1 Introduction

I

This paper extends the evidence by examining the relationship between PCCC and per capita income (PCI) in a given year. Household surveys like the NSS collect consumption expenditure data but no income data. The India Human Development Survey (IHDS), however, collected both income and consumption expenditure data in 2004-05, enabling us to look at both income and expenditure elasticities of PCCC in 2004-05.2

Our main finding is that PCCC remains much the same at different levels of PCI (within rural or urban areas). In rural areas, for instance, PCCC hovers around 12 kg per month across all income groups. This pattern contrasts sharply with positive income elasticities of demand for other food items such as milk, vegetables and fruits. Instead of being correlated with income, PCCC is substantially affected by educational levels, household size and occupation patterns. After controlling for PCI, MPCE and other socio-economic variables, PCCC also varies across states and may therefore be influenced by regional food habits and local preferences.

This does not mean that there is a contradiction between NSS and IHDS data. Indeed, the positive relationship between PCCC and MPCE is also visible in the IHDS data and is very similar to the corresponding relationship in NSS data. The real contrast therefore is not between NSS and IHDS data but between expenditure-based and income-based Engel curves.

Recent research on changes in food consumption behaviour in India has been concerned with the decline of PCCC over the last few decades. In this context, the decline of PCCC has been attri buted to a diversification of diets (Dyson and Hanchate 2000), a higher availability of urban goods in rural areas (Kumar et al 2007), rising relative prices of cereals vis-à-vis other food items (Mittal 2007) and improved transport infrastructure (Rao 2000). Overall, researchers tend to argue that the decline in PCCC is related to development and increases in MPCE which enable households to move away from cereals towards a more diversified diet (Kumar et al 2009). However, this interpretation appears to jar with the fact that PCCC is positively related to MPCE across households, so that an increase in MPCE would be expected to lead to an increase, not decline, in PCCC.

The findings of this paper suggest a somewhat modified reading of the evidence, whereby per capita income is not one of the main determinants of PCCC. Rather, PCCC depends mainly on factors such as education, occupation, region and food habits. The decline of PCCC over time possibly reflects changes in these factors including changes in occupational patterns and activity levels. The positive relation between PCCC and MPCE at a given point of time, for its part, appears to be driven by measurement errors or short-term (e g, seasonal) fluctuations in earnings, activity levels and energy requirements.

The remainder of the paper is divided into seven sections. Section 2 introduces to the two surveys (NSS and IHDS) and discusses the data. In Section 3 data from the NSS and the IHDS surveys are used to examine the relationship between PCCC and MPCE in rural areas. Section 4 compares expenditure-based and income-based Engel-curves from IHDS data. In Section 5 these relationships are re-examined in a multivariate framework. Section 6 presents a summary of the corresponding findings for urban India (all data presented before that section pertain to rural areas, unless stated otherwise). Section 7 concludes.

2 NSS and IHDS

The main source of NSS data used in this paper is Report No 508 (“Household Consumer Expenditure Survey”) based on the NSS 61st round, conducted across the country between July 2004 and June 2005. Data were collected from a sample of 79,298 rural and 45,346 urban households spread over 7,999 villages and 4,602 urban blocks, respectively (NSSO 2006). In comparison, the IHDS sample consists of only 27,010 rural and 13,126 urban households, covering “all states and union territories of India (with the exception of Andaman and Nicobar and Lakshadweep)”. Furthermore, “of the 612 districts in India in 2001, 382 are included in IHDS” and “the sample is spread across 1,503 villages and 971 urban blocks” (Desai et al 2010).

Table 1: Comparison of IHDS Data with Other Sources (in %)

Sources: Desai et al (2010, Table AI.3). SC: scheduled caste, ST: scheduled tribe.

The National Sample Survey Office (NSSO) has been carrying out surveys over the last 60 years. It is the most established data collection organisation in India. The IHDS, on the other hand, is the first survey of its kind and requires some scrutiny.

community, health and education taken from a larger list compiled by Desai et al (2010).

