DP2012-14

Demand for Nutrients in India:
An Analysis Based on the 50th, 61st and66th Rou nds of the NSS*
Raghav GAIH A
Ni dhi KAICKER
Katsushi S. IMA I
G ane sh THAPA
June 6, 2012

* The Discussion Papers are a series of research papers in their draft form, circulated to encourage
discussion and comment. Citation and use of such a paper should take account of its provisional character.
In some cases, a written consent of the author may be required.

Edited on 6th June, 2012

Demand for Nutrients in India: An Analysis Based on the 50th, 61st and 66th
Rounds of the NSS
Raghav Gaiha
Formerly, Faculty of Management Studies, University of Delhi
Nidhi Kaicker
School of Business, Public Policy and Social Entrepreneurship, Ambedkar University Delhi
Katsushi S. Imai
School of Social Sciences, the University of Manchester & RIEB, Kobe University
Ganesh Thapa
International Fund for Agricultural Development, Rome
Abstract
In response to the Deaton and Dreze (2009) explanation of a downward shift in the calorie Engel
curve in terms of lower requirements due to health improvements and lower activity levels in
India, we develop an alternative explanation embedded in a standard demand theory framework,with food price and expenditure effects and shifting food and expenditure elasticities. The
analysis is carried out with unit record data for three NSS rounds over the period 1993-2009: 50th,
61st and 66th. There are shifts in demands due to factors other than lower requirements. While anearlier analysis with the 50th and 61st rounds of the NSS over the period 1993-2004 (Gaiha etal.2012) corroborated in part the Deaton-Dreze conjecture of lower requirements, the extended
analysis for 1993-2009 reported here undermines this conjecture as time effects weaken over the
more recent period (2004-2009). But there is also weakening of food price and expenditureelasticities over this period. Closer scrutiny of food preferences and taste for variety is necessary
to understand better nutritional deprivation as also to design more effective policies to ameliorateit.
Key words: calories, protein, fat, food prices, expenditure, India.
JEL codes: C21, D12, I31, I32.
*Corresponding Author:
Katsushi Imai (Dr)Economics, School of Social Sciences; University of Manchester, Arthur Lewis Building,
Oxford Road, Manchester M13 9PL, UK; Phone: +44-(0)161-275-4827 ; Fax: +44-(0)161-275-4928
E-mail: Katsushi.Imai@manchester.ac.uk

1

Demand for Nutrients in India: An Analysis Based on the 50th, 61st and 66th
Rounds of the NSS1Introduction
Various studies point to a puzzle. In India despite rising incomes, there has been a sustained
decline in per capita calorie intake. In an important contribution, Deaton and Dreze (2009)
offer a detailed analysis of the decline in calorie intake over the period 1983 to 2004. Their
principal findings are summarised below.
Average calorie consumption was about 10 per cent lower in rural areas in 2004–05 than in
1983. The proportionate decline was larger among the more affluent sections of the
population, and about 0 for the bottom quartile of the per capita expenditure scale. In urban
areas, there was a slight change in average calorie intake over this period.
The decline of per capita consumption is not confined to calories. It also applies to proteins
and other nutrients, with the exception of fats whose consumption has increased in both ruraland urban areas over this period.
As incomes rose over this period, these declines are puzzling. A more contentious view
offered by Deaton and Dreze (2009) is that the latter are not attributable to changes in relative
prices as an aggregate measure of the price of food — treated synonymous with the price ofcalories — changed little during the period in question. So the puzzle is essentially this: percapita calorie consumption is lower at a given level of per capita household expenditure,across the expenditure scale, at low levels of per capita expenditure as well as high. In otherwords, there is a steady downward shift of the calorie Engel curve.
Deaton and Dreze (2009), hereafter DD, are emphatic that the downward shift of the calorie
Engel curve is due to lower calorie requirements, associated mainly with better health andlower activity levels. As the evidence offered is fragmentary and patchy, this explanation islargely conjectural.
The present study builds on an earlier critique of DD, by developing a demand theory based
explanation of calories and proteins but over a longer period 1993-20092. The most recent
1

We are grateful to T. Elhaut for guidance in the initial stage, Anil Deolalikar and Raghbendra Jha for valuableadvice, and Raj Bhatia for competent data analysis. Although this study was funded by the Asia and Pacific
Division of IFAD, the views expressed are personal.
2
For details, see Gaiha et al. (2012).

2

round (66th round corresponding to the year 2009-10) of the National Sample Survey (NSS)
provides new insights on the consumption and expenditure behaviour of households. This
round throws new light on the trends in nutrient intake in the past two decades. The analysis
is based mostly on unit record data collected for the 50th, 61st and 66th rounds of the NSS(corresponding to 1993–94, 2004–05, and 2009-10, respectively).

1.1. Nutrient Intakes

Calories
In rural areas, the mean calorie intake reduced from 2156 in 1993 to 2047 in 2004, a decline
of about 5 per cent. The reduction in calorie intake was slower between 2004 and 2009 – only
1 per cent. In urban areas, the mean calorie intake reduced from 2074 in 1993 to 2021 in 2004
and 1982 in 2009, i.e. a 3 per cent decline in the first period and a 2 per cent decline in thesecond.

Until recently, a calorie intake of 2400 per day was considered adequate for a typical adultengaged in physically strenuous work of a certain duration in rural India. More recent

Fig 1: Calorie Distribution in Rural India

Fig 2: Calorie Distribution in Urban India

80

9

5

4

15

15

Calorie Intake Distribution: Urban India
Proportion of Population (%)

Proportion of Population (%)

Calorie Intake Distribution: Rural India
100

19

60

43

46

40

40
20

31

37

35

0

<1800

1993-94

2004-05

2009-10

1801–2400

2401–3000>3000 calories

100

10

25

24

35

35

28

29

30

1993-94

2004-05

2009-10

1701–2100

2101–2600>2600 calories

80
26

60
40
20

30

0

<1700

3

11

16

assessments have used lower calorie ‘requirements’ (1800 calories). 3 Figures 1 & 2 show the
distribution of calories in rural and urban India between 1993 and 2009. Detailed tables
containing the mean calorie intakes are given in the annexure.

