have better human development,

demographic and social development indicators, and the records of state interventions in the areas

of food security, primary education and affirmative action in favour of the weaker sections are

generally considered to be better in most, if not all states of south India, particularly in comparison

with the densely populated north Indian states. In this backdrop, the fact that southern states

generally have a lower incidence of consumption poverty but a relatively higher degree of calorie-

deprivation has been an important issue in the discussion. Patnaik (2007) views poverty as being

underestimated in southern states, whereas Dev (2005) argues that poverty using calorie norm in

southern states give absurd results.

Deaton and Dreze (2009) criticizes calorie norm as poverty method as this norm places all

southern states at higher deprivation level despite a fact that these states perform better in some

anthropometric measures. The incompatibility of the poverty estimates and levels of calorie

deprivation is brought out sharply in Table 6.

The discussion here has been widened by incorporating two additional indicators of deprivation

and it is important to note that southern states particularly Karnataka, Tamil Nadu, Andhra

Pradesh rank high on more than two deprivation indicators which confirm their poor performance

on selected deprivation indicators. For example, Karnataka ranks 10th in poverty level, 21st in


calorie deprivation, 12th in children underweight and 13th in BMI of women. Similarly, Tamil

Nadu ranks 12th in poverty level, 19th in calorie deprivation, and 12th in BMI of women.

Performance of Andhra Pradesh in terms of deprivation indicators is 13th in calorie deprivation,

7th in children underweight and 11th in BMI. Kerala is the only state in southern region which

perform better in all deprivation indicators. Maharashtra however record better performance in

terms of anthropometric measures but poverty (14th) and calorie deprivation (18th) level is high in

this state. On the other hand, Punjab, Himachal Pradesh, Jammu and Kashmir and Haryana are

best performing states in all deprivation measures whereas worst performance is shown by

Jharkhand, Madhya Pradesh, West Bengal, Orissa, Chhattisgarh and Bihar (Fig. 1). At the state

level, a correlation among the different indicators of deprivation is low6.

Table 6 Performance of States on selected Deprivation Indicators and their ranking during 2004-05

States Below poverty Line*

Below 2400 Kcal*

Children (< 3) Under weight#

BMI below normal (Women)#

Jammu &

Kashmir 4.3 (1) 65.5 (1) 31.6 (2) 26.1 (6) Punjab 9 ( 2) 68.4 (4) 29.9 (1) 14.5 (3) Andhra Pradesh 10.5 (3) 83.8 (13) 40.4 (7) 37.5 (11) Himachal Pradesh 10.5 (4) 66.3 (2) 36.4 (5) 25.8 (5) Arunachal

Pradesh 10.9 (5) 70.9 (5) 42.1 (11) 14.3 (1) Haryana 13.2 (6) 67.6 (3) 41.8 (10) 32.5 (8) Kerala 13.2 (7) 75.4 (9) 31.9 (3) 14.3 (2) Rajasthan 18.3 (8) 74.5 (7) 45.9 (14) 36.5 (9) Gujarat 18.9 (9) 84.8 (15) 50 (17) 41.9 (15) Karnataka 20.7 (10) 89 (21) 45.1 (12) 38.2 (13) Assam 22.1 (11) 85.4 (16) 41.1 (9) 39.5 (14) Tamil Nadu 23 (12) 87.3 (19) 34.8 (4) 37.5 (12) West Bengal 28.4 (13) 78.1 (10) 46.7 (15) 44.9 (18) Maharashtra 29.6 (14) 86.9 (18) 40.1 (6) 15.4 (4) Uttar Pradesh 33.3 (15) 73.3 (6) 49.4 (16) 37.2 (10) Madhya Pradesh 36.8 (16) 87.5 (20) 62.6 (20) 44.2 (17) Uttaranchal 40.6 (17) 74.5 (8) 40.8 (8) 30.8 (7) Chhattisgarh 40.8 (18) 84 (14) 54.6 (18) 45.7 (19) Bihar 42.6 (19) 78.8 (12) 59.3 (19) 45.9 (20) Jharkhand 46.2 (20) 85.7 (17) 63.1 (21) 47.8 (21) Orissa 46.9 (21) 78.5 (11) 45.7 (13) 43.7 (16)