Much of the analysis in this paper focuses on three variables

– PCCC, MPCE and PCI. The main variable of interest is monthly PCCC, defined in both surveys as cereal consumption during the preceding 30 days. NSS reports include monthly PCCC (measured in kg) as “total cereals” which is the sum of rice, wheat, jowar, bajra, maize, barley, small millets, ragi and “other cereals”. Rice and wheat alone account for about 90% of total cereals. Monthly PCCC (measured in kg) is calculated from the IHDS data set as the sum of three items – wheat, rice and “other cereals”.

The NSS defines MPCE “for the household as the total consumer expenditure over all items divided by its size and expressed on a per month (30 days) basis” (NSSO 2006: 5). In the NSS, household consumer expenditure pertains to “the total of the monetary values of consumption of various groups of items, namely, (1) food, paan (betel leaves), tobacco, intoxicants and fuel and light, (2) clothing and footwear, and (3) miscellaneous goods and services and durable articles” (ibid: 4).3

The IHDS, on the other hand, measures MPCE on a mixed recall period. Questions regarding consumption expenditure “were modelled on the short form of the National Sample Survey” and data collection used “a mixed recall period with data for commonly used items restricted to the preceding 30 days” (Desai et al 2010).

In contrast, income data were collected for the preceding year. “The IHDS measure of income is summed across over 50 separate components including wages and salaries, net farm income, family business net income, property, and pension incomes”. Total household income measured by the IHDS pertains to the entire year. In this paper, PCI refers to annual per capita income as calculated from IHDS data, expressed in rupees per month (for comparability with MPCE).

3 Per Capita Cereal Consumption and Expenditure

From Table 1, which compares IHDS data Table 3: PCCC for Each MPCE Class (Rural India, 1993-2005; kg/month)

with data from the Census 2001, the NSS 0-5 5-10 10-20 20-30 30-40 40-50 50-60 60-70 70-80 80-90 90-95 95-100

1993-94 9.68 11.29 12.03 12.63 13.19 13.33 13.72 14.07 14.41 14.59 14.98 15.78

1999-2000 9.78 11.15 11.64 12.27 12.56 12.89 13.03 13.36 13.45 13.67 13.73 14.19

2004-05 9.88 10.87 11.33 11.70 11.98 12.16 12.37 12.61 12.77 12.72 12.77 13.50

% change 1993-2005 2.1 -3.7 -5.8 -7.4 -9.2 -8.8 -9.8 -10.4 -11.4 -12.8 -14.8 -14.4

Source: NSS Report No 508. The column headings give the percentile range of the relevant MPCE class, from the poorest

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figures across 12 MPCE classes for the three “thick rounds” of the NSS between 1993-94 and 2004-05. The largest decline in PCCC occurred among the top MPCE classes, with reductions of up to 15%. In fact, the higher the MPCE class, the larger is the percentage decline. Figure 1 plots Engel curves based on Table 3. The basic pattern is a clockwise “swing” over time, with larger PCCC declines for higher MPCE classes.

Figure 1: Per Capita Cereal Consumption for MPCE Classes (1993-2005, Rural India)

earlier between PCCC and MPCE. Indeed, there is hardly any change in PCCC across income classes. For the bottom decile, PCCC is 12.29 kg per month, just about 1% more than the 12.12 kg for the top quartile.

4.1 Comparison of Engel Curves

The contrast discussed in the preceding section can also be seen (more clearly) by looking at MPCE-based and PCI-based

Per Capita Cereal Consumption 11 13 15 1999-2000 2004-05

Engel curves of per capita cereal consumption. These Engel 1993-94 curves, estimated by non-parametric methods from IHDS data,

are plotted together in Figure 2.4 As expected, the MPCE-based Engel curve has a positive slope, relatively steep initially, and then gradually flattening. In contrast, the PCI-based Engel curve is more or less flat.

9

Figure 2: PCI-Based and MPCE-Based Engel Curves (Rural India, 2004-05)

5 10 20 30 40 50 60 70 80 95 100

50

MPCE classes (%)

MPCE-based PCI-based

0 500 1,000 1,500 2,000 Per capita income/per capita expenditure

Kernel = epanechnikov, degree = 0, bandwidth = 47.96Source: Author’s calculation from IHDS data. Q1, Q2, etc, indicate different quartiles of theper capita income scale. The diagram is restricted to per capita incomes below Rs 2,000, which include 90% of the observations.

