Are the Non-poor Households Nutritionally Secure? An Assessment from NSSO Unit Level Data in India Between 2004–2005 and 2011

Abstract

This article analyses the status of nutrition in relation to the status of poverty among the socio-economic groups in both rural as well as in urban India. It also examines the role of public distribution system (PDS) along with other socio-economic factors in the reduction of nutrition insecurity of poor as well as non-poor households on the basis of National Sample Survey Office (NSSO) unit level data of Consumer Expenditure Survey of the latest rounds. It was found that the incidence of poverty as well as nutrition insecurity has declined significantly in the country during the years 2004–2005 and 2011–2012. However, as much as 24.4 per cent non-poor households in 2011–2012 remained nutritionally insecure. They were higher among the non-poor upper caste households and in the urban areas. The latter spent more on food items, evident in the growth rate of monthly per capita food consumption expenditure (MPFCE). But higher nutrition insecurity because of lower value of calorie accompanied diversification of consumption pattern among these households in favour of protein and fat items. Notwithstanding this trend, level of education, food consumption expenditure, PDS benefits and cultivable land have favourable impact on the nutritional status of non-poor households.

Keywords

Poverty nutrition insecurity public distribution system NSSO India

Introduction

Food and nutritional security along with poverty are serious issues in both the developing and less developed countries. United Nations Development Programme (UNDP) has also addressed these issues in Sustainable Development Goals (SDGs). The first two goals of SDGs are set up for addressing poverty and nutrition insecurity. This reflects the insight that policies, programmes and processes to recover nutrition outcomes have an important role to play in the reduction of poverty and nutritional insecurity. Indian economy has experienced relatively high economic growth and decline in poverty over the past two decades (Bhagwati & Panagariya, 2012; Dreze & Sen, 2013). However, this transformation has not been matched by improvements in nutritional status (Desai et al., 2016). This incoherence is revealed in a number of puzzles: rising incomes and declining cereal consumption, sharp poverty decline and low improvement in nourishment, declining poverty and increasing use of the public distribution system (PDS) (Himanshu & Sen, 2013; IIPS & Macro International, 2007; MoSPI & WFP, 2019; National Sample Survey Organization [NSSO], 2014; Thorat & Desai, 2016). Indeed, according to the National Family Health Survey (NFHS-3 and NFHS-4), the percentage share of underweight children marginally declined from 42.5 per cent in 2005–2006 to 35.7 per cent in 2015–2016 (IIPS & ICF, 2017). Undernourishment, infant and child mortality rate, etc., in India remains in the worst position than most of the countries of Sub-Saharan Africa. In respect of the Global Hunger Index the hunger level of India was in the ‘alarming’ category during 2000–2005. Since then, India has been able to reduce its score and is placed in the ‘serious’ category. The position of India came down during recent years, from 100 in 2017 to102 in 2019 (IFPRI, 2019).

Table 1 shows the trends of per capita calorie, protein and fat consumption in rural as well as in urban India for three decades starting from 1983. The per capita consumption of calories reduced gradually in rural India up till 2009–2010. Similar trend was observed in urban India too. This decline in per capita calorie consumption has also been pointed out by Rao (2001), NSSO (2011), Patnaik (2004, 2007), Meenakshi and Viswanathan (2006), Suryanarayana and Silva (2007) and Deaton and Dreze (2009). In rural India, per capita calorie consumption was 2,240 kcal/day in 1983 which decreased to 2,020 kcal/day in 2009–2010 and revived thereafter to 2,099 kcal/day in 2011–2012. In its urban counterpart, it declined from 2,070 kcal/day in 1983 to 1,946 kcal/day in 2009–2010 and improved thereafter to 2,058 kcal/day. Srivastava and Chand (2017) also pointed out that even if the year 2009–2010 is excluded from the trend, considering it abnormal because of severe drought (GoI, 2013) and lower food availability in the country, the per capita calorie intake in the year 2011–2012 shows increase over the year 2004–2005. The similar trend is also observed in case of per capita per day protein consumption. But the per capita per day fat consumption, executed in the last two columns in Table 1, shows upward movement. It is a matter of concern that the consumption pattern of India is diversified from carbohydrate and protein to fat consumption. Further, the gap between the rural and urban India in respect of consumption pattern of calories, protein and fat is reducing gradually.