Using the higher calorie requirement of 2400, over 71% of the rural households were calorie
deprived or more generally undernourished in 19934. With the lower norm of 1800, this
proportion falls sharply to about 31%, implying a large concentration of households in thecalorie intake range of 1800–2400. The proportion of undernourished rises from 71% tonearly 80% in 2004 and 2009. The proportion below the lower cut-off rose from about 31%
in 1993 to close to 37% in 2004, indicating high levels of calorie deprivation. It declined only
marginally between 2004 and 2009. The mean calorie intake reduced from 2156 in 1993 to
2047 in 2004 to 2020 in 2009.

In urban India, assuming lower calorie norms of 1700 and 2100 (given less strenuous
physical activity in urban areas), about 28 % consumed less than 1700 calories in 1993.
About 58 % were below the higher calorie norm of 2100. Worse, this proportion rose to about
64 % in 2004. The proportion of people below the lower cut-off changed only slightly. The
proportion of people below the lower and the higher calorie norm changed only slightly
between 2004 and 2009. While the mean calorie intake (overall) decreased between 2004 and2009, it increased for those below the lower calorie norm. Although less alarming than thecalorie deprivation increase in rural India, it is nevertheless worrying.

DD drew attention to the downward shift in the calorie Engel curve over the period 1983–
2004. We find that for the period 1993-2009 the calorie Engel curves for rural India display adownward shift-especially above extremely low levels of monthly per capita expenditure(MPCE) at 2004 prices (Figures 3 and 4). The proportionate reduction in calorie intake ismuch higher at higher MPCE. The calorie Engel curve in urban India for 2004 lies above
1993 at lower levels of MPCE. At MPCE >Rs 500, calorie intake was higher in 1993 than in
2004. The proportionate reduction in calorie intake at higher MPCE was larger. Similarly, thecalorie Engel curve for 2009 lies above that for 2004 at lower levels and the crossover occurs

3

Srinivasan (1992 ) is deeply sceptical of such requirements on the ground that energy expenditure adjusts tointake within a range.
4
Although calorie deprivation is an aspect of undernutrition, we sometimes use them interchangeably for
expositional convenience.

4

at MPCE of Rs. 500. Thus, there is evidence of a downward shift of the calorie Engel curvein both rural and urban India, especially at higher levels of MPCE.

Fig 3: Calorie Engel Curve for Rural India

Fig 4: Calorie Engel Curve for Urban India

Protein
The mean protein intakes reduced from 60.3 gms in 1993 to 55.8 gms in 2004, i.e. by 7 percent. The reduction was slower in the second period – by 3 per cent with mean intake of 54.1
gms in 2009. In urban areas, the mean protein intake reduced from 57.3 gms in 1993 to 55.4
gms in 2004 and 53.4 gms in 2009, i.e. a reduction of about 3-4 per cent in both periods.

To assess the proportion of people who are protein-deprived, a cut-off of 60 (gms) of protein
Fig 5: Protein Intake Distribution in Rural India

Fig 6: Protein Intake Distribution in Urban India

12

20

21

Protein Intake Distribution: Urban India

9

Proportion of Population (%)

Proportion of Population (%)

Protein Intake Distribution: Rural India
100
90
80
70
60
50
40
30
20
10
0

21

23

38

41

24

29

29

1993-94

2004-05
61–75

34

<45

46–60

100
90
80
70
60
50
40
30
20
10
0

14

11

23

25

7
20

35

38

44

25

29

29

2009-10

1993-94

2004-05

2009-10>75 gms

<45

5

46–60

61–75

>75 gms

intake is used5. Figures 5 and 6 show the distribution of protein in rural and urban India
between 1993 and 2010. Detailed tables containing the mean protein intakes are given in theannexure.

While protein deficiency is in large measure linked to calorie deficiency, we note that wellover 57 % of rural households consumed fewer than the required protein intake in 1993. Just
under a quarter of the households consumed <45 (gms) of protein. Within both ranges ofprotein intake, the proportions rose more than moderately (e.g., in the lower range, theproportion of households rose from about 24 % in 1993 to about 29 % in 2004; in the higher
range, the proportion rose from 57% in 1993 to 66% in 2004 to 70% in 2009). Although themean intake of protein for households in the lower range remained unchanged, it reduced
from 60 gms in 1993 to 56 gms in 2004 and 54 gms in 2009.

The share of protein-deficient households in urban India (considering the 60 gm cut-off)remained unchanged between 1993 and 2004 but increased substantially between 2004 and2009. Overall mean protein intakes, however, reduced.

Figures 7 and 8 report protein Engel curves for rural and urban India between 1993–2009.
The rural–urban contrast in protein intake is striking. In the rural areas, protein intake wasconsistently lower across expenditure classes in 2009 than in 2004 which in turn was lower
than in 1993. The gap between 1993 and 2004 intakes widens considerably at higher MPCE.
Fig 7: Protein Engel Curves for Rural India5

Fig 8: Protein Engel Curves for Urban India

For details, see Gopalan et al. (1971, 1992), and ICMR (1990).

6In urban areas, the 2004 curve was above the 1993 curve at low levels of MPCE and, after the
cross-over expenditure of about Rs 500, the former lay below the 1993 curve. The 2009 curvelies above the 2004 curve at lower levels of MPCE and below the 2004 curve at higher levels.

Fats
Mean fat intake increased substantially between 1993-94 and 2004-05. In rural areas, themean fat intake increased by 12 per cent, from 31.5 gms in 1993 to 35.4 gms in 2004. In
urban areas, the mean fat intake increased by 13 per cent, from 42.1 gms in 1993 to 47.5 gms
in 2004. The increase in fat intakes in the second period, i.e. 2004-05 and 2009-10 were more
moderate. Mean fat intakes increased to 38.2 gms (increase of 8 per cent) in rural areas, and48.9 gms (increase of 3 per cent) in urban areas.

