chapter-4 expenditure elasticity and demand...

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Chapter-4

Expenditure Elasticity and Demand Projections

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Chapter-4 Expenditure Elasticity and Demand Projections

4.1 Introduction

In the previous chapter the researcher has discussed about the demand parameters derived

by the QUAIDS model. The parameters of different food items were further used for

calculation of compensated and uncompensated elasticities of demand. This chapter dealt

with the expenditure elasticities of various food items which are derived by using the

panel regression approach. These estimated elasticities are used for demand projections

of these items.

4.2 Expenditure Elasticities of Selected Food Items

The expenditure elasticities of selected food items which have been estimated using the

panel regression approach. The panel regression is popular approach for the panel data

analysis. In this study the data on monthly per capita consumption expenditure have been

taken from eight NSSO rounds viz.. 55th

(1999-2000), 56th (2000-2001), 57

th (2001-

2002), 58th

(2002), 59th

(2003), 61st (2004-05), 62

nd (2005-2006), 63

rd (2006-2007), 64

th

(2007-08) and 66th

(2009-10) rounds. In each round the monthly per capita consumption

expenditure data are given for the different states of India. So the researcher has a data set

which is cross sectional in different time periods. Therefore it was decided to apply the

panel regression approach for estimating expenditure elasticity. This approach has been

outlined in the first chapter of this study.

There are three different panel regression models namely Pooled OLS, Fixed Effect

Model and Random Effect Model. Each model has its independent character. The panel

regression models which have been used in this study are as follows;

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(A) Pooled OLS

𝑙𝑛𝑀𝑃𝐶𝐸𝐹𝑥𝑠𝑡 = 𝛼 + 𝛽 𝑙𝑛𝑀𝑃𝑇𝐸𝑠𝑡 + 𝜖𝑠𝑡

Where,

PMCEFxst = Monthly Per Capita Consumption Expenditure on food item x for state

s……n and for the year t……..m;

𝑀𝑃𝑇𝐶𝐸𝑠𝑡 = Monthly Per Capita Total Consumption Expenditure for state s……n and

for the year t……..m. 𝛼, 𝛽 and 𝜖 are the parameters of model.

(B) Fixed Effect Model

𝑀𝑃𝐶𝐸𝐹𝑥𝑠𝑡 = βs + β1𝑀𝑃𝑇𝐶𝐸st + ust

Where,

𝑀𝑃𝐶𝐸𝐹𝑥𝑠𝑡 = is the Monthly Per capita Consumption Expenditure on

food item x for state s and time t.

𝑀𝑃𝑇𝐶𝐸𝑠𝑡 = Monthly Per Capita Total Consumption Expenditure for state

s……n and for the year t……..n

βs =(s = 1…..n) is the unknown intercept for each state

(n state – specific Intercepts)

ust = is the error term

(c) Random Effect Model

𝑙𝑛𝑀𝑃𝐶𝐸𝐹𝑥𝑠𝑡 = β0 + β1 𝑙𝑛𝑀𝑃𝑇𝐶𝐸 𝑠𝑡+ ust + εst

Where, ust explains ―the between state error‖ and εst is ―within state error‖.

The above mentioned panel regression model has used for rural, urban and for all India.

When the model is applied for all India the urban dummies included in equation. The

selection of the appropriate panel regression model has been done by using different tests.

The selection of Pooled OLS model has been done by using the Joint test and the

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Breusch-Pagan test and the selection between Fixed Effect model and Random Effect

model has been done by using the Hausman test. The assumptions of these tests are as

follows;

Joint Test:

H0 = The Pooled OLS Model is adequate, in favor of the Fixed Effect alternative

If the p-value of this test is less than 0.05, the null hypothesis will be rejected, that means

the fixed effect model gives better results than the pooled OLS model and vice versa.

Breusch-Pagan test

H0 = The Pooled OLS Model is adequate, in favor of the Random Effect

alternative


the random effects model gives better results than the pooled OLS model and vice versa.

Hausman test

H0 = the random effects model is consistent in favor of the fixed effects model


the fixed effect model gives better results than the random effects model and vice versa.

Thus, all the three subsets of three approaches can be tested with each other using the

above mentioned tests.

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4.2.1 Expenditure Elasticities of Selected Food Items - Rural Area

In the following tables the results of different tests which have been used for the selection

of appropriate panel regression model are given.

Table 4.2.1(a): Selection of Panel Regression Model for Calculation of Expenditure

Elasticities of Different Food Items (Rural Area)

Food Items Joint Test

(P value)

Breusch-

Pagan test

(P value)

Hausman

test

(P value)

Selection among the

Fixed Effect/Random

Effect/Pooled OLS

methods

Cereals 0.000 0.000 0.004 Fixed Effect

Pulses 0.000 0.000 0.018 Fixed Effect

Milk 0.000 0.000 0.05 Fixed Effect

Edible oil 0.000 0.000 0.010 Fixed Effect

Meat, Fish &

Chicken

0.000 0.000 0.434 Random Effect

Vegetables 0.000 0.000 0.000 Fixed Effect

Sugar 0.000 0.000 0.432 Random Effect

Total Food 0.000 0.000 0.000 Fixed Effect

Source: Estimated by Researcher

On the basis of above table, it may concluded that only in the case of meat, Chicken &

fish and the sugar consumption the random effect model has been found to be appropriate

because the null hypotheses test by Hausman test has not been rejected. Therefore, the

random effects model has been used for deriving the expenditure elasticity for these two

food items.

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Table 4.2.1 (b): Expenditure Elasticities of Selected Food Items in India (Rural Areas)

Food Items Intercept Elasticity R2

Cereals 1.563*** 0.49*** 0.90

Pulses -3.641*** 1.01*** 0.91

Milk -1.217** 0.74*** 0.90

Edible oil -3.385*** 1.01*** 0.88

Meat, Fish & Chicken -5.072*** 1.25*** N.A.

Vegetables -4.796*** 1.31*** 0.94

Sugar -2.56*** 0.78*** N.A.

Total Food -0.437*** 0.97*** 0.98


*** 0.01 Significance level, ** 0.05 Significance Level

N.A. - R2 is not applicable as Random Effect is selected

It can be observed from the above table that the expenditure elasticities of food items

likes cereals, pulses, milk, edible oil, meat, chicken & fish, vegetables and sugar are 0.49,

1.01, 0.74, 1.01, 1.25, 1.31 and 0.78 respectively in rural area. For pulses, edible oil,

meat, chicken & fish and vegetables the expenditure elasticities are greater than one. So,

one can say that with one percent increase in the total expenditure of rural people, their

expenditure on pulses, edible oil, meat, chicken and fish and vegetables have been

increased by more than one percent. The lowest expenditure elasticities have been found

for cereals and the highest for vegetables.