Source: See Table 3.

Another pattern which emerges, to be compared with the income approach later on, is the relation between PCCC and MPCE at a point of time, i e, in a given year. One finds that PCCC increases across the 12 MPCE classes (Figure 1 and Table 3),

hence a positive relationship in every year.

To sum up, two key findings emerge from NSS data. First, there

Per Capita Cereal Consumption

40

30

20

10

is a positive and monotonic relationship between MPCE and PCCC across households; in other words, better-off households (in terms of MPCE) consume more cereals than poorer households. Second, over the last decades of rising MPCE levels, PCCC has declined, particularly (but not only) among higher MPCE classes.

3.2 Evidence from NSS and IHDS

It is now possible to compare the NSS 2004-05 findings with IHDS data for the same year. As Table 4 indicates, both surveys show a clear pattern of rising PCCC with higher expenditure

levels. The relationship be-Table 4: PCCC for MPCE Classes (Rural tween PCCC and MPCE is quite India, 2004-05; kg/month)

NSS IHDS

similar in both surveys, with,

0-5 9.88 9.35

if anything, a somewhat

5-10 10.87 10.18

higher expenditure elasticity

10-20 11.33 10.83

of PCCC in the IHDS data.

20-30 11.70 11.64 30-40 11.98 11.95

4 Per Capita Cereal

40-50 12.16 12.29 Consumption and Income 50-60 12.37 12.80

As mentioned earlier, the IHDS 60-70 12.61 12.71

data set includes the per capita 70-80 12.77 12.92 80-90 12.72 13.37

income, which provides a

90-95 12.77 13.68

unique opportunity to analyse

95-100 13.50 14.58

cereal consumption from an-

Source: NSS Report No 508 and author’s other angle. In this section, calculations from IHDS data.

IHDS data are used to compare Table 5: PCCC for PCI Classes (Rural India, 2004-05; kg/month)

expenditure-based with in-

Bottom decile 12.29

come-based variations in PCCC.

Bottom quartile 12.10

Table 5 presents a broad-

Second quartile 12.09

brush picture of the PCCC-PCI

Third quartile 12.21

relationship. Clearly, there is no

Top quartile 12.12

positive relationship between

Full sample 12.13 PCCC and PCI, as observed Source: Author’s calculations from IHDS data.

0

This pattern (a flat PCI-based Engel curve for cereals) contrasts sharply with positive income elasticities of demand for milk, vegetables and fruits. Note also that cereal expenditure rises with per capita income, even though cereal consumption (in quantity terms) does not. This is because higher-income households tend to purchase more expensive cereals.

4.2 Interpretation

The question why the two approaches (MPCE-based and PCIbased) yield such different Engel curves (Figure 2) remains. One may recall that the IHDS collected income data on a yearly basis, whereas expenditure data are based on a monthly recall period. It is a common practice of national survey organisations to collect expenditure rather than income data for the simple reason that total household income is difficult to measure. In rural India individuals often do not have regular occupations or permanent jobs. Therefore, total household income cannot be traced back to one income source alone, but needs to be derived from several sources which may vary seasonally. Because of the diversity of income sources and the irregular flow of income, it is difficult to measure income as accurately as monthly consumption expenditure.

In light of this, analysing the relationship between PCCC and PCI may seem implausible. If PCI were so badly measured as to be virtually random, the flat PCI-based Engel curve would simply reflect measurement errors. However, it can be shown that the IHDS’ PCI variable is not just a random variable. Indeed,

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it correlates very well, in the expected manner, with other socio-economic variables such as education, occupation, and so on. This would not be the case if PCI were just “noise”.

