Post on 11-Jul-2018
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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
If the p-value of this test is less than 0.05, the null hypothesis will be rejected, that means
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
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 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
Source: Estimated by Researcher
*** 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)
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 0.003 0.341 0.000 Fixed Effect
Pulses 0.042 0.083 0.000 Fixed Effect
Milk 0.042 0.169 0.053 Fixed Effect
Edible oil 0.000 0.000 0.001 Fixed Effect
Meat, Fish
& Chicken
0.763 0.385 0.660 Pooled OLS
Vegetables 0.015 0.118 0.005 Fixed Effect
Sugar 0.077 0.173 0.970 Pooled OLS
Total Food 0.000 0.000 0.000 Fixed Effect
Source: Estimated by Researcher
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
Source: Estimated by Researcher
*** 0.01 Significance level, ** 0.05 Significance Level
N.A. - R2 is not applicable as Random Effect is selected
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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
N.A. - R2 is not applicable as Random Effect is selected
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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)
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 0.005 Fixed Effect
Pulses 0.033 0.533 0.015 Fixed Effect
Milk 0.000 0.000 0.761 Random Effects
Edible oil 0.000 0.000 0.005 Fixed Effect
Meat, Fish
& Chicken
0.000 0.000 0.999 Random Effects
Vegetables 0.000 0.000 0.000 Fixed Effect
Sugar 0.000 0.000 0.999 Random Effects
Total Food 0.000 0.000 0.017 Fixed Effect
Source: Estimated by Researcher
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
Source: Estimated by Researcher
*** 0.01 Significance level, ** 0.05 Significance Level
N.A. - R2 is not applicable as Random Effect is selected
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
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 0.931 Random Effects
Pulses 0.000 0.000 0.521 Random Effects
Milk 0.000 0.000 0.969 Random Effects
Edible oil 0.000 0.000 0.963 Random Effects
Meat, Fish
& Chicken
0.000 0.000 0.688 Random Effects
Vegetables 0.000 0.000 0.494 Random Effects
Sugar 0.000 0.000 0.908 Random Effects
Total Food 0.000 0.003 0.436 Random Effects
Source: Estimated by Researcher
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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
Food Items Intercept Elasticity Urban
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**
Source: Estimated by Researcher
*** 0.01 Significance level, ** 0.05 Significance Level *0.10 Significance Level
N.A. - R2 is not applicable as Random Effect is selected
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
Table 4.2.4.2 (a) Selection of Panel Regression Model for Calculation of Expenditure
Elasticities of Selected Food Items - „Poor‟ Families
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 0.339 Random Effects
Pulses 0.000 0.000 0.299 Random Effects
Milk 0.000 0.000 0.883 Random Effects
Edible oil 0.000 0.000 0.999 Random Effects
Meat, Fish
& Chicken
0.000 0.000 0.120 Random Effects
Vegetables 0.000 0.000 0.914 Random Effects
Sugar 0.000 0.000 0.957 Random Effects
Total Food 0.000 0.000 0.987 Random Effects
Source: Estimated by Researcher
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***
Source: Estimated by Researcher
*** 0.01 Significance level, ** 0.05 Significance Level
N.A. - R2 is not applicable as Random Effect is selected
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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
Table 4.2.4.3 (a) Selection of Panel Regression Model for Calculation of Expenditure
Elasticities of Selected Food Items – „Non-Poor‟ Families
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 0.359 Random Effects
Pulses 0.000 0.000 0.997 Random Effects
Milk 0.000 0.000 0.990 Random Effects
Edible oil 0.000 0.000 0.070 Random Effects
Meat, Fish &
Chicken 0.000 0.000 0.963 Random Effects
Vegetables 0.000 0.000 0.040 Fixed Effects
Sugar 0.000 0.000 0.615 Random Effects
Total Food 0.000 0.000 0.776 Random Effects
Source: Estimated by Researcher
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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***
Source: Estimated by Researcher
*** 0.01 Significance level, ** 0.05 Significance Level
N.A. - R2 is not applicable as Random Effect is selected
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
Table 4.2.4.4 (a) Selection of Panel Regression Model for Calculation of Expenditure