Source: * Same as Table 5, # Computed from National Family Health Survey, Fact Sheets, 2005-06

The level of nutrition (Table 7) in terms of calorie and protein intake across all major states show

that Karnataka, Tamil Nadu, Andhra Pradesh, Gujarat and Maharashtra are the states where calorie

and protein intake is quite low and in fact these states also show maximum decline in both the

nutrients between 1993-94 and 2004-5. The deficiency of calorie and protein from

recommendation is quite high in all southern states.

During 2004-05 deficiency of calorie was high in Andhra Pradesh (409 kcal), Gujarat (501 kcal),

Karnataka (538 kcal), Madhya Pradesh (472 Kcal), Maharashtra (476 kcal) and Tamil Nadu (536

kcal). In fact deficiency of protein was also larger in these states such as Andhra Pradesh (13 gm),

Assam (10 gm), Gujarat (9 gm), Karnataka (13 gm) Kerala (7 gm), Maharashtra (8 gm), Tamil

Nadu (16 gm) and West Bengal (10 gm).


This above analysis shows that calories and protein deprivations are consistently high in all

southern states except Kerala, whereas there are some states like Punjab, Himachal Pradesh,

Jammu and Kashmir, Haryana, Uttar Pradesh and Rajasthan where calorie intake recorded a slight

decline and consumption of protein is increasing during the period under consideration. In fact

states showing lower level of calorie deficiency (such as Haryana, Himachal Pradesh, Jammu and

Kashmir and Punjab) have performed better during 2004/05 and they have also recorded a larger

increase of calorie and protein in diet during the period under consideration.

Table 7 Change in level of nutrients and deficiency from recommendation between 1993/94-2004/05

across all major states in rural India

States

Calorie Intake Deficit from

RDA, 2400 Kcal Protein Intake

Deficit from RDA, 60 gm

1993-94

2004-05

1993-94

2004-05

1993-94

2004-05

1993-94

2004-05

Andhra Pradesh 2044 1991 356 409 50.3 47.4 10 13 Arunachal Pradesh 2126 2316 274 84 61.3 59.4 +1 1 Assam 1983 2055 417 345 49.5 50.4 10 10 Bihar 2113 2021 287 379 60.1 54.9 0 5 Gujarat 1989 1899 411 501 55.3 50.5 5 9 Haryana 2486 2212 +86 188 78.2 67.8 +18 +8 Himachal Pradesh 2322 2314 78 86 70.4 67.0 +10 +7 Jammu & Kashmir 2504 2358 +104 42 75.3 62.4 +15 +2 Karnataka 2067 1862 333 538 54.7 47.0 5 13 Kerala 1956 2113 444 288 50.2 53.4 10 7 Madhya Pradesh 2158 1928 242 472 62.6 53.9 +3 6 Maharashtra 1933 1924 467 476 54.7 51.8 5 8 Orissa 2197 2008 203 392 52.6 46.2 7 14 Punjab 2414 2219 +14 181 74.6 64.5 +15 +4 Rajasthan 2461 2157 +61 243 78.9 67.1 +19 +7 Tamil Nadu 1872 1865 528 536 46.1 43.9 14 16 Uttar Pradesh 2303 2195 97 205 70.3 64.2 +10 +4 West Bengal 2210 2065 190 335 54.7 50.5 5 10 Total 2148 2044 252 356 59.9 55.1 0 5

Source: Same as Table 2. Notes: Same as Table 4

A state level analysis may hide the micro level variations in calorie deprivation. There is some

heterogeneity within the states so far as nutrition deficiency is concerned. Hence, an analysis has

also been performed at NSS region level (Fig. 2) which tries to identify the regions experiencing

calorie deprivation. Out of selected 72 NSS regions, 48 regions experience higher level of

nutrition deficiency (more than 80 percent). The worst performance is shown by regions of

Madhya Pradesh which include Vindhyan and south western parts. Dry areas of Gujarat also

exhibit higher nutrition deficiency. Coastal parts of Maharashtra and southern parts of Orissa show

more than 92 percent population to be calorie deprived. The performance of regions of southern

states also does not pose a better picture.