Table 1.

Per Capita per Day Consumption of Calories, Protein and Fat, 1983 to 2011–2012

Year	NSSO Rounds	Calories (kcal)		Protein (g)		Fats (g)
Year	NSSO Rounds	Rural	Urban	Rural	Urban	Rural	Urban
1983	38	2,240	2,070	63.5	58.1	27.1	37.1
1987–1988	43	2,233	2,095	63.2	58.6	28.3	39.3
1993–1994	50	2,153	2,073	60.3	57.7	31.1	41.9
1999–2000	55	2,148	2,155	59.1	58.4	36	49.6
2004–2005	61	2,047	2,020	55.8	55.4	35.4	47.4
2009–2010	66	2,020	1,946	55.0	53.5	38.3	47.9
2011–2012	68	2,099	2,058	56.5	56.0	41.6	52.5

Source: Authors’ estimation from NSSO unit level data of 38th, 43rd, 50th, 55th, 61st, 66th and 68th round survey of household consumer expenditure.

Table 2.

Distribution of Population by the Level of Calorie Consumption in India, 1993–1994 and 2011–2012

Level of Calorie Consumption (kcal)	1993–1994			2011–2012
Level of Calorie Consumption (kcal)	Rural	Urban	Total	Rural	Urban	Total
Up to 2,089	49.8	57.5	51.7	35.3	39.7	36.6
Up to 2,100	0.7	0.8	0.8	0.7	0.8	0.7
Up to 2,154	3.8	3.7	3.7	4	3.8	3.9
Up to 2,400	14.6	13.6	14.3	17.6	16.8	17.4
Above 2,400	31.1	24.4	29.5	42.4	38.9	41.4
Total	100	100	100	100	100	100

Source: Authors’ estimation from NSSO unit level data of 50th and 68th round survey of household consumer expenditure.

The distribution of the population by the level of calorie consumption (kcal) in rural, urban and India as a whole is shown in Table 2. During 1993–1994 to 2011–2012, the percentage share of people in the lower calorie consumption classes has declined whereas the percentage share of people in the higher calorie consumption classes has increased in both rural and urban areas. It implies that the nutrition status of the people improved over time. Deaton and Dreze (2009) and Srivastava and Chand (2017) also observed that the percentage share of undernourished people in India increased from 67 per cent in 1993–1994 to 76 per cent in 2004–2005 and reduced thereafter to 72 per cent in 2011–2012. But during this period the poverty reduced at a higher rate. The percentage of population below the poverty line fell from 45.3 per cent in 1993–1994 to 37.2 per cent in 2004–2005 and further to 21.9 per cent in 2011–2012 based on Tendulkar methodology of estimation of poverty (GoI, 2014). The high incidence of undernourishment together with the low incidence of poverty indicates that not only poor people but also a significant portion of non-poor people are nutritionally insecure. Therefore, the analysis of the status of nutritional insecurity for poor vis-à-vis non-poor households is enviable. The present study analyses the status of nutrition in relation to the status of poverty among the socio-economic groups in both rural as well as in urban India and also examines the role of PDS along with other socio-economic factors for reducing nutrition insecurity of poor vis-à-vis non-poor households on the basis of NSSO unit level data of Consumer Expenditure Survey (CES) for the period 2004–2005 to 2011–2012.