Elasticities of Selected Food Items- „Rich‟ Families
Food Items Joint Test
(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
Milk 0.000 0.000 0.200 Random Effects
Edible oil 0.000 0.000 0.744 Random Effects
Meat, fish &
chicken
0.000 0.000 0.989 Random Effects
Vegetables 0.000 0.000 0.963 Random Effects
Sugar 0.000 0.000 0.090 Random Effects
Total Food 0.000 0.000 0.001 Fixed Effects
Source: Estimated by Researcher
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
Food Items Intercept Elasticity Urban
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
Source: Estimated by Researcher
*** 0.01 Significance level, ** 0.05 Significance Level
N.A. - R2 is not applicable as Random Effect is selected
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***
Source: Estimated by Researcher
*** 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.
118
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
119
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)
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 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)
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 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
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
Source: Calculated by Researcher
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
Source: Calculated by Researcher
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
Source: Calculated by Researcher
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)
Chart-4.3 Projected Demand and Supply of Selected Food Items in the year of 2031
(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
Cereals Pulses Sugar Edible Oil Vegetables
Demand Supply
133
Chart-4.4 Projected Demand and Supply of Selected Food Items in the year of 2041
(PCI grows at actual rate and Production of Food Items accroding to Scenerio – 1)
Chart-4.5 Projected Demand and Supply of Selected Food Items in the year of 2051
(PCI grows at actual rate and Production of Food Items accroding to Scenerio – 1)
566.48
78.4 74.53 77.3
816.26
418.26
52.8188.75
20.9
633.03
Cereals Pulses Sugar Edible Oil Vegetables
Demand Supply
623.8
86.93 82.57 85.68
905.6
438.78
68.26101.98
27.28
826.29
Cereals Pulses Sugar Edible Oil Vegetables
Demand Supply
134
Chart-4.6 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 – 2)
Chart-4.7 Projected Demand and Supply of Selected Food Items in the year of 2031
(PCI grows at actual rate and Production of Food Items accroding to Scenerio – 2)
436.17
59.34 56.53 58.53
616.75
254.12
22.0943.27
13.81
191.29
Cereals Pulses Sugar Edible Oil Vegetables
Demand Supply
503.04
69.06 65.72 68.11
718.45
266.58
28.5549.72
18.03
249.69
Cereals Pulses Sugar Edible Oil Vegetables
Demand Supply
135
Chart-4.8 Projected Demand and Supply of Selected Food Items in the year of 2041
(PCI grows at actual rate and Production of Food Items accroding to Scenerio – 2)
Chart-4.9 Projected Demand and Supply of Selected Food Items in the year of 2051
(PCI grows at actual rate and Production of Food Items accroding to Scenerio – 2)
566.48
78.4 74.53 77.3
816.26
279.66
36.9157.14
23.53
325.92
Cereals Pulses Sugar Edible Oil Vegetables
Demand Supply
623.8
86.93 82.57 85.68
905.6
293.37
47.71 65.6630.72
425.42
Cereals Pulses Sugar Edible Oil Vegetables
Demand Supply
136
4.5.2 Projected Demand and Supply Gap of selected Food Items
– 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
Source: Calculated by Researcher
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
Source: Calculated by Researcher
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.