Fig. 1

28

JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING

Fig. 2

JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING

Fig. 2

JOURNAL OF REGIONAL DEVELOPMENT AND PLANNINGJOURNAL OF REGIONAL DEVELOPMENT AND PLANNINGJOURNAL OF REGIONAL DEVELOPMENT AND PLANNING


Inland northern parts of Karnataka, coastal northern Tamil Nadu and south-western Andhra

Pradesh experiencing much higher level of nutrition deficiency which may be one of the reasons

of poor performance of southern states on deprivation indicators. It is clear from the figure (Fig.

2) that the regions in south India where level of calorie deprivation is relatively high form a

contiguous belt. The regions which pose a picture of relatively better nutrition sufficiency include

northern and southern parts of Punjab, Himachal Pradesh, western plains of West Bengal, Jhelum

Valley and mountainous parts of Jammu and Kashmir, central and western Uttar Pradesh.

Table 8 Logistic Regression Analysis for Showing Probability of Getting Required Calories

Variables Variable Categories Beta Sig.@ Exponential Beta

Social Group

Others (Ref)^ 0.000 1 Scheduled Tribe 0.421 0.000 1.524 Scheduled Caste 0.318 0.000 1.375

Religious group

Hindu (Ref)^ 0.000 1 Muslim 0.296 0.000 1.344 Christian -0.159 0.000 0.853 Others -0.4 0.000 0.671

Education Level

Primary or below (Ref)^ 0.000 1 Not Literate 0.108 0.000 1.114 Secondary -0.32 0.000 0.726 Graduate or above -0.631 0.000 0.532

Marital Status

Currently Married (Ref)^ 0.000 1 Never Married 0.104 0.000 1.109 Widow/Divorced/Separated -0.28 0.000 0.756

Household Size

1-4 (Ref)^ 0.000 1 5-6 0.682 0.000 1.978 7-8 0.924 0.000 2.52 Above 8 1.147 0.000 3.15

Occupation

Type

Self employed in non agriculture (Ref)^

0.000 1

Agricultural Labour 0.154 0.000 1.166 Other Labour 0.282 0.000 1.326 Self employed in agriculture

-0.524 0.000 0.592

Others -0.186 0.000 0.83

Poverty Line

Group

Above Poverty Line (Ref)^ Below poverty Line 2.649 0.000 14.146

Regions

Central (Ref)^ 0.000 1 North 0.147 0.000 1.159 East 0.107 0.000 1.113 North East 1.094 0.000 2.987 West 1.033 0.000 2.809 South 0.969 0.000 2.636

Constant 0.204 0.000 1.226

Source: Same as Table 5. Note:@Significance level, ≥ 0.01= 1 percent, 0.02-0.05= 5 percent, 0.06-0.1= 10 percent; ^Reference Category Dependent Variable: Calorie Intake, 1 shows below 2400 Kcal and 0 shows 2400 & above Kcal


PROBABILITY OF CONSUMING RECOMMENDED CALORIES: A

DISAGGREGATED ANALYSIS

In this section the factors affecting probability of consuming recommended calories have been

probed through a logistic regression (Table 8). Our results clearly show that probability of getting

recommended calories is quite low among all weaker socio-economic groups. For example, as the

family size increases, the likelihood of consuming recommended calories declines which exhibits

poor nutritional conditions of bigger households. The households covering more than 8 members

in the family exhibit higher probability (odd ratio 3.15) of being calorie deprived than the small

families having 1-4 members. Among social groups, ST are worst affected as the probability of

consuming recommended calories is very low compared to other social groups. SC people

however have lower likelihood (1.35 odd ratios) of being calorie deprived than ST (1.52 odd

ratios). Regarding religious group, Muslims suffer badly as they have higher probability of being

calorie deprived than the Hindus whereas Christians (0.853 odd ratio) and other religion people

(0.671 odd ratio) enjoy better calorie intake than the Hindus. Education level plays an important

role to determine calorie intake. It has been analysed that as the level of education increases, the

likelihood of consuming calories from the norm also rises. Highly educated people show more

chances of taking recommended calories than the other lower education group people.