Literature Review

The reduced calorie consumption could be attributed to advancements in transport, water and electricity supply, and other facilities, reducing physical effort, and the poor rural households may have curtailed their cereal intake in line with the reduced physical exertion. This has been treated as a puzzle in the Indian perspective (Deaton & Dreze, 2009; Gaiha et al., 2012). Another explanation (Landy, 2009) which attributes to the reduction of calorie intake of lower income classes is the tendency to consume region-specific foodstuffs based on their cultural references which may not often meet the required calorie norms. Gaiha et al. (2010) studied the nutrient intake in India during 1993–1994 to 2004–2005 and observed that food habits have changed in the downward direction in terms of calorie, protein and other nutrients. To tackle various challenges of food and nutritional insecurity Swamy and Ramesh (2018) suggested that the focus should be given on crop diversification and more on public and private investment which should be made in rural areas for generating more income from non-farm sectors. Kumar et al. (2017) observed that overtime the diversity in food consumption has turned out to be more obvious and illustrates its optimistic impact on nutrition security. Basar and Das (2018) showed that nutrition insecurity depends on various socio-economic and demographic characteristics as well as on the preference of the consumption items of the households.

Swain (2008), Ride et al. (2006), Ghosh (2006), Sen and Himanshu (2013), Himanshu (2013), Rana and Goli (2017) and Jha and Acharya (2016) pointed out that public expenditure-led social safety nets like PDS, ICDS can significantly reduce poverty, food insecurity and malnutrition. Khera (2011) studied the effectiveness of India’s PDS as a food security intervention, using ﬁeld survey data collected from Rajasthan. She found that the PDS affects the composition (away from more nutritious ‘coarse cereals’ to less nutritious cereals), rather than level of cereal consumption. Srivastava and Chand (2017) observed that the sufficiently large increase in income along with improved PDS after 2004–2005 has triggered the upward trend in energy intake during recent years.

Memane (2019) and Narayana (2017) found that people in the rural areas are poor as well as food insecure only because they could not avail various types of public works, food and nutrition programmes. Radkar (2017) focused on the extent and proximate determinants of undernutrition in India and suggested that together with supplementary nutrition programmes, equitable healthcare access is also necessary to eradicate undernutrition in India. Das and Sengupta (2017) found the effect of specific factors on well-being in terms of food security, poverty and inequality across different castes and religions in India with the help of NSSO unit level data. For food and nutritional security, Gopalan (1995) and Muralikrishnan et al. (2017) highlighted the importance of unequal distribution relative to inadequate production of food. Swaminathan (2003), Dreze (2004) and Vyas (2000) analysed the significance of market and civil society for carrying out a vital role in reducing food insecurity. Basu (2011) underlined the difficulties of high food inflation and lack of storage facilities in India resulting in food and nutrition insecurity. Tandon and Lands (2011) examined the dependency of nutrition insecurity on the household’s behaviour. Waibel and Hohfeld (2016) analysed the relationship between nutrition insecurity and poor in two Asian countries and found that poverty and income influences nutrition outcomes, but other factors such as mother’s height, education, migration and sanitation too have a significant contribution.

From the brief review of existing literature on nutrition insecurity it is revealed that there is hardly any study that analyses the nutrition status of non-poor households. Existing studies are also deficient in analysing the status of nutrition of the poor vis-à-vis non-poor households across regions and social castes. The role of PDS to overcome poverty of the households was analysed in a number of studies but its impact on nutrition status of poor and non-poor households is not yet analysed. Thus, the present study explores these and allied issues in details in Indian context.

Database and Methodology

The study is largely based on NSSO unit level data of CES: 61st round in 2004–2005 and 68th round in 2011–2012. These two rounds are quinquennial rounds with a much larger sample and 68th round is the latest one in respect of CES. The interim 66th round in 2009–2010 is not considered because of its abnormality (GoI, 2013; Srivastava & Chand, 2017). The 61st and 68th rounds were conducted after the expansion of the targeted public distribution system (TPDS); therefore, it is apt to study its effect on nutrition. The present study which estimates and analyses poverty and nutrition insecurity is based on these NSSO unit level data.

Estimation of Poverty

Poverty is usually calculated in India by the means of poverty line. In the present study to analyse the poverty in India during 2004–2005 to 2011–2012 we have used the poverty line specified by the Planning Commission following the Expert Group (Tendulkar) methodology (GoI, 2014). The status of poverty is measured by using the methodology of Foster et al. (1984) as

p_{α} = \frac{1}{N} \sum^{​} ​ {(\frac{z - e_{i}}{z})}^{α}; α = 0, 1 a n d 2

where Z is the poverty line, e_i is the expenditure of the ith household, N is the number of total population, q is the number of poor people having expenditure less than Z and α is a measure of sensitivity.