PCCC and MPCE, too, are exposed to measurement errors, and further, PCCC measurement errors and MPCE measurement errors are likely to be correlated, because MPCE is the sum of cereal expenditure and other expenditure. According to Deaton and Subramaniam (1996), each item of consumption “is certainly measured with some error” and since MPCE is the sum of many different items, including cereals, MPCE is also measured with error. While calculating the Engel curve for PCCC with respect to MPCE, spurious correlation occurs as “the error of measurement is positively correlated with the composite error term in the regression, itself partly determined by the measurement error in (cereals)”. As the lowest expenditure classes spend up to 35% on cereals (NSS Report No 508), this bias can be substantial, possibly reflected in the positive slope of the PCCC-MPCE curve.

The different slopes of the two Engel curves (MPCE-based and PCI-based) can also be plausibly related to the fact that consumption expenditure is measured on a monthly basis while income is measured on a yearly basis. Expenditure being monthbased may entail that PCCC with respect to MPCE is exposed to the monthly fluctuations of expenditure. For instance, fluctuations can occur with seasonality. In harvest seasons, there is more work for agricultural labourers than in slack seasons. Besides more income at their disposal individuals are likely to have higher energy requirements in those seasons, resulting in higher cereal consumption. This, in turn, leads to a positive slope for the MPCE-based Engel curve.

Monthly expenditure is also exposed to fluctuations arising from short-term adjustments in consumption. Certain months may require a tightening of the belt due to unforeseen events like crop failure, death or sickness, etc. Especially poor households who are not able to fall back on savings may then be forced to substantially adjust their consumption patterns. As other items of the household budget, e g, school fees, transport expenses or house rent may be difficult to adjust, alterations in Since NSS data suggest a positive relation between PCCC and MPCE, one would expect dalit and adivasi households to consume less cereals than the more affluent castes. Table 6,

however, suggests that Table 6: Per Capita Cereal Consumption there are, in fact, no signifi-and Monthly Per Capita Income by Caste/

Religious Group (Rural India, 2004-05; kg/

cant differences in PCCC

month)

across castes and religious Caste/ Religion PCCC PCI

groups. This is in line with High caste 11.83 888 OBC 12.28 614

the absence of any rela-

Dalit 12.26 499

tionship between PCCC

Adivasi 12.18 534

and PCI (and with the pro-

Muslim 12.2 588

posed interpretations of

Other 10 1807

this pattern, discussed in

OBC is Other Backward Classes. Source: Author’s calculation from IHDS data.

the preceding section).

Table 7: Per Capita Cereal Consumption

It is another story, how

and Per Capita Income by Occupation Group

ever, when one considers (Rural India, 2004-05)

Occupation PCCC PCI

occupation groups with

Cultivation 12.63 634

their respective activity

Agriculture 12.07 416

levels and energy require

Non-agri labour 12.06 449

ments. Table 7 shows

Profession/pension 11.95 1,084

average PCCC and PCI

Salaried 11.70 1,182

for different occupation

Artisan 11.62 769

groups, and suggests that	Other	11.54	684
those with occupations in-	Business	11.33	1,000
volving	manual	labour	Petty trade	11.19	695
consume	more	cereals	Total	12.13	646

Source: Author’s calculations from IHDS data.

than salaried or sedentary

Occupations (“main source of income”) are workers (Figure 3). That ranked in descending order of PCCC.

activity levels can play a role in determining cereal consumption has been discussed earlier by Deaton and Subramaniam (1996) and Deaton and Drèze (2009), among others. It is plausible enough that cereals act as a source of inexpensive calories for workers engaged in heavy labour, with low wages but high calorie requirements.

Figure 3: Per Capita Cereal Consumption and Per Capita Income by Occupation

(Rural India, 2004-05) 14

Cultivation

PC Cereal Consumption

Ag labour Pensi

cereal consumption may be the outcome. These could be reflected

in a short-term response of PCCC to MPCE even if there is no

relation between PCCC and per capita income on a yearly basis.

To illustrate the point, consider two identical agricultural

on

Allied ag Non ag labour Artisan Salaried

12

10

Business

Petty Trade

Profession

labour households who are surveyed in different months, say one in the peak season and the other in the lean season. Even though their yearly income would be identical (by assumption), total consumption expenditure and PCCC would be reported as low for the household interviewed in the lean season and high for the other one. Hence, a positive relation between PCCC and MPCE would emerge.

4.3 Other Observations

This section supplements the preceding analysis with brief observations on the relationship between PCCC and various socio-economic indicators.