Considering the probability of calorie intake among occupation groups, agricultural labourers and

other labourers have lesser probability of consuming recommended calories than the employed in

non-agriculture. Self employed in agriculture and other occupation groups have more chances of

becoming energy sufficient than those who are not self employed in agriculture. As far as poverty

level is concerned, people below the poverty line have a much higher likelihood of being calorie

deprived (14.146 odd ratios) than the Above Poverty Line category people.

The probability of consuming recommended calories across different geographical regions of rural

India show that compared to central region (covering states of Uttar Pradesh, Madhya Pradesh and

Chhattisgarh), all regions show lower likelihood to consume recommended calories. Among them

north eastern, western and southern region covering states of Gujarat, Maharashtra, Karnataka,

Tamil Nadu, Andhra Pradesh and Kerala exhibit more chances of being calorie deprived from

recommended calories. Northern and eastern states such as Punjab, Himachal Pradesh, Jammu and

Kashmir, Haryana, Rajasthan, Orissa and Bihar show lower probability of calorie deprived than

the other regions. However, these regions are prone to calorie deprivation when compared with

central region. A relatively lower likelihood of being calorie deprived is resulted by higher

consumption of cereals.

CONCLUSION

From this analysis it is found that over a decade (1994-2005) the consumption pattern of Indians

has changed significantly. Consumption of cereals, particularly coarse cereals, has declined

whereas consumption of other food items such as vegetables, fruits, milk and milk products, meat

increased slightly which have a direct bearing on nutrient intake. Due to decline in cereal

consumption and lower increase in consumption of other food items nutrient pattern in rural India

has also changed substantially. Share of cereals particularly coarse cereals to total calories has

declined whereas calories from oil and fat have increased. Since cereals are also a good source of

protein but its decline has also led to lowering down of protein. In rural India on an average per


capita per day calorie and protein intake is falling and consumption of oil and fat is increasing.

This, to some extent, is as per the expenditure of dietary transition models. However, given the

relative underperformance of India in the nutrition front, this decline in cereal consumption has

often been viewed as deterioration in the living standard of the poor. The disaggregated analysis of

calorie and nutrition deficiency in rural India carried out in this study clearly points out that

deprivation is higher among marginalized social and economic groups. It is the poor, SC and ST

groups, agricultural labourers who suffer most in terms of calorie deprivation.

There is much gap in official poverty and calorie deprivation level. We have estimated both

poverty and calorie deprivation across social groups. Those having bigger families, less education,

lower MPCE and those belonging to ST, SC, agricultural labour and other labour class, Muslims

are found to have higher levels of poverty as well as calorie deprivation. Thus, in terms of

distribution of deprivation across social and economic groups, there is a consistency between

poverty and calorie deprivation although the levels are quite different in many cases. The interstate

variations, however, does not show much consistency. The southern states particularly Karnataka,

Tamil Nadu, Andhra Pradesh perform poor on more than two deprivation indicators. Gujarat and

Maharashtra, considered as relatively developed states perform worse on both methods of poverty

measurement. On the other hand, Punjab, Himachal Pradesh, Jammu and Kashmir and Haryana

are best performing states in all deprivation measures. From a regional point of view, it is found

that most of the NSS regions having majority of population being calorie deprived than

recommendation fall in the southern, western and central parts of India. All the southern states

except Kerala and including Gujarat and Maharashtra presents maximum decline in calorie and

protein intake from the recommendation whereas Punjab, Himachal Pradesh, Jammu and Kashmir

and Haryana, Uttar Pradesh and Rajasthan show lower decline in calories and in fact increase in

protein intake. These states also show lower level of calorie deprivation and poverty.