When,

α = 0, p₀ implies head count ratio (HCR) of poverty (i.e., incidence of poverty),

α = 1, p₁ implies poverty gap (i.e., depth of poverty) and

α = 2, p₂ implies square poverty gap (i.e., severity of poverty).

The HCR of poverty is defined as the percentage of people whose monthly precipitate expenditure is below the poverty line. Besides the HCR, some questions are arising regarding how poor are the poor on average? How poor are the poorest of the poor? These questions can be answered by analysing the depth and severity of poverty. The poverty gap (depth of poverty) captures the average shortfall in expenditure of the population living below the poverty line. Squaring the poverty gap (severity of poverty) gives an indication of inequality existing within the population living below the poverty line (Delamonica & Minujin, 2007; Gencer, 2020; Pathak & Mishra, 2013; Touray, 2016). It thus emphasises the shortfalls of the poorest persons (Foster, 2006).

Estimation of Nutrition Insecurity

In order to measure poverty Planning Commission of India has specified the poverty line but no such line is defined for food and nutrition insecurity by the Commission. Thus, on the basis of certain norms we have formulated the nutrition insecurity line. The Indian norms of per capita per day calorie requirement, 2,400 kcal in rural and 2,100 kcal in urban, was established nearly four decades ago (GoI, 1979) and also recommended by the Nutrition Expert Group (1968). Deaton and Drèze (2009) also considered these calorie norms and cited it as ‘minimum requirements’, to explore the facts and interpretations of food and nutrition security in India during 1983 to 2004–2005. Qadeer et al. (2016) used the NSSO data to analyse the trend of calorie intake during 1993–1994 to 2009–2010 based on this ‘minimum requirements’ of calories. Srivastava and Chand (2017) had also used this ‘minimum requirements’ of calories to estimate the incidence (HCR) of undernourishment in India on the basis of CES of NSSO. In this context the present study considers this ‘minimum requirements’ of per capita per day calorie consumption to distinguish between nutrition secure and nutrition insecure households for the year 2004–2005.

In 2010, Indian Council of Medical Research (ICMR) reduced the Recommended Dietary Allowances (RDAs) of calories compared to their earlier RDAs of 1990 (ICMR, 1990, 2010). The reduced average calorie norm—based on revised ICMR RDAs—was used as a cut-off to estimate the incidence of poverty by the Expert Group to Review the Methodology for measurement of poverty in 2012. The newly formed average calorie requirements are 2,154.91 kcal/day/person for rural areas and 2,089.35 kcal/day/person for urban areas (GoI, 2014). Thus, we have considered these ‘minimum requirements’ of calorie to distinguish between nutrition secure and nutrition insecure households for the year 2011–2012.

The estimation of calorie intake based on NSSO data is done by transforming the reported consumption quantities of food items into calorie figures. Nutrient values of different items are largely based on ‘Nutritive Values of Indian Foods’ (Gopalan et al., 1980) which was revised and modified by Narasinga et al. (1991). In the nutrition table, the calorie values of the original items are only given. For the calorie value of the by-products of the particular item we have considered the calorie value of the original item. Foster et al. (1984) methodology is applied to measure the status of nutrition insecurity:

N I_{α} = \frac{1}{N} \sum^{​} ​ {(\frac{\overset{─}{C} - C_{i}}{\overset{─}{C}})}^{α}; α = 0, 1 a n d 2

where N is the number of total population and n is the number of nutrition insecure people and $\overset{─}{C}$ is the ‘minimum requirements’ of calories (per capita per day) and C_i is the actual per capita per day consumption of calories of the ith person.