Although a precise range for fat requirements cannot be specified, a range of 40–60 (gms) of
fat intake is desirable6. Figures 9 and 10 show the distribution of fat in rural and urban India
between 1993 and 2009. Detailed tables containing the mean fat intakes are given in theannexure.

Fig 9: Fat Intake Distributions for Rural India

Fig 10: Fat Intake Distributions for Urban India

15

17

Fat Intake Distribution: Urban India
Proportion of Population (%)

Proportion of Population (%)

Fat Intake Distribution: Rural India
100
90
80
70
60
50
40
30
20
10
0

21

26
34
40
25
27

24
34
23

1993

<20

2004

21–30

31–50

14

100
90
80
70
60
50
40
30
20
10
0

19

25

26
32
37
30
31
28
25

2010

1993

>50 gms

<25

6

22

26–40

15

10

2004

2010

41–60

>60 gms

Gopalan et al. (1971) observe: ‘The quantity of fat that should be included in a well balanced diet is not knownwith any degree of certainty. However, it appears desirable in the present state of knowledge that the daily
intake of fat should be such that it contributes no more than 15 to 20 % of the calories in the diet. A total of
about 40 to 60 gms of fat can therefore be safely consumed daily, and in order to obtain the necessary amounts
of essential fatty acids, the fat intake should include at least 15 gms of vegetable oils’ (pg. 8). Also see ICMR
(1990).

7

Even considering the first three ranges of fat intake, an astonishingly high estimate for fat
deficient households for rural India (over 85%) is obtained for 1993. Well over one-third ofhouseholds were under the lowest range of <20 (gms). The corresponding household sharewith fat intakes <50 gms fell to over 83% in 2004 and 79% in 2009. The share of households
consuming <20 gms of fat fell sharply (from over 34% in 1993 to well over 22% in 2004 and
14% in 2009). Mean fat intakes increased over the period 1993 to 2009.

Using higher ranges of fat intake for urban areas, fat deprivation was pervasive (about 81% of
the households consumed <60 gms of fats in 1993). About a quarter consumed <25 gms.
Over the period 1993–2009, the reduction in the proportion of fat- deprived was slight (from
81% in 1993 to 78% in 2004 to 75% in 2009). However, as in rural India, the proportion
consuming fats <25 gms fell sharply. Mean fat intake increased.

Figures 11 and 12 show the Engel curves for fat. In rural areas, the fat Engel curves for 1993
lay below that for 2004 at lower levels of MPCE. The 2009 curve lies above the 2004 curve
at lower levels of MPCE. The crossover is at approximately Rs.650. In urban areas, thecrossover is at Rs.1000. The curves converge beyond this.
Fig 11: Fat Engel Curves for Rural India

Fig 12: Fat Engel Curves for Urban India

Thus, taking nutritional norms as valid, the overall picture of nutritional deprivation
worsened considerably over the period 1993–2009. The following Kernel density functions(figure 13) clearly show a leftward shift in calorie and protein intake distributions and arightward shift in the fat intake distribution in both rural and urban areas between 1993 and20097.
7

For an admirably clear exposition of kernel density functions, see Deaton (1995).

8

Fig 13: Kernel Density Functions for Calories, Proteins and Fats consumed – Rural and
Urban India (1993 – 2009)

1.2. Factors Underlying Changes in Diets and Nutrition

We build on the DD analysis (2009) of food commodities that contributed to reduction incalories, protein and increase in fat consumption.

9

Figure 14 shows the distribution of calorie, protein and fat intake by food commodities forrural and urban areas between 1993 and 2009. Detailed tables containing mean per capita
intake of nutrients from the various food items are given in the annexure.

Fig 14: Calorie Intake by Food Commodities, 1993, 2004 and 2009
0%

10%

20%

30%

40%

1993-94

50%

60%

70%

71%

80%

6%

90%

5%

100%

7%

1% 4%

5% 1%

8%

1% 4%

2004-05

68%

7%

2009-10

64%

7%

8%

8%

6% 1%

11%

2% 5%

10%

6% 1%

12%

2% 5%

1993-94

58%

56%

2009-1054%

Cereals

Milk

Vanaspati-Oil

9%

10%

Sugar

Eggs & Meats

11%

PulsesFruits

5% 1% 4% 1% 4%

6% 1% 5% 2% 4%

Vegetables

In rural India, calorie intakes declined between 1993 and 2009. Most of it is attributable tothe decline in calories from cereals, and pulses to some extent. Calories from cereals reduced
from 1530 in 1993 to 1383 in 2004 and 1296 in 2009. The contribution of milk/milk
products, sugar, fruits and vegetables remained largely unchanged between 1993 and 2009.
The contribution of vanaspati oil to the calorie intake increased substantially. Calories
obtained from oil increased from 111 in 1993 to 145 in 2004 and 169 in 2009.

Similar results are found for urban areas as well. The reduction in the contribution of pulses
to calorie intakes was more drastic in urban areas between 2004 and 2009.

10

URBAN

2004-05

9%

7%

RURAL

5% 1%

Fig 15: Protein Intake by Food Commodities, 1993, 2004 and 2009
0%

10%

20%

30%

1993-94

40%

50%

60%

70%

80%

69%

68%

2009-1066%

60%

2004-05

58%

2009-10

13%

57%

Cereals

Milk

5%

10%

12%

14%

Eggs & Meats

Pulses

13%

5%

13%

6%

4%

5%

10%

5%

6%

17%

6%

6%

18%

7%

6%

12%

6%

Fruits & Vegetables

In both rural and urban areas, protein intake declined between 1993 and 2009 (Figure 15).
Much of it is reflected in a reduction in protein intake from cereals and pulses. In rural areas,
the protein intake from these commodities decreased from 50 gms in 1993 to 45 gms in 2004
and 40 gms in 2009. The corresponding numbers for urban areas are 44 gms (1993), 42 gms(2004) and 37 gms (2009). Intake from dairy products (milk, eggs and meats) increased
moderately. Proteins from other sources are a very small fraction and largely remained
unchanged between 1993 and 2009.