The value of R square ranges between 0.88 to 0.9. Thus, 88.0% to 91.0% of the variation

in the monthly per capita consumption expenditure on these items is due to variation in

the monthly per capita total consumption expenditure

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4.2.2 Expenditure Elasticities of Selected Food Items - Urban Area Table 4.2.2(a): Selection of Panel Regression Model for Calculation of Expenditure

Elasticities of Different Food Items (Urban Area)


(P value)

Breusch-Pagan

test (P value)

Hausman test

(P value)

Selection among the

Fixed Effect/Random

Effect/Pooled OLS

methods



Milk 0.042 0.169 0.053 Fixed Effect


Meat, Fish

& Chicken

0.763 0.385 0.660 Pooled OLS


Sugar 0.077 0.173 0.970 Pooled OLS



In the case of urban area, the pooled OLS model has been used only for the meat, chicken

& fish and sugar and the fixed effects model has been applied for rest of the food items.

For meat, chicken & fish the null hypothesis has not been rejected in the joint test,

Breusch-pagan test and Hausman test. Therefore the Pooled OLS model is selected on the

basis of joint test. The same result has been observed for sugar.

Table 4.2.2 (b) Expenditure Elasticities of Selected Food Items in India (Urban Areas)

Food Items Intercept Elasticity R2

Cereals 2.95*** 0.26*** 0.10

Pulses -0.76 0.56*** 0.64

Milk -4.24*** 1.21*** 0.36

Edible oil -0.22 0.53*** 0.67

Meat, Fish & Chicken -3.08** 0.93*** 0.10

Vegetables -0.42 0.63*** 0.54

Sugar -2.30*** 0.71*** 0.33

Total Food 1.23*** 0.71*** 0.92




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The expenditure elasticities for different food items in the urban area have been given in

the above table. Elasticities for the food items like cereals, pulses, milk, edible oil, meat,

chicken & fish, vegetables and sugar were 0.26, 0.56, 1.21, 0.53, 0.93, 0.6 and 0.71

respectively. Only for the milk, the expenditure elasticity has been found greater than

one. The lowest expenditure elasticity has been found for cereals and the highest for milk.

Expenditure elasticities of all food items has been observed to be statistically significant.

The value of R square has ranged between 0.10 and 0.67. For cereals and meat, chicken

& fish the value of R square is very low (0.10), which indicate that only 10% of the

variation in the monthly per capita consumption expenditure on these food items is due to

variation in the monthly per capita total consumption expenditure. The low R square

values have also been observed for milk (0.36) and for sugar (0.33). For all other food

items the values of R2 are high.

Table 4.2.2 (c) Comparison of Expenditure Elasticities of Rural and Urban Areas

Food Items Expenditure Elasticities

Rural Area Urban Area

Cereals 0.49*** 0.26***

Pulses 1.01*** 0.56***

Milk 0.74*** 1.21***

Edible oil 1.01*** 0.53***

Meat, Fish & Chicken 1.25*** 0.93***

Vegetables 1.31*** 0.63***

Sugar 0.78*** 0.71***

Total Food 0.97*** 0.71***

Source: Estimated by Researcher *** 0.01 Significance level, ** 0.05 Significance Level


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The expenditure elasticity of cereals, pulses, edible oil, meat, fish & chicken, vegetables

and sugar in the rural area has been found to be more than expenditure elasticity of these

items in the urban area. So, One can infer that rural people are more responsive to change

in the monthly per capita total consumption expenditure than urban people as far as these

commodities are concerned. However in the case of milk the opposite situation has been

found. The urban people are more responsive as far as milk consumption in concerned

when their total budget has been changed.

4.2.3 Expenditure Elasticities of Selected Food Items - All India Table 4.2.3 (a) Selection of Panel Regression Model for Calculation of Expenditure

Elasticities of Selected Food Items (All India)


(P value)

Breusch-Pagan

test (P value)

Hausman test

(P value) Selection among the

Fixed Effect/Random

Effect/Pooled OLS

methods



Milk 0.000 0.000 0.761 Random Effects


Meat, Fish

& Chicken

0.000 0.000 0.999 Random Effects


Sugar 0.000 0.000 0.999 Random Effects



In the above table the p-values of Joint test, Breusch-Pagan test and Hausman test are

given. On the basis of these p-values, the researcher has selected the fixed effect model

for cereals, pulses, edible oil, and vegetables and for total food. In the case of milk, meat,

fish & chicken and sugar consumptions the random factors are affected and therefore the

random effect model has been selected.

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Table 4.2.3(b) Expenditure Elasticities of Selected Food Items in India (All India)

Food Items Intercept Elasticity Urban

dummy

R2

Cereals 2.62*** 0.37*** 0.09*** 0.61

Pulses 3.51*** 0.95*** 0.28*** 0.78

Milk -3.36*** 1.08*** -0.1.01 N.A.

Edible oil 2.89*** 0.90*** 0.20*** 0.83

Meat, Fish & Chicken -2.62*** 0.87*** -0.03 N.A.

Vegetables -3.63*** 1.09*** 0.28*** 0.77

Sugar -3.27*** 0.84*** 0.297*** N.A.

Total Food 0.11 0.89*** 0.16*** 0.96




On the basis of above table the researcher can say that the expenditure elasticity for

different food items like cereals, pulses, milk, edible oil, meat, chicken & fish, vegetable

and sugar were 0.37, 0.95, 1.08, 0.90, 0.87, 1.09 and 0.84 respectively for all India. These

elasticities have been found to be statistically significant at 0.01 significance levels. The

expenditure elasticities of milk and vegetables have been noted to be greater than one

which implies that there is more variation in the monthly per capita consumption

expenditure of this food item compared to change in the monthly per capita total

consumption expenditure. Hence, these food items may be considered to be the luxurious

items in food basket. The higher expenditure elasticity has also shown that if the per

capita income will grow at faster rate, the demand for these food items will also go up at

faster rate. The lowest expenditure elasticity has been recorded for cereals and the highest

for vegetables.

The coefficient of urban dummy has been found to be statistically significant for cereals,

pulses, edible oil, vegetables and sugar. These coefficients are positive which implies that

there is significant difference between the effects of the monthly per capita total

consumption expenditure on these food items in the case of rural and urban areas. The

value of R ranges between 0.61 and 0.96.