When,

α = 0, NI₀ denotes the incidence of nutrition insecurity (INI),

α = 1, NI₁ denotes the depth of nutrition insecurity (DNI) and

α = 2, NI₂ denotes the severity of nutrition insecurity (SNI).

The interpretation of three measurements relating to status of nutrition insecurity is quite similar to the measurement of poverty. The INI is defined as the percentage of people whose daily calorie intake is below the ‘minimum requirement’. The DNI indicates shortfall in calorie intake of the population living below ‘minimum requirement’ of calorie and SNI gives an indication of inequality in respect of calorie intake among the population living below the ‘minimum requirement’ of calorie. The SNI also indicates the shortfalls of calorie intake of the most nutritionally insecure person.

Multinomial Logit Model to Analyse the Status of the Households

The nutrition status of the households (i.e., nutrition secure or nutrition insecure) in relation to poverty (i.e., poor or non-poor) is classified into four groups: non-poor as well as nutrition secure (non-poor_nutri secure), non-poor but nutrition insecure (non-poor_nutri insecure), poor but nutrition secure (poor_nutri secure) and poor as well as nutrition insecure (poor_nutri insecure). These four possible outcomes are specified as 0, 1, 2 and 3 and hence the nutrition status of the households is multinomial in nature. A multinomial logit (MNL) model is called for to estimate the nutrition status of the households in relation to poverty (Y). Here we have pooled the sample households of 2 years (2004–2005 and 2011–2012). For household i with time t it the MNL is stated as follows:

Y_{i t} = {X^{'}}_{i t} β + ε_{i t}

The MNL model with j number of categories (j = 0, 1, 2 and 3) specifies that

p_{i j} = \frac{\exp ({X^{'}}_{i t} β_{j})}{\sum_{j = 1}^{m} \exp ({X^{'}}_{i t} β_{j})}

where X_it are case-specific regressors which includes an intercept and household’s characteristics along with PDS benefits. Clearly, this model confirms that 0 < p_ij <1 and $\sum_{j = 1}^{m} p_{i j} = 1$ . To ensure model identification, is set to zero for one of the categories, and coefficients are then interpreted with respect to that category, called the base category. ε_it is the random error term (Cameron & Trivedi, 2010).

Status of Poverty and Nutrition Insecurity in India

Figure 1 shows the incidence, depth and severity of poverty in rural, urban and India as a whole for the years 2004–2005 and 2011–2012. The incidence of poverty (i.e., HCR) in India declined from 37.8 per cent in 2004–2005 to 22.0 per cent in 2011–2012 which exactly reflects the estimates of Planning Commission (GoI, 2014). The incidence of poverty reduced in both rural and urban areas but it was still higher in the former areas than the latter areas. In rural India it reduced by 16 percentage points and in urban India it reduced by 12 percentage points. The depth and severity of poverty in both the rural and urban areas have observed a declining trend during this period and were higher in rural India. The sharp reduction of poverty during 2004–2005 to 2011–2012 is accounted for the acceleration of per capita income and per-capita food production including livestock and fish products (Srivastava & Chand, 2017), increase of farmers’ income and wages of agricultural labour (Chand et al., 2015) and the expansion of PDS benefits (Dreze & Khera, 2013; Himanshu, 2013; Thomas & Chittedi, 2019).

The status of nutrition insecurity in India is shown in Figure 2. The share of nutrition insecure people (INI) decreased from 74.5 per cent in 2004–2005 to 39.9 per cent in 2011–2012. Deaton and Dreze (2009) also observed similar trends for the period 1983 to 2004–2005. The reduction was also observed in the DNI from 17.5 per cent in 2004–2005 to 6.5 per cent in 2011–2012. There has been reduction in the SNI as well from 5.6 per cent in 2004–2005 to 1.6 per cent in 2011–2012. Thus, it can be concluded that the overall status of nutrition security has improved in India during the considered period.