Between 1993-2009 fat intake rose in both rural and urban areas. Vanaspati-oil has the
maximum share in total fat intakes, and its contribution rose over the period 1993 and 2009.
In rural areas, fat intake from vanaspati-oil rose from 12 gms in 1993 to 16 gms in 2004 and19 gms in 2009. In urban areas, the share of vanaspati-oil rose from 19 gms in 1993 to 22
gms in 2004 and 24 gms in 2009. The other significant contributor to fat intake are cereals,
pulses and milk products (including ghee and butter), but their contribution reduced between
1993 and 2009. Declining intake of fats from these commodities were more than offset by theincreased intake of fat from vanaspati-oil.
11

URBAN

1993-94

9%

100%

RURAL

2004-059%90%Fig 16: Fat Intake by Food Commodities, 1993, 2004 and 20090%

10%

1993-94

20%

30%

17%

50%

60%

70%

31%

14%

27%

2009-10

11%

26%

10%

2004-05

8%

29%

2009-10

7%

30%

39%

46%

2%

8%

4%

1%

9%

3%

1% 3% 3%

44%

2%

47%

Vanaspati-Oil

100%

49%

31%

Milk

90%

2%

49%

Eggs & Meats

Pulses

10%

4%

12%

3%

URBAN

1993-94

80%

RURAL

2004-05

Cereals

40%

1% 3% 3%

Fruits & Vegetables

1.3. Demand Theory Based Explanation of Changes in Nutrient Intake

In response to the Deaton–Dreze (2009) explanation of a downward shift in the calorie Engel
curve in terms of lower ‘requirements’ due to health improvements, and less strenuous
activity levels, and more sedentary life-styles, we have developed an alternative explanationof changes in the consumption of calories, protein and fats over the period 1993–2009. This
explanation is embedded in a standard demand theory framework, with food prices 8 and
expenditure (as a proxy for income) cast in a pivotal role. Based on different experiments,
robust demand functions are estimated for each of the three nutrients viz. calories, protein andfats (Tables 1-3). Our results show consistently robust food price and expenditure effects,
thus raising further doubts about the adequacy of the DD explanation. The analysis is based
8

An important point to bear in mind is that the price effects capture both own and cross-price effects throughsubstitutions between food commodities. Briefly, as prices change, demands for commodities change andconsequently calorie (and other nutrients’) intakes. Underlying this is a presumption that food choices are
informed by their nutritional content. As Deaton and Dreze (2009) emphasise, people do not buy calories andother nutrients but food commodities. However, if food choices are informed by their nutritional values, it ismeaningful to talk about demands for calories and other nutrients12

on unit record data collected for the 50th, 61st and 66th rounds of the NSS (corresponding to
the years 1993–94, 2004–05, and 2009-10, respectively). We will discuss separately the
results for rural and urban samples.

(a) Demand Function
A basic demand equation for nutrients (calories, protein, fats) in rural and urban India withpooled data for 1993 2004 and 2009 is given below:(1)

where the dependent variable is log of nutrient consumed by ith household in time t, Pjt is avector of food prices computed from the NSS at the village level (j) and time t, E it ishousehold per capita expenditure for ith household in time t, Xit is a vector of household
characteristics (e.g. number of adult males, females, household size ) and a few others
specified as dummy variables (caste), D1t is a dummy variable that takes the value 1 for 2004
and 0 otherwise (to allow for changes in factors other than food prices and expenditure overtime), D2t+k is the second time dummy that takes the value 1 for 2009 and 0 otherwise, and it is the error term. This equation is estimated using robust regression. Although a Chow test

for a structural shift is not feasible with robust regression, we have employed two
refinements: one is the use of a time dummy that could potentially capture the healthimprovements and less strenuous activity patterns (associated with easier access to drinking
water, better transportation facilities); and the other is interactions of food price variables
with the time dummy to allow for different price and expenditure effects over time9.

An important point to bear in mind is that the price effects capture both own and cross-priceeffects through substitutions between food commodities. Briefly, as prices change, demands
for commodities change and consequently, for example, calorie (and other nutrients’) intakes.

9

While these effects yield insights into changing consumer demands, a more detailed analysis along the lines ofBehrman and Deolalikar (1989) and Jha et al. (2009) would further deepen our understanding consumer
preferences and tastes for variety.

13(b) Results
We first consider the calorie demand function. For both rural and urban areas, each of theprice effects has a significant negative coefficient, with the exception of price of fruits andvegetables whose coefficient in not significant. Moreover, the price effects on the demand for
calories have in many cases weakened, as captured by the coefficients of the interaction term
(i.e. interaction between the price and the time dummies). A case in point is price of cereals
and pulses. Rising incomes have a positive impact on calorie demand, and so does the
proportion of adults in the household. The former effect, however, weakened over time. As
household size increases, calorie demand decreases. Education of adult female has a negative
impact on calorie demand, as this may be attributed to consumption of a more balanced diet,rich in protein and vitamins and reduced dependence on cereals, which is a major source ofcalories. By contrast, education of adult male is not significant in rural areas but positive inurban areas. SCs have lower demands for calories in both rural and urban areas while that of
Others is positive in rural areas and negative in urban areas relative to STs. The effect of timealone is reduced consumption of calorie between 1993 and 2004. However, somewhat
surprisingly, the residual time effect is not significant between 2004-09. That this undermines
the DD explanation emphasising lower calorie requirements due to improvements in theepidemiology of the disease environment, less strenuous physical activities and moresedentary life styles cannot be ruled out.

In case of protein demand in both rural and urban areas, there is a positive influence of theprice of fruits and vegetables, whereas the elasticity of protein demand with respect to allother food prices is negative. As in the case of calorie demand, several price effects
weakened over over time, as captured by the interaction term (between food prices and thetime dummies). A case in point is price of milk and milk products, eggs and meats. Higher
income/expenditure increase protein demand but less so over time. Increasing household size
reduces protein demand, whereas rising proportion of adults in the household increases it.
The impact of education of adult female is negative, and that of education of adult male isnot significant. SCs have lower demand for protein while Others have higher demand relative
to STs. The residual effect of time after accounting for the interaction is significant with anegative coefficient suggesting a lower demand for protein in 2009 and 2004 as compared to
1993.