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4.2.4 Expenditure Elasticities of Selected Food Items in India for

Different Classes of Families

Here an attempt has been made to derive expenditure elasticities of selected food items

for different class of families. The data of monthly per capita consumption expenditure

on selected food items by different expenditure classes for different states were available

in 55th

(1999-2000) and 61st (2004-2005) rounds. Hence, the researcher has complied the

data from these rounds. On the basis of recent poverty line given by the Planning

Commission the researcher has classified the families into four groups viz.. ‗very poor‘,

‗poor‘, ‗non-poor‘ and ‗rich‘ families. The detailed information has been given to the

section on methodology contained this study. The panel regression approach has also

been used to estimate the expenditure elasticities of the selected food items for different

classes of families. The results of the panel regression approach are presented as follows;

4.2.4.1 Expenditure Elasticities of Selected Food Items for „Very Poor‟

Families

Table 4.2.4.1 (a) Selection of Panel Regression Model for Calculation of Expenditure

Elasticities of Selected Food Items - „Very Poor‟ Families


(P value)

Breusch-Pagan

test (P value)

Hausman test

(P value) Selection among the

Fixed Effect/Random

Effect/Pooled OLS

methods

Cereals 0.000 0.000 0.931 Random Effects

Pulses 0.000 0.000 0.521 Random Effects


Edible oil 0.000 0.000 0.963 Random Effects

Meat, Fish

& Chicken


Vegetables 0.000 0.000 0.494 Random Effects


Total Food 0.000 0.003 0.436 Random Effects


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In the consumption basket of selected items, the random effects model has been found to

be the best. Hence, the random effects model has been used for determining expenditure

elasticities of these food items.

Table 4.2.4.1 (b) Expenditure Elasticities of Selected Food Items in India

- „Very Poor‟ Families


Dummy

R2

Cereals -0.19 0.78*** 0.07**

N.A.

Pulses -0.27 0.46*** -0.02

Milk -3.42** 1.07*** -0.16**

Edible oil -6.50*** 1.59*** 0.11**

Meat, Fish & Chicken -3.12 0.93* -0.04

Vegetables -3.12*** 1.07*** 0.06*

Sugar -2.29** 0.74*** -0.01

Total Food 0.05 0.91*** 0.03**


*** 0.01 Significance level, ** 0.05 Significance Level *0.10 Significance Level


The expenditure elasticities of milk, edible oil, and vegetables have been found to be

greater than one (i.e. 1.07, 1.59 and 1.07 respectively), which implies that monthly per

capita consumption expenditure of these food items is more responsive to the change in

the monthly per capita consumption total consumption expenditure of the ‗very poor‘

families. The expenditure elasticities of food items like cereals, pulses, meat, fish &

chicken and sugar have been found to be less than one. Therefore, it can be concluded

that these food items are basic necessities for the ‗very poor‘ families. The lowest

expenditure elasticity has been noted for pulses and the highest for edible oil.

The coefficients of urban dummy have been found to be statistically significant for

cereals, milk, edible oil and total food. These coefficients are positive for cereals,

vegetables and total food which imply that the rural people elasticities of these food items

are higher than urban people. However for milk the coefficient of urban dummy is

negative which shows that the elasticity of milk is higher for the urban people than the

rural people.

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4.2.4.2 Expenditure Elasticities of Selected Food Items for „Poor‟ Families


Elasticities of Selected Food Items - „Poor‟ Families


(P value)

Breusch-Pagan

test (P value)

Hausman test

(P value)

Selection among the

Fixed Effect/Random

Effect/Pooled OLS

methods





Meat, Fish

& Chicken






In the case of ‗poor‘ families, the random effects model has been used for all food items

on the basis of Hausman test.

Table 4.2.4.2 (b) Expenditure Elasticity of Selected Food Items in India - „Poor‟ Families

Food Items Intercept Elasticity Urban Dummy R2

Cereals 1.01*** 0.58*** 0.03

N.A.

Pulses -3.74*** 1.05*** 0.01

Milk -8.37*** 1.87*** -0.04

Edible oil -6.67*** 1.58*** 0.29***

Meat, Fish & Chicken -4.29*** 1.18*** -0.12*

Vegetables -2.91*** 1.04*** 0.09**

Sugar -4.99*** 1.18*** 0.11**

Total Food -0.47*** 0.99*** 0.07***




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The results of random effects model applied for all food items for poor class families are

given in the above table. The expenditure elasticities have been found to be greater than

one for all food items excepting cereals. Therefore, it can be said that the poor class

families have changed their monthly per capita consumption expenditure by more than

one percent for all food items excepting cereals when the monthly per capita total

consumption expenditure changed by one percent. The high expenditure elasticities of

these food items show that ‗poor‘ families are highly responsive to change in food

consumption of these items when their total consumption expenditure increases. The

expenditure elasticity of cereals reported to be 0.58. The lowest expenditure elasticity is

0.58 for cereals and the highest is 1.87 for Milk.

The coefficients of urban dummy have been found to be statistically significant for edible

oil, vegetables, sugar and total food. These coefficients are positive which imply that the

rural people elasticities of these food items are higher than urban people.

4.2.4.3 Expenditure Elasticities of Selected Food Items for „Non-Poor‟

Families


Elasticities of Selected Food Items – „Non-Poor‟ Families


(P value)

Breusch-Pagan

test (P value)

Hausman test

(P value)

Selection among the

Fixed Effect/Random

Effect/Pooled OLS

methods





Meat, Fish &

Chicken 0.000 0.000 0.963 Random Effects

Vegetables 0.000 0.000 0.040 Fixed Effects




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On the basis of above table, the random effects model has been selected for all food items

excepting vegetables for ‗non-poor‘ families. However, the fixed effect model for

vegetables is recommended by the test.

Table 4.2.4.3 (b) Expenditure Elasticities of Selected Food Items in India

- „Non-Poor‟ Families

Food Items Intercept Elasticity Urban Dummy R2

Cereals 2.21*** 0.38*** 0.04

N.A.

Pulses -3.90*** 1.06*** 0.07

Milk -7.60*** 1.75*** -0.02

Edible oil -7.20*** 1.62*** 0.23***

MFC -4.27*** 1.17*** -0.08

Vegetables -2.23*** 0.92*** 0.07** 0.95

Sugar -3.11*** 0.80*** 0.11*** N.A.

Total Food 0.02 0.91*** 0.07***




The expenditure elasticities of different food items like cereals, pulses, milk, edible oil,

meat, fish & chicken, vegetables and sugar have been found to be 0.38, 1.06, 1.75, 1.62,

1.17, 0.92 and 0.80 respectively. So, the elasticities of milk, edible oil and meat, fish &

chicken have been noted to be greater than one and for cereals, pulses, vegetables and

sugar it is less than one. The expenditure elasticity has been found to be highest for milk

and the lowest for cereals.

The coefficients of urban dummy have been found to be statistically significant for edible

oil, vegetables, sugar and total food. These coefficients are positive which imply that the

rural people elasticities of these food items are higher than urban people.

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4.2.4.4 Expenditure Elasticities of Selected Food Items for „Rich‟ Families


Elasticities of Selected Food Items- „Rich‟ Families


(P value)

Breusch-Pagan

test (P value) Hausman test

(P value) Fixed Effect/Random

Effect/Pooled OLS

Cereals 0.000 0.000 0.050 Fixed Effects

Pulses 0.000 0.000 0.020 Fixed Effects



Meat, fish &

chicken




Total Food 0.000 0.000 0.001 Fixed Effects


For the ‗rich‘ families, the random effects model have been selected for the food items

like milk, edible oil, meat, fish & chicken, vegetables and sugar and for the rest of food

items the fixed effects model has been applied for deriving the expenditure elasticities.