Chart-4.10 Projected Demand and Supply of Selected Food Items in the year of 2021
(PCI grows at lower rate and Production of Food Items accroding to Scenerio - 1)
Chart-4.11 Projected Demand and Supply of Selected Food Items in the year of 2031
(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
Cereals Pulses Sugar Edible Oil Vegetables
Demand Supply
362.65
43.9 42.44 43.45
450.43
398.7
40.85
77.23
16.01
484.98
Cereals Pulses Sugar Edible Oil Vegetables
Demand Supply
138
Chart-4.12 Projected Demand and Supply of Selected Food Items in the year of 2041
(PCI grows at lower rate and Production of Food Items accroding to Scenerio - 1
Chart-4.13 Projected Demand and Supply of Selected Food Items in the year of 2051
(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
Cereals Pulses Sugar Edible Oil Vegetables
Demand Supply
458.23
57.24 55.11 56.61
589.51
438.78
68.26101.98
27.28
826.29
Cereals Pulses Sugar Edible Oil Vegetables
Demand Supply
139
Chart-4.14 Projected Demand and Supply of Selected Food Items in the year of 2021
(PCI grows at lower rate and Production of Food Items accroding to Scenerio - 2)
Chart-4.15 Projected Demand and Supply of Selected Food Items in the year of 2031
(PCI grows at lower rate and Production of Food Items accroding to Scenerio - 2)
310.85
36.88 35.75 36.53
377.5
254.12
22.09
43.27
13.81
191.29
Cereals Pulses Sugar Edible Oil Vegetables
Demand Supply
362.65
43.9 42.44 43.45
450.43
266.58
28.55
49.72
18.03
249.69
Cereals Pulses Sugar Edible Oil Vegetables
Demand Supply
140
Chart-4.16 Projected Demand and Supply of Selected Food Items in the year of 2041
(PCI grows at lower rate and Production of Food Items accroding to Scenerio - 2)
Chart-4.17 Projected Demand and Supply of Selected Food Items in the year of 2051
(PCI grows at lower rate and Production of Food Items accroding to Scenerio - 2)
412.57
50.81 49.01 50.27
522.44
279.66
36.9157.14
23.53
325.92
Cereals Pulses Sugar Edible Oil Vegetables
Demand Supply
458.23
57.24 55.11 56.61
589.51
293.37
47.7165.66
30.72
425.42
Cereals Pulses Sugar Edible Oil Vegetables
Demand Supply
141
4.5.3 Projected Demand and Supply Gap of selected Food Items
– 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
Source: Calculated by Researcher
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
Source: Calculated by Researcher
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
Chart-4.18 Projected Demand and Supply of Selected Food Items in the year of 2021
(PCI grows at higher rate and Production of Food Items accroding to Scenerio – 1)
Chart-4.19 Projected Demand and Supply of Selected Food Items in the year of 2031
(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
Cereals Pulses Sugar Edible Oil Vegetables
Demand Supply
608.33
87.94 83.17 86.59
919.45
398.7
40.85
77.23
16.01
484.98
Cereals Pulses Sugar Edible Oil Vegetables
Demand Supply
143
Chart-4.20 Projected Demand and Supply of Selected Food Items in the year of 2041
(PCI grows at higher rate and Production of Food Items accroding to Scenerio – 1)
Chart-4.21 Projected Demand and Supply of Selected Food Items in the year of 2051
(PCI grows at higher rate and Production of Food Items accroding to Scenerio – 1)
681.91
99.1 93.67 97.57
1036.62
418.26
52.8188.75
20.9
633.03
Cereals Pulses Sugar Edible Oil Vegetables
Demand Supply
747.98
109.19 103.16 107.49
1142.67
438.78
68.26101.98
27.28
826.29
Cereals Pulses Sugar Edible Oil Vegetables
Demand Supply
144
Chart-4.22 Projected Demand and Supply of Selected Food Items in the year of 2021
(PCI grows at higher rate and Production of Food Items accroding to Scenerio – 2)
Chart-4.23 Projected Demand and Supply of Selected Food Items in the year of 2031
(PCI grows at higher rate and Production of Food Items accroding to Scenerio – 2)
530.16
76.19 72.11 75.04
796.18
254.12
22.0943.27
13.81
191.29
Cereals Pulses Sugar Edible Oil Vegetables
Demand Supply
608.33
87.94 83.17 86.59
919.45
266.58
28.5549.72
18.03
249.69
Cereals Pulses Sugar Edible Oil Vegetables
Demand Supply
145
Chart-4.24 Projected Demand and Supply of Selected Food Items in the year of 2041
(PCI grows at higher rate and Production of Food Items accroding to Scenerio – 2)
Chart-4.25 Projected Demand and Supply of Selected Food Items in the year of 2051
(PCI grows at higher rate and Production of Food Items accroding to Scenerio – 2)
681.91
99.1 93.67 97.57
1036.62
279.66
36.9157.14
23.53
325.92
Cereals Pulses Sugar Edible Oil Vegetables
Demand Supply
747.98
109.19 103.16 107.49
1142.67
293.37
47.7165.66
30.72
425.42
Cereals Pulses Sugar Edible Oil Vegetables
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.