14

In case of fats too in both rural and urban areas , there are significant price and expenditureeffects – positive in the case of price of cereals, oil and fruits and vegetables, and negative incase of milk products and sugar. The positive price elasticity of fats with respect to edible oil
price is somewhat surprising, although the price effect has weakened over time. MPCE has
positive Unlike in the case of calorie and protein, highest education of adult female has apositive impact on fat demand, suggesting that households with females having higher than
middle level of education tend to eat diets rich in fats, shifting away from diets rich incalories and proteins. Moreover, highest education of adult male also has a positive andsignificant effect. As in the case of calorie and protein, household size has a negative impact
and proportion of adults in a household has a positive impact on demand for fats. SCs andOthers have higher demands relative to STs. The effect of time alone is to increase the fat
demand in 2009 and 2004, as compared to 1993.

2. Concluding Observations
The main findings of our analysis are summarised below from a policy perspective.

An important insight from the comparison of three rounds of the NSS over the period 19932009 points to a slowing down of the reduction in the calorie intake in both rural and urban
areas in 2004-09 relative to 1993-2004. A similar slowing down occurred in protein intake
over the more recent period (2004-09). While fat intake continued to rise over this period but
at a lower rate.

A demand theory based explanation of changes in demands for these nutrients reaffirmed theimportant roles of food price and expenditure changes. A new insight, however, is that inseveral cases food price effects weakened over time, as also expenditure effects. While theresidual time effect weakened for both calories and protein over the period 1993-2004, it
slowed down over the more recent period. The effect of time on fat demand was, however,positive and strengthened over the more recent period. Altogether thus the DD explanation oflower calorie and protein demands over time as a result of lower requirements is further
undermined.

From a policy perspective, the important roles of food price stabilisation and expansion of
livelihood opportunities remain key to ameliorating nutritional deprivation despite loweringof nutritional requirements. A closer scrutiny of dietary preferences and tastes is crucial for
15both understanding sources of nutritional deprivation and for designing effective policies for
mitigating it.

16

Table 1: Robust Regression Estimates: Demand for Calories, 1993-2009
RURAL
Dependent Variable: Logof Per capita consumptionof calories

URBAN

No. of Obs.

=

193785

No. of Obs.

=

124481F(60,193724)=

11926.31

F(60,124420)=

3705.11

Prob. > F=

0

Prob. > F=0

Coefficient

Elasticity

Coefficient

Elasticity

Price: Cereals & Pulses-0.012(-25.24)***-0.166(-25.24)***-0.006(-10.2)***-0.090(-10.2)***---interaction T1 (2004)

0.011(20.72)

***

0.049

(20.72)

***

0.001

(0.96)-

0.003(0.96)----interaction T2 (2009)
Price: Milk, Milk
Products, Eggs & Meats---interaction T1 (2004)

0.010(20.05)

***

0.071

(20.05)

***

0.004

(6.99)***

0.037(6.99)***-0.003(-64.21)***-0.098(-64.21)***-0.001(-12.53)***-0.035(-12.53)

***

0.002

(42.88)

***

0.026

(42.88)

***

0.000

(3.29)***

0.003(3.29)***---interaction T2 (2009)

0.002(49.83)

***

0.040

(49.83)

***

0.001

(9.04)***

0.013(9.04)***

Price: Oil-0.008(-26.67)***-0.086(-26.67)***-0.004(-10.1)***-0.048(-10.1)***---interaction T1 (2004)

0.004(11.75)

***

0.015

(11.75)

***

0.001

(1.89)*

0.003(1.89)*---interaction T2 (2009)

0.006(19.34)

***

0.031

(19.34)

***

0.004

(9.94)***

0.024(9.94)***

Price: Sugar-0.002(-12.7)***-0.124(-12.7)***-0.004(-12.09)***-0.183(-12.09)***---interaction T1 (2004)

0.002(11.36)

***

0.050

(11.36)

***

0.003

(10.29)

***

0.061

(10.29)***---interaction T2 (2009)
Price: Fruits &Vegetables---interaction T1 (2004)

0.001(7.04)***

0.027(7.04)***

0.002(5.16)***

0.033(5.16)***-0.001(-1.16)--0.010(-1.16)-0.001(1.94)*

0.026(1.94)*

0.001(2.75)***

0.010(2.75)***

0.000(0.26)-

0.001(0.26)----interaction T2 (2009)
Monthly per capita
expenditure---interaction T1 (2004)-0.001(-1.74)*-0.007(-1.74)*-0.003(-3.92)***-0.027(-3.92)

***

0.001

(401.95)

***

0.418

(401.95)

***

0.000

(225.49)

***

0.240

(225.49)

***

0.000

(-116.0)***-0.054(-116.0)

***

0.000

(-23.86)***-0.011(-23.86)***---interaction T2 (2009)

0.000(-171.2)***-0.063(-171.2)

***

0.000

(-50.97)***-0.022(-50.97)***

Household Size
Proportion of Adults in
the household
Education Dummy: Males
Education Dummy:
FemalesTime Dummy (2004)-0.006(-30.76)***-0.033(-30.76)***-0.021(-68.4)***-0.096(-68.4)

***

0.202

(91.39)

***

0.137

(91.39)

***

0.235

(81.83)

***

0.169

(81.83)

***

0.000

(-0.23)-0.000(-0.23)-0.012(8.05)***

0.008(8.05)***-0.025(-21.5)***-0.008(-21.5)***-0.013(-9.52)***-0.007(-9.52)***-0.159(-18.93)***-0.061(-18.93)***-0.105(-8.29)***-0.036(-8.29)***Time Dummy (2009)-0.007(-0.69)--0.002(-0.69)-0.006(0.45)-

0.002(0.45)-

Caste Dummy (SC)-0.011(-5.86)***-0.002(-5.86)***-0.017(-4.98)***-0.002(-4.98)***Caste Dummy (Others)

0.010(6.16)***

0.006(6.16)***-0.005(-1.77)*-0.004(-1.77)*

Constant

7.569(890.23)

***

7.712

(592.82)******, ** and * refer to significance at the 1%, 5% and 10% level, respectively.
State Dummies are omitted and details will be furnished on request; Education Dummy: If highest level of education is middle or lower, thendummy = 0, else=1; Omitted Time period: 1993; Omitted caste: ST

17

Table 2: Robust Regression Estimates: Demand for Protein, 1993-2009
RURAL
Dependent Variable:
Log of Per capita
consumption of protein

URBAN

No. of Obs.