Dalit (or scheduled caste) and adivasi (or scheduled tribe) communities are among the most deprived in rural India.

8 200 400 600 800 1,000 1,200 1,400 Per capita income Source: Author’s calculations from IHDS data.

Geographic differences in PCCC are also apparent, as illustrated in Figure 4 (p 67). From these substantial interstate differences it appears at first sight that poorer states have higher PCCC levels than richer states (also see Appendix Table A1, p 71). For example, Bihar with a mean PCI of Rs 408 and mean PCCC of 16.08 kg contrasts with Kerala where PCI is about four times as high and PCCC (8.24 kg) about half as much. This relationship, however, is weakened by a few states like Himachal Pradesh with relatively high levels of PCI and PCCC, and

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14-17 13-14 12-13 10.5-12 0-10-5 Missing PCCC (kg/month)

Source: Author’s calculations from IHDS data.

Jharkhand with low levels of PCI and PCCC. It seems that the interstate patterns may be as much a matter of geography and food habits as a matter of development or per capita income.

5 Regression Analysis

1.3 kg more cereals than individuals of the

default category (above poverty line and no

ration card). This suggests that, at a given level

of PCI or MPCE, having an Antyodaya card

ambiguous. In regressions (1) to (3) with state

dummies, the coefficient for BPL is not signi

Author’s calculations from IHDS data. ** significant at 1% level. * significant at 5% level.

Table 9B: Coefficients of State Dummies (Default State: Himachal Pradesh)

** significant at 1%, * significant at 5% level. States are ranked in descending order of column (1).

cereal consumption is lower in larger households. This is a well-known relationship, though it is not fully understood, as discussed in Deaton and Paxson (1998).5

Turning to education, years of schooling are negatively correlated with cereal consumption. It seems plausible that more years of education help to increase people’s awareness about

ficant. It is only when state dummies are excluded, in regressions (4) to (6), that it turns significant, but with a negative sign. It seems to be a puzzle that holders of BPL cards consume fewer cereals than the default category. This question is re-examined in the next section.

Coefficients for occupation dummies suggest that jobs with relatively low energy requirements (e g, professions, petty trade and business) are associated with lower PCCC. For instance, with income controlled for, PCCC is about 1.4 kg less for those employed in petty trade or business compared with those employed as cultivators (the default category). This is consistent with the idea that energy requirements may be an important determinant of cereal consumption (Deaton and Drèze 2009).

The state dummies are presented in Table 9B. With Himachal Pradesh (HP) as the default state, Bihar has the largest positive (and significant) coefficient. At a given level of per capita income, PCCC is about 3 kg higher in Bihar than in HP. This does not apply to neighbouring Jharkhand, where PCCC is about 2.7 kg lower than in HP. Generally, cereal consumption levels are lower in the southern states (Andhra Pradesh, Karnataka, Kerala, Tamil Nadu) and western region (Gujarat and Maharashtra). The coefficients indicate that PCCC can vary substantially across states even when other factors are controlled for: the difference between Bihar and Kerala is as large as 8 kg per month. It would be simple and perhaps plausible to attribute state differences in PCCC to regional tastes and preferences (Radhakrishna et al 2004; Murty 1998). But why individuals in

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Bihar should consume 6 kg more cereals per month Table 10A: OLS Regressions, Urban India (Dependent Variable: PCCC)

Independent Variables With State Dummies Without State Dummies

PCI 0.000** 0.000** 0.000** 0.000*

MPCE 0.000** 0.000** 0.000** 0.000**

R² 0.148 0.155 0.156 0.073 0.081 0.082

Prob F 0.000 0.000 0.000 0.000 0.000 0.000

F 47.22 49.91 49.09 54.20 60.44 57.89

Observations 13,633 13,633 13,633 13,633 13,633 13,633

Author’s calculations from IHDS data. ** significant at 1% level.

reach local – and otherwise remote-markets as well. Taking infrastructure as an indicator of development, Table 10B: Continuation of Table 10A: Coefficients of State Dummies