Table 4.2.4.4 (b) Expenditure Elasticities of Selected Food Items in India -„Rich‟ Families


Dummy

R2

Cereals 6.02*** -0.16** -0.10*** 0.88

Pulses 4.95*** -0.23** -0.29*** 0.84

Milk 1.89** 0.37*** -0.30***

N.A.

Edible oil -3.35*** 0.96*** 0.16***

Meat, fish &

chicken

1.92** 0.26** -0.21***

Vegetables 2.21*** 0.27*** -0.13***

Sugar 0.45 0.32*** 0.07

Total Food 3.90*** 0.34*** -0.11*** 0.95




In the case of rich class families, the expenditure elasticities for cereals and pulses have

been found to be negative (i.e. -0.16 and -0.23 respectively), which implies that with

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increase in total expenditure proportionate share on cereals and pulses has been declined.

The expenditure elasticities of other all food items have been found to be less than one.

Hence, it can be said that the food items like cereals and pulses are staple food items for

richer families as well as other all food items are also less responsive to change in their

income.

The coefficient of urban dummy has been found to be statistically significant for all food

items excepting sugar. These coefficients are negative for food items like cereal, pulses,

milk meat, fish & chicken and vegetables which imply that the elasticities of richer

families of the rural area are lower than urban areas.

On the basis of above discussions it is concluded that the consumption of selected food

items for families with lower income is greater influenced by random factors than

families with higher income.

4.2.4.5 Comparison of Expenditure Elasticities of Selected Food Items for

Different Class of Families

Table 4.2.4.5 Comparison of Expenditure Elasticities of Selected Food Items for Different

Classes of Families in India

Food Items Expenditure Elasticities

„Very Poor‟ „Poor‟ „Non-Poor‟ „Rich‟

Cereals 0.78*** 0.58*** 0.38*** -0.16**

Pulses 0.46*** 1.05*** 1.06*** -0.23**

Milk 1.07*** 1.87*** 1.75*** 0.37***

Edible oil 1.59*** 1.58*** 1.62*** 0.96***

Meat, fish &

chicken 0.93** 1.18*** 1.17*** 0.26**

Vegetables 1.07*** 1.04*** 0.92*** 0.27***

Sugar 0.74*** 1.18*** 0.80*** 0.32***

Total Food 0.91*** 0.99*** 0.91*** 0.34***


*** 0.01 Significance level, ** 0.05 Significance Level N.A. - R2 is not applicable as Random Effect is selected

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On the basis of above table one can say that the expenditure elasticities of selected food

commodities are positive and decline with increase in household income. The

expenditure elasticities are much higher for poor households than for richer households.

The consumption food items like milk, edible oil, meat, fish & chicken and vegetables

are highly responsive to change in the income of the ‗very poor‘, ‗poor‘ and ‗non-poor‘

families. Therefore, when the income level of these families increases the demand for the

food items like milk, edible oil, meat, fish & chicken and vegetables will increase at

higher rate in future.

4.3 Demand Projection of Major Food Items in India

The estimation of probable future demand for food items is essential for planners. It is

required to design major economic policies like food security, agricultural schemes,

import and exports of agricultural output etc… In this section the researcher has tried to

project the probable demand for major food items on the basis of projected population,

future per capita income growth and expenditure elasticities of these food items. The

projected demand of major food items has been calculated by using the International

Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT). This

demand projection model has also been used by Surbhi Mittal (2008) in Working Paper

No. 209, entitled ―Demand and Supply Trends and Projections of Food in India‖, Indian

Council for Research on International Economic Relations (ICSSR). The demand

projection model is as follows;

Dt = d0 * Nt (1+y * e)t

Where, Dt = household demand of a commodity in year t;

d0 = per capita demand of the commodities in the base year;

y = growth in per capita income; e is the expenditure elasticity of demand

for the commodity;

Nt = the projected population in year t.

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For the calculation of probable future demand for major food items, researcher requires

the data on projected population and average per capita income growth for projection

years. The data on projected population has been taken from the publication entitled ―The

Future Population of India - A Long-range Demographic View‖ published by the

Population Foundation of India in 2007. The projected population in India is given in the

following table;

Table 4.3.1 Projected Population in India (In millions)

Year Total Population

Rural

Population

Urban

Population

% of Urban

Population

in Total

2011

1203.71

(1.45)

812.51

(0.87)

391.21

(1.82) 32.50

2021

1380.21

(1.28)

869.02

(0.65)

511.19

(2.35) 37.04

2031

1546.16

(1.07)

831.65

(-0.45)

714.51

(2.85) 46.21

2041

1695.05

(0.88)

788.40

(-0.55)

906.66

(2.12) 53.49

2051

1823.52

(0.70)

753.53

(-0.46)

1070.01

(1.53) 58.68 Source: The Future Population of India - A Long-range Demographic View‖ , The Population Foundation of India, 2007.

Note: (1) The Projected Rural and Urban Population is calculated on the base of estimated urban population share in total population given in 2011 census provisional. (2) Figure in brackets‘ indicates the average annual growth rate.

Chart no. 4.1 Projected Annual Average Growth Rate in Rural, Urban and Total

Population of India

-1

-0.5

0

0.5

1

1.5

2

2.5

3

3.5

2011 2021 2031 2041 2051

Gro

wth

Rat

e (%

)

Years

TOTAL RURAL URBAN

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The Population Foundation of India had projected population of 1203.71 million in 2011,

which will reach 1823.53 million in 2051. The decadal growth of the population is

assumed to be 14.55 % during 2001-2011, which will decline over period of time and

will come down to 7.05 % during the decade of 2041 to 2051. It can also be seen that the

urban population will increase at an increasing rate upto 2041 and then it will increase at

a decreasing rate. The increase is mainly due to the high rate of migration (Urbanization)

from rural to urban areas. It is estimated that over a period of time the urban population

share in total population will increase and reach 58.68% of the total population in 2051.

Table 4.3.2 Alternative Per Capita Income Growth Assumptions for Demand Projections

Year Low Actual High

2011 2.05 4.05 5.55

2021 2.22 4.22 5.72

2031 2.43 4.43 5.93

2041 2.62 4.62 6.12

2051 2.80 4.80 6.30

Source: Calculated by researcher from the data of GDPfc at constant price available on RBI website.

The growth rates in per capita income under alternative scenario have been

worked out by subtracting the population growth from income growth. This is then used

for projecting the per capita consumption of different food items.