The exercise undertaken to show probability of being calorie deprived concludes that never

married, big families, less educated, lower MPCE class, ST, SC, agricultural labour and other

labour class, Muslims, people living below poverty line and southern, north-eastern and western

states are some weaker sections and regions which are comparatively more prone to be poor and

undernourished than their respective reference categories. The debate so far has concentrated on

the observed divergence between poverty estimates and calorie deprivation. Our analysis,

however, points out that it is the relatively marginalized social and economic groups who face

greater calorie deprivation. Thus, there is an urgent need to focus on such high levels of

deprivation among the marginalized groups and regions.

_________________________________

Notes 1. Calorie norm has officially been taken to measure poverty level in India. Per capita per day intake of

2400 kcal for rural and 2100 kcal for urban areas are the norms to estimate poverty. Planning Commission makes adjustment in Consumer Price Index for Agricultural Labourers (CPIAL) and Consumer Price Index for Industrial Workers (CPIIW) to the base year poverty line (1973-74) for estimating rural and urban poverty respectively. Planning Commission’s estimation of poverty using indirect method shows lower level of poverty whereas directly using calorie norm to measure poverty gives a much higher level of deprivation.


2. Food items have been converted into nutritive values using the standard units given in report no. 513(61/1.0/6) Nutritional Intake In India (2004-2005), NSS 61st round National Sample Survey Organisation, Ministry Of Statistics & Programme Implementation Government of India.

3. For further details on measurement of official poverty line in India and changes in it, see Utsa Patnaik, 2007.

4. Standard Calories are given in the Report of the Export Group on Estimation of Proportion and Number of Poor. Perspective Planning Division. Planning Commission, 1993 - 2400 kcal per capita for rural area and 2100 kcal for urban area and standard protein intake is recommended in report on ‘Nutritional Status of Rural Population’ by National Institute of Nutrition (1996) Indian Council of Medical Research, Nutritional Status of rural population, Report of the NNMB surveys, National Nutritional Monitoring Bureau, Hyderabad.

5. Official poverty has been calculated using the report of ‘Poverty Estimates For 2004-05’ Government of India Press Information Bureau [Online at] planningcommission.nic.in/news/prmar07.pdf , Accessed on 12/03/2010 at Jawaharlal Nehru University.

6. The correlation between Below poverty line (BPL) and Below 2400 kcal is 0.472 (significant at 0.05 level) which is low as compared to correlation between BPL and Children underweight below 3 (0.733, significant at 0.01 level) and between BPL and Body Mass Index of Women (0.622, significant at 0.01 level).

References Bansil, P.C. (2003) – “Demand For Food Grains By 2020 Ad”, in S. Mahendra Dev et al. (eds.)

Towards A Food Secure India: Issues And Policies, Institute For Human Development, New Delhi.

Deaton, A. & Dreze, Jean (2009) – “Food and Nutrition in India: Facts and Interpretations”, Economic & Political Weekly, Vol. 44, No. 7, pp. 42-65.

Deaton, A. & Dreze, Jean (2010) – “Nutrition, Poverty and Calorie Fundamentalism: Response to Utsa”, Economic & Political Weekly, Vol. 45, No. 14, pp. 78-80.

Dev, S. M. (2005) – “Calorie Norms and Poverty”, Economic & Political Weekly, Vol. 40, No. 8, pp. 789-792.

Dubey, A. & Thorat, S. K. (2012) – “Has growth been socially inclusive during 1993/94-2009/10?” Economic & Political Weekly, Vol. 47, No. 10, pp. 43-54.

International Institute for Population Studies (2005-06) – National Family Health Survey 2005-06,

Fact Sheet, International Institute for Population Studies, Mumbai. Jones, R. Palmer & Sen, K. (2001) – “On India’s Poverty Puzzles and Statistics of Poverty”,

Economic & Political Weekly, Vol. 36, No. 3, pp. 211-217. Kumar, P., Mruthyunjaya & Dev, Madan M. (2007) – “Long Term Changes in Indian Food Basket

and Nutrition”, Economic & Political Weekly, Vol. 42, No. 35, pp. 3567-3572. Martorell, Reynaldo & Ho, Teresa J. (1984) – “Malnutrition, Morbidity, and Mortality”,