=

193784

No. of Obs.

=

124481F(60,193723)=

11445.86

F(60,124420)=

3914.91

Prob. > F=

0

Prob. > F=

Coefficient

Elasticity0

Coefficient

Elasticity

Price: Cereals & Pulses-0.013(-23.92)***-0.169(-23.92)***-0.009(-15.08)***-0.144(-15.08)***---interaction T1 (2004)

0.013(21.53)

***

0.055

(21.53)

***

0.005

(6.62)***

0.019(6.62)***---interaction T2 (2009)
Price: Milk, Milk
Products, Eggs & Meats---interaction T1 (2004)

0.012(21.87)

***

0.084

(21.87)

***

0.009

(13.86)

***

0.079

(13.86)***-0.003(-74.97)***-0.123(-74.97)

***

0.000

(-3.73)***-0.011(-3.73)

***

0.002

(49.93)

***

0.032

(49.93)

***

0.000

(-1.96)*-0.002(-1.96)*---interaction T2 (2009)

0.003(58.15)

***

0.050

(58.15)

***

0.000

(2.64)***

0.004(2.64)***

Price: Oil-0.010(-32.16)***-0.112(-32.16)***-0.007(-17.09)***-0.088(-17.09)***---interaction T1 (2004)

0.005(16.23)

***

0.022

(16.23)

***

0.003

(6.58)***

0.012(6.58)***---interaction T2 (2009)

0.007(22.18)

***

0.038

(22.18)

***

0.006

(15.62)

***

0.041

(15.62)***

Price: Sugar-0.002(-10.85)***-0.114(-10.85)***-0.003(-8.44)***-0.139(-8.44)***---interaction T1 (2004)

0.002(9.41)***

0.045(9.41)***

0.001(3.37)***

0.022(3.37)***---interaction T2 (2009)
Price: Fruits &Vegetables---interaction T1 (2004)

0.001(2.49)**

0.010(2.49)**

0.000(0.82)-

0.006(0.82)

-

0.002

(4.2)

***

0.040

(4.2)

***

0.002

(2.44)**

0.036(2.44)**

0.000(0.84)-

0.003(0.84)--0.001(-0.81)--0.004(-0.81)----interaction T2 (2009)
Monthly per capita
expenditure---interaction T1 (2004)-0.003(-6.52)***-0.030(-6.52)***-0.003(-3.66)***-0.027(-3.66)

***

0.001

(391.84)

***

0.438

(391.84)

***

0.000

(219.12)

***

0.254

(219.12)

***

0.000

(-100.4)***-0.051(-100.4)

***

0.000

(-15.27)***-0.008(-15.27)***---interaction T2 (2009)

0.000(-138.9)***-0.058(-138.9)

***

0.000

(-37.05)***-0.017(-37.05)***

Household Size
Proportion of Adults in
the household
Education Dummy:
Males
Education Dummy:
FemalesTime Dummy (2004)-0.005(-23.68)***-0.027(-23.68)***-0.018(-55.26)***-0.084(-55.26)

***

0.198

(82.93)

***

0.134

(82.93)

***

0.226

(72.47)

***

0.163

(72.47)

***

0.001

(1.16)-

0.001(1.16)-

0.012(7.43)***

0.008(7.43)***-0.025(-20.31)***-0.008(-20.31)***-0.014(-9.47)***-0.008(-9.47)***-0.210(-23.15)***-0.081(-23.15)***-0.050(-3.6)***-0.017(-3.6)***

Time Dummy (2009)-0.031(-2.84)***-0.009(-2.84)***-0.047(-3.07)***-0.015(-3.07)***

Caste Dummy (SC)-0.007(-3.64)***-0.001(-3.64)***-0.007(-1.87)*-0.001(-1.87)*

Caste Dummy (Others)

0.011(6.23)***

0.007(6.23)***

0.005(1.43)-

0.004(1.43)-

Constant

3.952(431.44)

***

4.101

(289.87)******, ** and * refer to significance at the 1%, 5% and 10% level, respectively.
State Dummies are omitted and details will be furnished on request; Education Dummy: If highest level of education is middle or lower, thendummy = 0, else=1; Omitted Time period: 1993; Omitted caste: ST

18

Table 3: Robust Regression Estimates: Demand for Fats, 1993- 2009
RURAL
Dependent Variable:
Log of Per capita
consumption of Fats

URBAN

No. of Obs.

=

193784

No. of Obs.

=

124481F(60,193723)=

18957.78

F(60,124420)=

7662.01

Prob. > F=

0

Prob. > F=0

Coefficient

Elasticity

Coefficient

Elasticity

Price: Cereals & Pulses

0.016(20.33)

***

0.218

(20.33)

***

0.012

(14.62)

***

0.193

(14.62)***---interaction T1 (2004)-0.005(-5.98)***-0.023(-5.98)***-0.008(-8.71)***-0.035(-8.71)***---interaction T2 (2009)
Price: Milk, Milk
Products, Eggs & Meats---interaction T1 (2004)-0.009(-10.67)***-0.062(-10.67)***-0.006(-6.61)***-0.052(-6.61)***-0.013(-192.4)***-0.478(-192.4)***-0.009(-76.98)***-0.327(-76.98)

***

0.008

(105.09)

***

0.106

(105.09)

***

0.005

(35.37)

***

0.056

(35.37)***---interaction T2 (2009)

0.010(133.13)

***

0.172

(133.13)

***

0.007

(55.87)

***

0.121

(55.87)***

Price: Oil

0.002(4.45)***

0.023(4.45)***

0.007(13.64)