120

Table 4.3.3 Projected Demand for Selected Food Items in India (On the Assumption of Alternative Per Capita Income Growths)

Food

Items

Projected Demand for Food Items (in MMT) Annual Growth Rate

PCI

Growth 2011 2021 2031 2041 2051

2011-

2021

2021-

2031

2031-

2041

2041-

2051

Cereals

Actual 373.80 439.79 507.30 571.35 629.24 1.50 1.33 1.12 0.92

Low 263.01 312.76 364.99 415.33 461.40 1.59 1.43 1.21 1.00

High 456.89 535.07 614.03 688.36 755.12 1.46 1.29 1.08 0.88

Pulses

Actual 50.37 59.75 69.54 78.95 87.53 1.57 1.41 1.19 0.98

Low 30.61 37.09 44.16 51.12 57.60 1.75 1.60 1.36 1.12

High 65.19 76.74 88.57 99.81 109.98 1.51 1.34 1.13 0.92

Milk

Actual 367.58 436.41 508.40 577.65 640.91 1.58 1.42 1.20 0.99

Low 219.70 266.83 318.44 369.40 416.87 1.77 1.62 1.38 1.14

High 478.50 563.59 650.87 733.84 808.94 1.51 1.34 1.13 0.93

Sugar

Actual 48.60 57.60 66.97 75.97 84.17 1.56 1.40 1.18 0.97

Low 30.03 36.31 43.13 49.83 56.05 1.73 1.58 1.34 1.11

High 62.52 73.56 84.86 95.58 105.26 1.50 1.33 1.12 0.92

Edible oil

Actual 48.93 58.02 67.51 76.61 84.92 1.57 1.40 1.19 0.98

Low 29.95 36.26 43.13 49.88 56.16 1.74 1.59 1.35 1.12

High 63.17 74.35 85.79 96.66 106.48 1.50 1.33 1.12 0.92

Meat, fish

& chicken

Actual 34.60 41.02 47.71 54.13 59.98 1.56 1.40 1.19 0.98

Low 21.28 25.74 30.59 35.37 39.80 1.73 1.59 1.35 1.11

High 44.84 52.77 60.89 68.59 75.56 1.50 1.33 1.12 0.92

Vegetables

Actual 527.40 626.19 729.53 828.95 919.77 1.58 1.42 1.20 0.99

Low 314.85 382.47 456.51 529.64 597.77 1.77 1.62 1.38 1.14

High 686.82 808.98 934.30 1053.43 1161.27 1.51 1.34 1.13 0.93 Source: Calculated by Researcher

121

In the above table, the projected demands for various food items under the alternative per

capita income growth assumption are given. There are three alternative per capita income

growth assumptions here. In all assumptions, the demand for various food items will

increase in future at all India level. But the rate of increase in demand for these food

items is going to decline in future. If we assume that the per capita income will increase

by the actual growth rate of per capita income, cereals demand will increase by 1.50%,

1.33%, 1.12% and 0.92% per annum during the period of 2011 to 2021, 2021 to 2031,

2031 to 2041 and 2041 to 2051 respectively. However, if it is assumed that the per capita

income will increase by lower rates given earlier, the demand for cereals will increase by

1.59, 1.43, 1.21 and 1.00 per annum respectively. And if we assume that the per capita

income will increase by higher rates given earlier, the demand for cereals will be

increased by 1.46, 1.29, 1.08 and 0.88 percent per annum respectively. So, one can say

that the demand for cereals will increase in the projected time period in physical term but

it will increase at a diminishing rate. The similar pattern in growth of the projected

demand for various food items have been reported above. If we assume that per capita

income will increase at low rate, the demand will increase faster than actual growth and

high growth rate assumptions. However, when we assume a high growth rate, the growth

rate of projected demand is less than low growth assumption and well as actual growth

assumption. So the rate of demand for various food items will be higher if the economy

grows at a lower rate. The projected demand for pulses, milk and vegetables will increase

at a higher rate compared to the other food items. It is due to high elasticities of demand

for these food items. The growth rate of demand for various food items will decline over

a period of time and it can be explained by the decrease in the population growth rate in

future. But when we consider the total demand of various food items in quantity terms, it

will be increase in future due to increase in total population.

122

Table 4.3.4 Projected Demand for Selected Food Items in Rural India (On the Assumption of Alternative Per Capita Income Growths)


PCI

Growth 2011 2021 2031 2041 2051

2011-

2021

2021-

2031

2031-

2041

2041-

2051

Cereals

Actual 328.67 361.68 357.46 349.06 342.31 0.91 -0.12 -0.24 -0.20

Low 220.883 246.395 247.135 244.473 242.347 1.035 0.030 -0.109 -0.088

High 409.51 448.14 440.20 427.50 417.28 0.86 -0.18 -0.30 -0.24

Pulses

Actual 32.23 35.68 35.49 34.85 34.34 0.97 -0.05 -0.18 -0.15

Low 19.428 21.985 22.386 22.432 22.472 1.163 0.179 0.021 0.018

High 41.83 45.95 45.32 44.17 43.25 0.90 -0.14 -0.26 -0.21

Milk

Actual 160.42 177.17 175.81 172.28 169.46 0.95 -0.08 -0.20 -0.17

Low 100.965 113.585 114.953 114.596 114.319 1.111 0.119 -0.031 -0.024

High 205.01 224.86 221.44 215.55 210.81 0.88 -0.15 -0.27 -0.22

Sugar

Actual 28.62 31.63 31.39 30.78 30.28 0.95 -0.07 -0.20 -0.16

Low 17.895 20.150 20.412 20.365 20.329 1.119 0.128 -0.023 -0.018

High 36.67 40.23 39.63 38.58 37.74 0.89 -0.15 -0.27 -0.22

Edible oil

Actual 31.73 35.13 34.94 34.32 33.82 0.97 -0.05 -0.18 -0.15

Low 19.130 21.647 22.042 22.087 22.126 1.163 0.179 0.021 0.018

High 41.19 45.24 44.62 43.49 42.58 0.90 -0.14 -0.26 -0.21

Meat, fish

& chicken

Actual 28.93 32.07 31.95 31.42 30.99 0.98 -0.04 -0.17 -0.14

Low 16.991 19.297 19.724 19.827 19.913 1.195 0.216 0.052 0.043

High 37.89 41.65 41.12 40.11 39.30 0.90 -0.13 -0.25 -0.21

Vegetables

Actual 394.91 437.85 436.29 429.13 423.39 0.98 -0.04 -0.17 -0.14

Low 230.658 262.165 268.167 269.750 271.057 1.202 0.224 0.059 0.048

High 230.66 262.16 268.17 269.75 271.06 0.90 -0.13 -0.25 -0.21 Source: Calculated by Researcher

123

On the basis of the above table, it can be that, in rural area the demand for various food

items will increase upto year of 2021 in quantity terms but after 2021 the demand for

various food items is likely to decline. If we assume the present per capita income

growth, the demand for various food items will decline for all food items even then it will

come down to negative growth which implies decrease in quantity term also. If the

economy grows at a higher rate, the demand for various food items will increase at a

faster rate than the present growth rate and lower growth rate. It is due to the high

expenditure elasticity of demand for various food items in rural area. It is estimated that

in future the demand for various food items will decline in quantity term also which is

due to decrease in the rural population in future the rural population will quickly shift to

urban areas for better prospects, search of employment and other reasons.