Bihar 2.250** 2.456** 2.446** of urban goods” individuals in rural areas, too, do increasingly Assam 1.585* 1.889** 1.881** make use of the opportunity to diversify their food baskets. Orissa 0.948 1.138 1.135 Table 10: Proportion of BPL and Antyodaya Punjab 0.499 0.664 0.667 Cardholders (Rural India, 2004-05; %)

Turning to the BPL puzzle, Chhattisgarh 0.435 0.691 0.678

Andhra Pradesh 73.02 0.86 one may recall that the Uttar Pradesh -0.036 0.132 0.121

Karnataka 64.81 3.20 Orissa 55.27 3.09 coefficient of the BPL Jharkhand -0.037 0.221 0.206 Tamil Nadu 50.89 0.07 dummy is negative (and Rajasthan -0.316 -0.141 -0.152 Karnataka -0.518 -0.368 -0.382

Gujarat 47.91 0.00 significant) in the regres-Jharkhand 43.15 2.82 Andhra Pradesh -0.859 -0.685 -0.7

Chhattisgarh 42.12 10.64 West Bengal -0.98-0.723 -0.737

Maharashtra 37.45 3.84 Uttarakhand -1.264 -1.058 -1.059

Kerala 35.27 0.00 Haryana -1.568* -1.582* -1.584*

Gujarat -1.765** -1.542* -1.561* Jammu and Kashmir 32.25 1.05 negative, after control-

Kerala -1.802** -1.524* -1.526* Madhya Pradesh 28.91 4.34 ling for income and ex-

Tamil Nadu -2.020** -1.804** -1.807** West Bengal 28.35 3.54 penditure. After all, BPL Author’s calculations from IHDS data. ** significant at 1% level. * significant at 5% level. Assam 26.32 0.81 States are ranked in descending order of column (1).

dised cereals through the In order to clarify the BPL puzzle, one should first notice that

PDS and may therefore be the negative coefficient is relatively small in magnitude, so that

expected to consume the difference in PCCC between BPL and the default category

more (or at least an equal (APL/non-cardholder) is only about 400gm a month. The state

Punjab 3.98 0.05 Source: Author’s calculation from IHDS data. amount) of cereals. analysis has revealed that PCCC in certain states like Bihar and

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Figure 5: PCI-Based and MPCE-Based Engel Curves (Urban India, 2004-05)

PCI-based MPCE-based 40 30 20 10 0

0 500 1,000 1,500 2,000 Per capita income/per capita expenditure

Kernel = epanechnikov, degree = 0, bandwidth = 100 Source: Author’s calculation from IHDS data. Q1, Q2, etc, indicate different quartiles of the per capita income scale. The diagram is restricted to per capita incomes below Rs 2,000 as in Figure 2.

Jammu and Kashmir is relatively high, and that PCCC in the southern states (among others like Gujarat) is predominantly low. Now, as Table 10 (p 69) indicates it is precisely the latter ones which have the highest shares of BPL cardholders. Therefore, one can think of the BPL dummy absorbing the state effects otherwise captured by state dummies. When state dummies are controlled for, it does not come into effect (the BPL coefficient is insignificant). Taking into account that the highest BPL card distributions are found in states with low PCCC, one can comprehend the negative relation between BPL and PCCC. Whether this correlation is due to state differences in the availability of markets, trade or eating habits needs to be proven. It is clear, however, that one cannot use the BPL dummy of regressions

(4) to (6) to measure the effectiveness of the PDS.

To sum up the regression analysis, income does not seem to play a significant role in determining PCCC. Instead, demographic variables, education, caste/religious background, and activity levels appear to be more influential.

6 Urban India

Most of the discussion so far pertains to rural areas. A similar analysis was carried out for urban areas, and the main findings are broadly similar. For lack of space, and to avoid repetition, detailed results are not presented. Briefly, the urban counterpart of Figure 2 is given in Figure 5, and the counterparts of Tables 9A and 9B are given in Tables 10A and 10B (p 69). Most of the earlier results apply in urban areas as well. In particular:

Having said this, there are one or two notable differences. For instance, in urban areas the slope of the MPCE-based Engel curve is positive only at very low levels of MPCE, in contrast with rural areas, where the positive slope persists until much higher in the MPCE scale (Figure 2). In urban areas, income volatility and fluctuations in expenditures are likely to be less, which may be one reason for a flatter MPCE-based Engel curve.