***

0.097

(13.64)***---interaction T1 (2004)-0.001(-2.51)**-0.005(-2.51)**-0.003(-5.71)***-0.014(-5.71)***---interaction T2 (2009)-0.002(-4.29)***-0.011(-4.29)***-0.004(-8.01)***-0.029(-8.01)***Price: Sugar-0.004(-13.65)***-0.217(-13.65)***-0.003(-7.5)***-0.170(-7.5)***---interaction T1 (2004)

0.001(3.06)***

0.022(3.06)***

0.001(1.23)-

0.011(1.23)----interaction T2 (2009)
Price: Fruits &Vegetables---interaction T1 (2004)-0.001(-1.69)*-0.011(-1.69)*-0.002(-4.2)***-0.040(-4.2)***

0.011(14.18)

***

0.206

(14.18)

***

0.006

(6.15)***

0.125(6.15)***-0.010(-12.27)***-0.070(-12.27)***-0.006(-5.56)***-0.037(-5.56)***---interaction T2 (2009)
Monthly per capita
expenditure---interaction T1 (2004)-0.011(-13.92)***-0.097(-13.92)***-0.008(-7.4)***-0.076(-7.4)***

0.001(443.65)

***

0.753

(443.65)

***

0.000

(288.58)

***

0.462

(288.58)

***

0.000

(-127.9)***-0.098(-127.)

***

0.000

(-18.96)***-0.014(-18.96)***---interaction T2 (2009)-0.001(-210.0)***-0.126(-210.0)

***

0.000

(-84.14)***-0.054(-84.14)***

Household Size
Proportion of Adults in
the household
Education Dummy:
Males
Education Dummy:
FemalesTime Dummy (2004)-0.019(-55.56)***-0.097(-55.56)***-0.039(-84.97)***-0.179(-84.97)

***

0.165

(45.84)

***

0.112

(45.84)

***

0.188

(43.68)

***

0.135

(43.68)

***

0.056

(32.04)

***

0.029

(32.04)

***

0.086

(38.25)

***

0.060

(38.25)

***

0.031

(16.7)***

0.010(16.7)***

0.086(40.86)

***

0.048

(40.86)

***

0.299

(21.75)

***

0.115

(21.75)

***

0.161

(8.45)***

0.055(8.45)***

Time Dummy (2009)

0.564(33.91)

***

0.165

(33.91)

***

0.360

(16.97)

***

0.114

(16.97)***

Caste Dummy (SC)0.052

(17.4)***

0.009(17.4)***

0.040(7.64)***

0.005(7.64)***

Caste Dummy (Others)

0.135(52.78)

***

0.090

(52.78)

***

0.108

(23.07)

***

0.086

(23.07)***

Constant

3.023(217.87)

***

3.474

(177.76)******, ** and * refer to significance at the 1%, 5% and 10% level, respectively.
State Dummies are omitted and details will be furnished on request; Education Dummy: If highest level of education is middle or lower, thendummy = 0, else=1; Omitted Time period: 1993; Omitted caste: ST

19

Annexure 1: Distribution of Nutrient Intake (1993 – 2009)
The following tables show the distribution of nutrient intake (proportion of population ineach category) in rural and urban areas between 1993 and 2009. Figures in parenthesis
indicate the mean nutrient intake in each range.
Table 1: Calorie Intake Distributions in Rural India, 1993, 2004 and 2009Year

Range of Calorie Intake Per Capita Per Day

Total

<1800

1801–2400

2401–3000

>3000

1993

31.09
(1491)

40.07(2084)

19.42(2650)

9.42(3636)

100(2156)

2004

36.68(1516)

43.11(2071)

15.07(2629)

5.14(3925)

100(2047)

2010

35.28(1531)

45.91(2071)

14.92(2622)

3.89(3545)

100(2020)

Table 2: Calorie Intake Distributions in Urban India, 1993, 2004 and 2009
Range of Calorie Intake Per Capita Per Day
Year

Total
<1700

1701–2100

2101–2600

>2600

1993

28.12
(1426)

29.62(1900)

25.76(2320)

16.49(3107)

100(2074)

2004

29.40(1440)

34.52(1900)

24.67(2313)

11.41(3252)

100(2021)

2010

30.10(1456)

35.34(1896)

24.36(2312)

10.19(3043)

100(1982)

Table 3: Protein Intake Distributions in Rural India, 1993, 2004 and 2009Year

Range of Protein Intake Per Capita Per Day (Gms)

Total

<45

46–60

61–75

>75

1993

23.81
(37.1)

33.79(52.4)

22.79(66.8)

19.61(94.4)

100(60.3)

2004

28.81(37.4)

38.05(52.2)

21.46(66.3)

11.68(93.9)

100(55.8)

2010

29.35(38.0)

40.81(52.1)

21.05(66.1)

8.79(89.2)

100(54.1)

20

Table 4: Protein Intake Distributions in Urban India, 1993, 2004 and 2009Year

Range of Protein Intake Per Capita Per Day (Gms)

Total

<45

46–60

61–75

>75

1993

24.90
(37.1)

37.77(52.4)

23.11(66.5)

14.23(90.7)

100(57.3)

2004

29.40(37.8)

34.50(52.3)

24.69(66.2)

11.40(94.9)

100(55.4)

2010

29.44(38.3)

43.66(52.1)

19.61(66.1)

7.29(87.6)

100(53.4)

Table 5: Fat Intake Distributions in Rural India, 1993, 2004 and 2009Year

Range of Fat Intake Per Capita Per Day (Gms)

Total

<20

21–30

31–50

>50

1993

34.30
(14.0)

25.08(24.7)

26.09(38.3)

14.54(72.1)

100(31.5)

2004

22.59(15.0)

27.21(24.9)

33.58(38.3)

16.62(74.6)

100(35.4)

2010

14.20(15.4)

24.05(25.1)

40.49(38.8)

21.25(67.2)

100(38.2)

Table 6: Fat Intake Distributions in Urban India, 1993, 2004 and 2009Year

Range of Fat Intake Per Capita Per Day (Gms)

Total

<25

26–40

41–60

>60

1993

25.04
(18.2)

29.84(32.3)

26.15(48.6)

18.97(80.2)

100(42.1)

2004

15.39(19.4)

31.02(32.6)

31.56(48.9)

22.04(85.8)

100(47.4)

2010

10.32(19.8)

27.88(33.0)

36.61(49.0)

25.19(78.1)

100(48.9)

Source: Authors’ calculations based on the 50th, 61st and 66th rounds of the NSS

21

Annexure 2: Distribution of Nutrient Intake by Food Commodities (1993 -2009)
The following tables show the distribution of nutrient (calorie, protein and fat) intake fromnine food commodity groups for rural and urban areas between 1993 and 2009.