124

Table 4.3.5 Projected Demand for Selected Food Items in Urban India (On the Assumption of Alternative Per Capita Income Growths)


PCI

Growth 2011 2021 2031 2041 2051

2011-

2021

2021-

2031

2031-

2041

2041-

2051

Cereals

Actual 91.06 121.66 174.42 226.57 272.92 2.52 3.02 2.30 1.70

Low 67.81 91.28 131.95 172.68 209.32 2.57 3.08 2.36 1.75

High 108.50 144.45 206.26 266.98 320.61 2.49 3.00 2.27 1.67

Pulses

Actual 12.15 16.36 23.66 30.96 37.53 2.57 3.08 2.36 1.75

Low 7.98 10.91 16.03 21.28 26.11 2.68 3.20 2.47 1.85

High 15.29 20.46 29.38 38.22 46.10 2.53 3.04 2.31 1.71

Milk

Actual 148.32 200.82 292.12 384.43 468.16 2.61 3.13 2.40 1.79

Low 87.43 121.25 180.90 243.30 301.61 2.79 3.30 2.56 1.93

High 193.99 260.49 375.53 490.27 593.08 2.55 3.06 2.34 1.73

Sugar

Actual 14.91 20.11 29.13 38.20 46.38 2.59 3.10 2.37 1.76

Low 9.44 12.96 19.14 25.53 31.43 2.72 3.23 2.50 1.88

High 19.01 25.46 36.62 47.70 57.59 2.54 3.05 2.32 1.72

Edible oil

Actual 12.10 16.28 23.53 30.77 37.29 2.57 3.08 2.36 1.75

Low 8.02 10.95 16.07 21.32 26.13 2.68 3.19 2.46 1.84

High 15.16 20.28 29.12 37.87 45.66 2.52 3.03 2.31 1.71

Meat, fish

& chicken

Actual 12.74 17.22 25.01 32.86 39.96 2.60 3.11 2.39 1.78

Low 7.77 10.72 15.92 21.32 26.35 2.76 3.27 2.53 1.91

High 16.48 22.10 31.83 41.51 50.17 2.55 3.06 2.33 1.73

Vegetables

Actual 117.77 158.70 229.68 300.88 365.03 2.58 3.09 2.37 1.76

Low 117.77 158.70 229.68 300.88 365.03 2.58 3.09 2.37 1.76

High 149.14 199.69 286.96 373.57 450.82 2.53 3.04 2.32 1.71 Source: Calculated by Researcher

125

In the urban area, the demand for various food items are projected to increase in quantity

terms over a period of time in future. However the rate of increase in the demand of

various food items has been found to be higher upto 2031 then it is likely to increase at a

decreasing rate. The huge increase in demand for various food items is due to rapid

increase in population and lower expenditure elasticities of demand for various food

items in urban area than rural. The population growth rate will come down to less than

one at all India level in future. This growth rate will be achieved due to decline growth

rate in rural population. The urban population will increase by more than 1 percent

annually in future due to urbanization. The transformation of population from rural to

urban areas leads to increase in the demand for major food items in urban areas.

4.4 Supply Projection in India

In the previous section of this chapter the researcher has concluded that the demand for

the various food items would increase. It is essential to predicate the future supply of

different food items for making the various strategies relating to food security in country.

This is especially true if one wants to plan for future, so that the gap between demand for

and supply of these commodities is bridged through several plans action. The supply

projections have been made by using a straightforward approach. As used in other studies

by Mittal (2008) and Sekhar (2008) in India and Abdel Rahman (1998) in Sudan, supply

projections have been made assuming the yield growths to be same as in the past decade.

Supply projections have been made for the years 2021, 2031, 2041 and 2051 using the

yield growth for the most recent period of 2004-05 to 2011-12 and taking 20011-12 as

the base year for area and production.

The following formula has been used for supply projection;

Yt = Y0*(1+r)t

Where, Yt = Year of Projection of harvest area and yield of food items,

Y0 = Harvest area and yield of food items in base year,

126

r = average annual growth of harvest area and yield of food items,

t = numbers of years under projection

After the calculation of projected harvest area for food items and yield of food items,

both projected values has been multiplied to arrive at the projected production of specific

food items.

The data on the total production, total harvested area and productivity of various food

items is given in following table.

127

Table 4.4.1 Average Annual production of Selected Food Items in India

Food Items

Area Harvested ('000) Yield (Kg/Hact.) Production (MMT)

1995-96 to

1999-2000

2000-01 to

2004-05

2005-06 to

2011-12

1995-96

to

1999-

2000

2000-01 to

2004-05

2005-2006 to

2011-12

1995-96 to

1999-2000

2000-01 to

2004-05

2005-06 to

2011-12

Cereals 101496.2

(0.30)

98427.66

(-1.10)

99930.28

(0.40)

1844

(2.00)

1893

(-1.00)

2153

(3.00)

187.14

(2.10)

186.48

(-2.10)

215.16

(3.60)

Pulses 22483.60

(-2.3)

21814.76

(1.2)

23636.63

(0.8)

616.80

(-0.12)

581.34

(-3)

644.72

(3.00)

13.86

(-2.4)

12.72

(-2.5)

15.27

(3.4)

Sugar

Cane#

4094.63

(0.7)

4169.50

(-0.2)

4702.88

(3.8)

69941.46

(2.08)

64730.98

(-1.99)

68237.67

(1.36)

286.29

(2.1)

270.30

(-5.1)

323.66

(4.9)

Oil Seeds##

25715.08

(-2.00)

23616.24

(2.10)

26873.89

(-0.70)

875.55

(-1.00)

872.47

(-2.30)

1046.44

(2.67)

22.53

(-5.00)

20.70

(-0.60)

28.13

(1.70)

Vegetables*

NA

6268.5

(2.9)

7847.6

(4.5) NA

14463.75

(1.33)

16150.25

(2.67)

NA

90.75

(4.1)

127.03

(7.1)

Sources: Calculated by researcher from various tables of (1) Agricultural Statistics at a glance, 2012, Directorate of Economics and statistics, Department of Agriculture and Cooperation (2) http://data.gov.in/dataset/all-india-and-state-wise-area-and-production-vegetables

Note:- (1) #from the total production of sugar cane, about 10.1% is the recovery ratio.

## from the total production of Oilseed the recovery ratio is about 33.9%

(2) Figure in brackets indicates average annual growth rate

N.A.- Data are not available

http://data.gov.in/dataset/all-india-and-state-wise-area-and-production-vegetables

128

On the basis of above table we can say that the area under cultivation for food items like

cereals, pulses, sugarcane and oilseed has fluctuated over a period of time. Similarly the

production and productivity of these food items have also fluctuated over a period of

time. However, during last seven years the total cultivation area, production and

productivity of these food items have increased which give the positive sign for future

production of these food items. But we know that the land is a constant factor of

production, as well as due to the industrialization the utilization of land will be more for

industrial sector and also that land under cultivation is going to be used for industrial

sector in India. So, it is necessary to focus on the use of land for farming and also try to

increase productivity of land.