7 Concluding Remarks

The main findings of this paper have already been summarised in the introduction. Briefly, we found that PCCC in India is unrelated to PCI. Instead, it is influenced by factors such as education, occupation, region, demography and food habits. As mentioned in the introduction, our findings help to resolve earlier puzzles of food intake data in India, particularly the decline of cereal consumption over time.

A few concluding clarifications and qualifications are due. First, the findings are derived from a single data set (the IHDS) and require corroboration from independent sources.

Second, it stands to reason that at very low levels of income, there must be a positive relationship between cereal consumption and per capita income. Destitute households often skip meals or go hungry – nothing in this paper disputes this well-established fact. However, it appears that the proportion

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more expensive cereals.

Notes

1 See Deaton and Drèze (2009) and the subsequent exchange between Deaton and Drèze (2010a, b) and Patnaik (2010a, b). See also Kumar et al (2009) on the decline of cereal consumption in India.

2 The IHDS data set is available at www.ihds. umd.edu. For a comprehensive report on human development in India based on IHDS data, see Desai et al (2010). The National Nutrition Monitoring Bureau (NNMB) surveys collect income as well as food intake data, but no expenditure data (see e g, NNMB 1997).

3 Since in the NSS report “the discussion of the basic results of the survey uses data collected with “last 30 days” as reference period for all items of consumption as has been the usual practice in the quinquennial rounds” (NSSO 2006: 5), the uniform reference period also applies here, whenever we refer to NSS data.

4 The Epanechnikov Kernel function is used to calculate the Engel curves with the STATA “lpoly” command.

5 Deaton and Paxson (1998) show evidence across developed and developing countries that per capita cereal consumption declines with household size, keeping per capita expenditure constant.

6 Occupation dummies are included in the regressions, but they leave much room for occupational choices within these broad categories.

7 Similarly, the differences for the states of Jammu and Kashmir, Jharkhand, Kerala, Karnataka and Orissa are larger than 10%.

8 See also Desai et al (2010): “...we caution the

come from NSS data over the years.

45: 4, 441-70.

Mittal, Surabhi (2007): “What Affects Changes in Cereal Consumption?”, Economic Political Weekly, 3 February.

Murty, K N (1998): “Foodgrain Demand in India to 2020 – A Comment”, Economic Political Weekly, 14 November.

NNMB (1997): 25 Years of National Nutrition Monitoring Bureau, National Nutrition Monitoring Bureau, National Institute of Nutrition, Hyderabad.

NSSO (2006): “Level and Pattern of Consumer Expenditure, 2004-05”, Report 508, National Sample Survey Organisation, New Delhi.

Patnaik, Utsa (2010a): “A Critical Look at Some Propositions on Consumption and Poverty”, Economic Political Weekly, 6 February.

– (2010b): “On Some Fatal Fallacies”, Economic Political Weekly, 20 November.

Radhakrishna, R, C H Hanumantha Rao, C Ravi and B Sambi Reddy (2004): “Chronic Poverty and Malnutrition in 1990s”, Economic Political Weekly, 39(28), 10 July.

Rao, C H Hanumantha (2000): “Declining Demand for Foodgrains in Rural India: Causes and Implications”, Economic Political Weekly, 35(4), 22 January.

Appendix Table A1: PCCC, MPCE and PCI in Major States (Rural India, 2004-05)

Uttar Pradesh 13.37 12.91 3.55 662 533 24.29 470

Cereal Accounting”, Economic Political Weekly, 20 November. Uttarakhand 11.58 11.88 -2.54 726 647 12.18 670

West Bengal 12.96 13.19 -1.67 628 562 11.72 580

mies of Scale, Household Size, and the Demand for Food”, Journal of Political Economy, Vol 106, India 12.13 12.12 0.12 719 559 28.62 646

No 5, October, 897-930. Source: NSS report Nr 508 and author’s calculations from IHDS data. Economic Political Weekly

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