Population and Development Review, Vol. 10, pp. 49-68. Mehta, Jaya & Venkatraman, S. (2000) – “Poverty Statistics, Bermicide’s Feast”, Economic &

Political Weekly, Vol. 35, No. 27, pp. 2377-2382. Mehta, Jaya (1982) – “Nutritional Norms and Measurement of Malnourishment and Poverty”,

Economic & Political Weekly, Vol. 17, No. 33, pp. 1332-1340. Ministry of Home Affairs (2010) – Sample Registration System, Report No. 1 of 2012, Statistical

Report 2010, New Delhi. Nasurudeen, P., Kuruvila, A., Sendhil, R. & Chandresekar, V. (2006) – “The Dynamics and

Inequality of Nutrient Consumption in India”, Indian Journal of Agriculture Economics, Vol. 61, No. 3, pp. 362-373.

Patnaik, Utsa (2004) – “Republic of Hunger”, Social Scientist, Vol. 32, No. 9/10, pp. 9-35. Patnaik, Utsa (2007) – “Neoliberalism and Rural Poverty in India”, Economic & Political Weekly,

Vol. 42, No. 30, pp. 3132-3150.


Patnaik, Utsa (2010) – “A Critical Look at Some Propositions on Consumption and Poverty”, Economic & Political Weekly, Vol. 45, No. 6, pp. 74-80.

Radhakrishna, R. & Reddy V. (2004) – ‘Food Security and Nutrition: Vision 2020’, [planningcommission.nic.in/reports/.../bkpap2020/16_Bg2020.pdf; accessed on 9 August 2010].

Radhakrishna, R. (2005) – “Food And Nutrition Security of the Poor, Emerging Perspectives and Policy Issues”, Economic & Political Weekly, Vol. 40, No. 18, pp. 1817-1821.

Radhakrishna, R., Rao, K. Hanumantha, Ravi, C. & Reddy, B. Sambi (2004) – “Chronic Poverty and Malnutrition in 1990s”, Economic &Political Weekly, Vol. 39, No. 28, pp. 3121-3130.

Rao, H. C. H (2000) – “Declining Demand for Food-Grains in Rural India: Causes and Implications”, Economic & Political Weekly, Vol. 35, No. 4, pp. 201-206.

Ray, Ranjan (2005-10) – ‘Analysis of Changes in Food Consumption and their Implications for Food Security and Undernourishment: The Indian Experience in the 1990s’, Discussion

Paper, University of Tasmania. Reddy, D. N. & Mishra, Srijit (2010) – Agrarian Crisis in India, Delhi: Oxford University Press. Sen, Pronab (2005) – “Of Calories and Things Reflections on Nutritional Norms, Poverty Lines

and Consumption behaviour in India”, Economic & Political Weekly, Vol. 40, No. 43, pp. 4611-4618.

Shariff, Abusaleh & Mallick, A. C. (1999) – “Dynamics of Food Intake and Nutrition by Expenditure Class in India”, Economic & Political Weekly, Vol. 34, No. 27, pp. 1790-1800.

Verma, Med Ram, Datta, K. K., Mandal, Subhasis & Tripathi, A. K. (2008) – “Diversification of Food Production and Consumption Patterns in India”, Journal of Agricultural & Food

Information, Vol. 8, No. 3, pp. 87-100. Viswanathan, Brinda (2001) – “Structural Breaks in Consumption Patterns: India 1952-1991”,

Applied Economics, Vol. 33, No. 9, pp. 1187-1200. World Food Programme (2009) – ‘India Tops World Hunger Chart’,

[http://www.wfp.org/Countries/India/News/Hunger-In-The-News?Page=4; accessed on 23 July 2011]

World Health Organisation (2003) – ‘Diet, Nutrition and the Prevention of Chronic Diseases’, Technical Report Series 916, Geneva.

Documents

POVERTY AND CALORIE DEPRIVATION ACROSS SOCIO-ECONOMIC GROUPS IN RURAL INDIA: A DISAGGREGATED ANALYSIS