Table 4.4.2 Average Growth of Area under Cultivation, Production and Yield of

Selected Food Items During the Period of 2005-06 to 2011-12

Items Area Production Yield

Cereals 0.4 3.6 3.26

Pulses 0.8 3.4 2.62

Sugarcane 3.8 4.9 1.36

Oil seeds -0.7 1.7 2.67

Vegetables 4.5 7.1 2.67 Source: Calculated by Researcher

It is clear from the above table that in the last seven years, the areas under cultivation,

productions and productivities of food items like cereals, pulses, sugar cane, oil seeds and

vegetables have increased. In the case of oil seeds the area under cultivation has

decreased but due to higher productivity it was possible to have high production. The

area under cultivation for vegetables and sugarcane has increased faster than other items

i.e. the area under cultivation for vegetables has increased annually at 4.5% and for

sugarcane it has increased at 3.8%. The productivity of the cereals has been found to be

higher followed by that ofoil seeds. Due to higher increase in the cultivated area under

vegetables and sugarcane production the future production of these items is estimated to

increase faster than other food items.

129

Table 4.4.3 Assumption of Maximum Land Covered under Harvesting for Selected Food

Items in Future

Food Items Average

Growth Rate

Assumption

(000‟ Hecters)

(Upto 2031)

Cereals 0.4 150000

Pulses 0.8 35000

Sugarcane 3.8 8000

Oil Seeds -0.7 24474

Vegetables 4.5 16500

Source: Calculated by Researcher

The land is the fixed factor of production, so when we estimate the projected harvest area

for different food items it should be kept in mind that land cannot be increased over a

period of time. Therefore, we assume that at certain point the land under cultivation of

different food items will become a constant. The researcher has assumed this on the basis

of total available land for agriculture and pattern of this land under the cultivation of

different food items.

Table 4.4.4 Projected Supply of Selected Food Items in India (in MMT)

Items

Scenerio-1

(Area under harvesting growth is

as table no. 4.4.2)

Scenerio-2

(Area under harvesting growth

is 0.0%)

2021 2031 2041 2051 2021 2031 2041 2051

Cereals 350.23 398.7 418.26 438.78 254.12 266.58 279.66 293.37

Pulses 30.86 40.85 52.81 68.26 22.09 28.55 36.91 47.71

Sugar 61.45 77.23 88.75 101.98 43.27 49.72 57.14 65.66

Edible oil 12.27 16.01 20.9 27.28 13.81 18.03 23.53 30.72

Vegetables 283.16 484.98 633.03 826.29 191.29 249.69 325.92 425.42


The projected supplies of the different food items are given in the above table. These

projections are made for two scenarios, first assumes that growth of area under harvesting

is as table no. 4.4.2 however at certain level the harvesting area have become a constant.

The second scenario is based on the assumption that there is no change in harvesting area

for different food items.

130

According to scenario one, the supply of the cereals is estimated to be 350.23 million

metric tons in 2021, which will increase and reach to 438.78 million metric tons in 2051.

The pulses, sugar, edible oil and vegetables supply is estimated to be 30.86, 61.45, 12.27

and 283.86 million tons in 2021 respectively, and will increase to 68.26, 101.98, 27.28

and 826.29 million tons in 2051 respectively.

On the basis of second scenario, the supplies of the cereals, pulses, sugar, edible oil and

vegetables are estimated to be 254.12, 22.09, 49.72, 13.81 and 191.29 million tons in

2021, which will increase to 293.37, 47.71, 65.66, 30.72 and 425.42million tons in 2051

respectively.

4.5 Projected Demand Supply Gap

When, the researcher has compared the projected demand for various food items and

supply of these food items, it has been observed that there will be wide gap between the

two in future. The availability of the supply will be smaller than demand for various food

items. The researcher has estimated probable demand and supply gap of selected food

items. This estimation is made for two scenarios of supply projections under the three

alternative assumptions of per capita income growth of projected demand.

4.5.1 Projected Demand and Supply Gap of selected Food Items

– If Economy Grows at the Actual Rate

The projected demand and supply gap of selected food items under the assumption of per

capita income will grow at actual rate given in the following tables;

Table 4.5.1 (a) Demand and Supply Gap (If Economy grows at the Actual Rate)

Food Items

Scenario-1

2021 2031 2041 2051

Cereals -85.94 -104.34 -148.22 -185.02

Pulses -28.48 -28.21 -25.59 -18.67

Sugar 4.92 11.51 14.22 19.41

Edible oil -46.26 -52.1 -56.4 -58.4

Vegetables -333.59 -233.47 -183.23 -79.31


131

Table 4.5.1 (b) Demand and Supply Gap (If Economy grows at the Actual Rate)

Food Items

Scenario-2

2021 2031 2041 2051

Cereals -182.05 -236.46 -286.82 -330.43

Pulses -37.25 -40.51 -41.49 -39.22

Sugar -13.26 -16 -17.39 -16.91

Edible oil -44.72 -50.08 -53.77 -54.96

Vegetables -425.46 -468.76 -490.34 -480.18


If we assume that the economy will grow at the actual rate, the projected data of demand

and supply gap of various food items given in the above tables and graphs no. 4.2 to 4.9

shows that according to the scenario-1, excepting sugar, there will be deficit in the

availability of food items like cereals, pulses and edible oil in all the projected years. The

demand and supply gap of cereals has been estimated to be 85.94 million tons in 2021,

which will increase to 185.02 million tons in 2051. The similar situation has been

observed for demand and supply of edible oil i.e. the gap between demand and supply of

edible oil has been estimated to 46.26 million tons in 2021, which will increase to 58.4

million tons in 2051. However in the case of pulses and vegetables, the gap between

projected demand and supply will reduce over a period of time i.e. this gap has been

estimated to 28.48 million tons for pulses in 2021, which will reduce and come down to

18.67 million tons in 2051. For vegetables the projected demand and supply gap has been

estimated to 333.59 million tons in 2021, which will reduce and come down to 79.31

million tons in 2051. It is due to the high growth rate and production of this food item.

However according to the second scenario the sugar supply also will be less than its

demand and therefore there will be a gap in demand and supply of sugar also. In this case

for other food items the deficit will be very huge.