Table 1. Mean Per Capita Calorie Intake by Food Commodities, 1993, 2004 and 2009
Year

Cereals

Milk
Products
Ghee/Butter

VanaspatiSugar
Oil

Eggs

Meat/Fish/Poultry

Pulses/Nuts/Dry Fruits

Fruits Vegetables

Total

2156

Rural India
1993-94

1530

137

111

103

2

12

151

20

89

Share

71.0%

6.4%

5.1%

4.8%

0.1%

0.6%

7.0%

0.9%

4.1%

2004-05

1383

137

145

98

3

12

156

23

90

Share

67.6%

6.7%

7.1%

4.8%

0.1%

0.6%

7.6%

1.1%

4.4%

2009-10

1296

141

169

93

3

12

84

20

83

Share

64.2%

7.0%

8.4%

4.6%

0.1%

0.6%

4.2%

1.0%

4.1%

2047
2020

Urban India
1993-94

1213

181

168

129

5

16

231

37

94

Share

58.5%

8.7%

8.1%

6.2%

0.2%

0.8%

11.1%

1.8%

4.5%

2004-05

1133

189

199

115

6

16

235

36

92

Share
200910
Share

56.1%

9.4%

9.8%

5.7%

0.3%

0.8%

11.6%

1.8%

4.6%

1071

199

217

110

5

14

106

33

83

54.0%

10.0%

10.9%

5.5%

0.3%

0.7%

5.3%

1.7%

4.2%

2074
2021

1982

Table 2. Mean Per Capita Protein Intake by Food Commodities, 1993, 2004 and 2009
Year

Cereals

Milk
Products
Ghee/Butter

1993-94

41.8

5.3

0

0

Share

69.3%

8.8%

0.0%

2004-05

37.9

5.3

Share

67.9%

2009-10
Share

VanaspatiSugar
Oil

Meat/Fish/Poultry

Pulses/Nuts/Dry Fruits

0.2

2

0.0%

0.3%

0

0

9.5%

0.0%

35.6

5.5

65.8%

10.2%

Eggs

Fruits Vegetables

Total

7.8

0.2

2.9

60.3

3.3%

12.9%

0.3%

4.8%

0.3

2

7.3

0.3

2.8

0.0%

0.5%

3.6%

13.1%

0.5%

5.0%

0

0

0.3

2

5.3

0.2

2.5

0.0%

0.0%

0.6%

3.7%

9.8%

0.4%

4.6%

Rural India

55.8
54.1

Urban India
1993-94

34.1

6.7

0

0

0.4

2.6

10

0.4

3.1

Share

59.5%

11.7%

0.0%

0.0%

0.7%

4.5%

17.5%

0.7%

5.4%

2004-05

32

7

0

0

0.5

2.7

9.8

0.4

3.1

Share
200910
Share

57.8%

12.6%

0.0%

0.0%

0.9%

4.9%

17.7%

0.7%

5.6%

30.2

7.4

0

0

0.4

2.6

6.5

0.4

2.7

56.6%

13.9%

0.0%

0.0%

0.7%

4.9%

12.2%

0.7%

5.1%

22

57.3
55.4

53.4

Table 3. Mean Per Capita Fat Intake by Food Commodities, 1993, 2004 and 2009

Year

Cereals

Milk
Products
Ghee/Butter

VanaspatiSugar
Oil

Eggs

Meat/Fish/Poultry

Pulses/Nuts/Dry Fruits

Fruits Vegetables

Total

31.5

Rural India
1993-94

5.4

9.7

12.3

0

0.2

0.3

2.4

0.7

0.4

Share

17.1%

30.8%

39.0%

0.0%

0.6%

1.0%

7.6%

2.2%

1.3%

2004-05

4.8

9.7

16.2

0

0.3

0.2

3.1

0.8

0.4

Share

13.6%

27.4%

45.8%

0.0%

0.8%

0.6%

8.8%

2.3%

1.1%

2009-10

4.3

10

18.7

0

0.3

0.2

1.1

0.8

0.3

Share

11.3%

26.2%

49.0%

0.0%

0.8%

0.5%

2.9%

2.1%

0.8%

35.4
38.2

Urban India
1993-94

4

13.2

18.6

0

0.4

0.4

4

1.1

0.4

Share

9.5%

31.4%

44.2%

0.0%

1.0%

1.0%

9.5%

2.6%

1.0%

2004-05

3.7

13.8

22.1

0

0.5

0.3

5.6

1.1

0.4

Share
200910
Share

7.8%

29.1%

46.5%

0.0%

1.1%

0.6%

11.8%

2.3%

0.8%

3.5

14.5

24.1

0

0.4

0.2

1.5

1.1

0.4

7.2%

29.7%

49.3%

0.0%

0.8%

0.4%

3.1%

2.2%

0.8%

Source: Authors’ calculations based on the 50th, 61st and 66th rounds of the NSS

23

42.1
47.5

48.9

References
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Intake”, Review of Economics and Statistics, vol. 71.
Deaton, A. (1995) The Analysis of Household Surveys: A Microeconomic approach to
Development Policy, Baltimore, MD: The Johns Hopkins University Press.
Deaton, A., and J. Dreze (2009), ‘Food and Nutrition in India: Facts and Interpretations’,
Economic and Political Weekly, XLIV(7), 42–65.
Gaiha, R., R. Jha and Vani S. Kulkarni (2012) “Demand for Nutrients in India: 1993 to2004”, Applied Economics, available online: 1 February, 2012.
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