132

Chart-4.2 Projected Demand and Supply of Selected Food Items in the year of 2021

(PCI grows at actual rate and Production of Food Items accroding to Scenerio - 1)


(PCI grows at actual rate and Production of Food Items accroding to Scenerio – 1)

436.17

59.34 56.53 58.53

616.75

350.23

30.8661.45

12.27

283.16

Cereals Pulses Sugar Edible Oil Vegetables

Demand Supply

503.04

69.06 65.72 68.11

718.45

398.7

40.85

77.23

16.01

484.98


Demand Supply

133





566.48

78.4 74.53 77.3

816.26

418.26

52.8188.75

20.9

633.03


Demand Supply

623.8

86.93 82.57 85.68

905.6

438.78

68.26101.98

27.28

826.29


Demand Supply

134





436.17

59.34 56.53 58.53

616.75

254.12

22.0943.27

13.81

191.29


Demand Supply

503.04

69.06 65.72 68.11

718.45

266.58

28.5549.72

18.03

249.69


Demand Supply

135





566.48

78.4 74.53 77.3

816.26

279.66

36.9157.14

23.53

325.92


Demand Supply

623.8

86.93 82.57 85.68

905.6

293.37

47.71 65.6630.72

425.42


Demand Supply

136


– If Economy Grows at the lower Rate

Suppose the per capita income grows at the lower rate, the projected gap between

demand and supply is given in following tables;

Table4.5.2 (a) Demand and Supply Gap (If Economy will grow at Lower Rate)

Food Items

Scenario-1

2021 2031 2041 2051

Cereals 39.38 36.05 5.69 -19.45

Pulses -6.02 -3.05 2 11.02

Sugar 25.7 34.79 39.74 46.87

Edible oil -24.26 -27.44 -29.37 -29.33

Vegetables -94.34 34.55 110.59 236.78


Table4.5.2 (b) Demand and Supply Gap (If Economy will grow at Lower Rate)

Food Items

Scenario-2

2021 2031 2041 2051

Cereals -56.73 -96.07 -132.91 -164.86

Pulses -14.79 -15.35 -13.9 -9.53

Sugar 7.52 7.28 8.13 10.55

Edible oil -22.72 -25.42 -26.74 -25.89

Vegetables -186.21 -200.74 -196.52 -164.09


The data show that, according to scenario-1 there will be a gap between demand and

supply for edible oil in all the projected years. This gap has been estimated to 24.26

million tons in 2021 which will increase to 29.33 million tons in 2051. In the case of

pulses it has been estimated that in 2021, the demand will be greater than its supply. But

after this year, between 2031 and 2051 the estimated demand will be less than its supply.

The probable demand and supply gap of vegetables has been estimated to be negative

only for the year of 2021 then this gap has been estimated to be positive.

137

But according to the scenario-2, there will be a gap between demand and supply for all

food items excepting sugar. The probable gap between demand for and supply of

selected food items are also illustrated by graphs no. 4.10 to 4.17.


(PCI grows at lower rate and Production of Food Items accroding to Scenerio - 1)



310.85

36.88 35.75 36.53

377.5

350.23

30.86

61.45

12.27

283.16


Demand Supply

362.65

43.9 42.44 43.45

450.43

398.7

40.85

77.23

16.01

484.98


Demand Supply

138


(PCI grows at lower rate and Production of Food Items accroding to Scenerio - 1



412.57

50.81 49.01 50.27

522.44

418.26

52.81

88.75

20.9

633.03


Demand Supply

458.23

57.24 55.11 56.61

589.51

438.78

68.26101.98

27.28

826.29


Demand Supply

139





310.85

36.88 35.75 36.53

377.5

254.12

22.09

43.27

13.81

191.29


Demand Supply

362.65

43.9 42.44 43.45

450.43

266.58

28.55

49.72

18.03

249.69


Demand Supply

140





412.57

50.81 49.01 50.27

522.44

279.66

36.9157.14

23.53

325.92


Demand Supply

458.23

57.24 55.11 56.61

589.51

293.37

47.7165.66

30.72

425.42


Demand Supply

141


– If Economy Grows at the Higher Rate

At last if we assume that per capita income will grow at higher rate, then the projected

demand and supply gap is given in the following tables.

Table 4.5.3 (a) Demand and Supply Gap (If Economy will grow at Higher Rate)

Food Items

Scenario-1

2021 2031 2041 2051

Cereals -179.93 -209.63 -263.65 -309.2

Pulses -45.33 -47.09 -46.29 -40.93

Sugar -10.66 -5.94 -4.92 -1.18

Edible oil -62.77 -70.58 -76.67 -80.21

Vegetables -513.02 -434.47 -403.59 -316.38


Table 4.5.3 (a) Demand and Supply Gap (If Economy will grow at Higher Rate)

Food Items

Scenario-2

2021 2031 2041 2051

Cereals -276.04 -341.75 -402.25 -454.61

Pulses -54.1 -59.39 -62.19 -61.48

Sugar -28.84 -33.45 -36.53 -37.5

Edible oil -61.23 -68.56 -74.04 -76.77

Vegetables -604.89 -669.76 -710.7 -717.25


On the basis of projected data of demand and supply of various food items under the

assumptions of higher per capita income growth and alternative area harvesting

growth rates, the researcher can say that the gap between demand and supply of

various food items implies that a deficit will arise in the availability of supply of all

food items in all projected years. The deficit in the supply of cereals, pulses edible oil

and vegetables has been found to be huge compared to other food items.

142


(PCI grows at higher rate and Production of Food Items accroding to Scenerio – 1)



530.16

76.19 72.11 75.04

796.18

350.23

30.8661.45

12.27

283.16


Demand Supply

608.33

87.94 83.17 86.59

919.45

398.7

40.85

77.23

16.01

484.98


Demand Supply

143





681.91

99.1 93.67 97.57

1036.62

418.26

52.8188.75

20.9

633.03


Demand Supply

747.98

109.19 103.16 107.49

1142.67

438.78

68.26101.98

27.28

826.29


Demand Supply

144





530.16

76.19 72.11 75.04

796.18

254.12

22.0943.27

13.81

191.29


Demand Supply

608.33

87.94 83.17 86.59

919.45

266.58

28.5549.72

18.03

249.69


Demand Supply

145





681.91

99.1 93.67 97.57

1036.62

279.66

36.9157.14

23.53

325.92


Demand Supply

747.98

109.19 103.16 107.49

1142.67

293.37

47.7165.66

30.72

425.42


Demand Supply

146

On the basis of projected data of demand and supply of various food items under

alternative per capita income growth assumption and alternative area harvesting

growth assumption, one may conclude that if the per capita income grows at actual

and lower rates and harvesting growth rate according to table no. 3.4.1, there will be

no more shortage of all food items excepting edible oil but if per capita income grows

at higher rate there will be a huge shortage of all the food items. However, under the

assumption of area harvesting growth rate is zero and under the assumption of

alternative per capita income growth rate, the gap between demand and supply of

various food items implies that there will be a huge demand-supply gap for cereals in

future. In the case of other food items this gap will be there but it will not be huge. So

the policy makers should focus on increasing the production of cereals by various

ways like increase in productivity of land, through better irrigation facilities and

increased use of fertilizers, better utilization of land using other resources, adoption of

the modern technology, multiple cropping pattern etc.. The other alternative is to

design the import and export policy of these food items in future, so as to bridge these

gaps.

chapter-4 expenditure elasticity and demand...

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