Rural as well as urban India has experienced a reduction in nutrition insecurity during 2004–2005 to 2011–2012. In rural India, INI decreased from 78.3 per cent in 2004–2005 to 40 per cent in 2011–2012. Together with INI, DNI also decreased from 19.1 per cent to 6.5 per cent whereas SNI decreased from 6.3 per cent to 1.6 per cent. Similar change was also observed in urban India. The distinctive feature is that the rural and urban gap narrowed down during this period but still INI and DNI were marginally higher in rural areas. On the other hand, in case of SNI which reflects the inequality of calorie consumption of nutritionally insecure people was marginally higher in urban India in the year 2011–2012 (Table 3).

Figure 1.

Source: Authors’ estimation from NSSO unit level data of CES of 61st and 68th rounds.

Figure 2.

Source: Authors’ estimation from NSSO unit level data of CES of 61st and 68th rounds.

Across social castes, the INI was higher for the Scheduled Tribes (STs) and Scheduled Castes (SCs) and the former happened to be in relatively worse position in the rural areas whereas the latter was found to be in relatively worse position in the urban areas. During 2004–2005 to 2011–2012 there has been a reduction of INI for all social castes. This reduction was higher among SCs (42 percentage points) and STs (39.9 percentage points) and was the least among Other Backward Castes (OBCs) (36.7 percentage points) in the rural areas. At the same time in the urban areas the decline was the highest for SCs (26.5 percentage points) followed by General castes (25.3 percentage points), STs (24.1 percentage points) and OBCs (21.8 percentage points) (Table 4).

Table 3.

Incidence, Depth and Severity of Nutrition Insecurity in India, 2004–2005 and 2011–2012

	Rural		Urban
	2004–2005	2011–2012	2004–2005	2011–2012
Incidence of nutrition insecurity (INI)	78.3	40.0	63.4	39.7
Depth of nutrition insecurity (DNI)	19.1	6.5	12.7	6.4
Severity of nutrition insecurity (SNI)	6.3	1.6	3.7	1.7

Source: Authors’ estimation from NSSO unit level data of CES of 61st and 68th rounds.

Table 4.

Incidence of Nutrition Insecurity by Castes in India, 2004–2005 and 2011–2012

Social Groups	2004–2005			2011–2012
Social Groups	Rural	Urban	Total	Rural	Urban	Total
Schedule Tribes (STs)	87	66.7	85.3	47.1	42.6	46.6
Schedule Castes (SCs)	82.9	72.5	80.8	40.9	46.0	42.0
Other Backward Classes (OBCs)	77.7	65.3	75.0	41.0	43.5	41.7
General castes	71.9	58.7	66.9	33.7	33.4	33.5
Total	78.3	63.4	74.5	40.0	39.7	39.9

Source: Authors’ estimation from NSSO unit level data of CES of 61st and 68th rounds.

Food Consumption Expenditure by the Status of Poverty and Nutrition Insecurity

The present section deals with the variation of monthly per capita food consumption expenditure (MPFCE) in real term (i.e., in constant price, base = 2004–2005) by the status of poverty and nutrition security in rural and urban India. MPFCE of households has increased overtime in both rural and urban areas. It was ₹322.5 in rural areas and ₹464.4 in urban areas in the year 2004–2005 which increased to ₹410.8 and ₹602.6, respectively, in 2011–2012. The annual average growth rate of MPFCE was higher in both rural and urban areas during 2004–2005 to 2011–2012 for non-poor households than that of poor households. This growth rate of MPFCE for non-poor households was higher in the urban areas while that for poor households it was higher in the rural areas (Table 5).

The poor and non-poor households were further disaggregated into nutrition secure and nutrition insecure households on the basis of ‘minimum requirement’ of calorie consumption. Among the poor, MPFCE of the nutritionally insecure households increased marginally in both rural and urban areas although it was less in the former than the latter areas. For the non-poor, the gap between rural and urban areas in terms MPFCE of the nutritionally insecure households increased overtime (Table 6).

Table 5.

Monthly per Capita Food Consumption Expenditure (MPFCE) (in ₹) of the Poor and Non-poor

Households of India, 2004–2005 and 2011–2012
Category of the Households	2004–2005		2011–2012		Growth Rate
Category of the Households	Rural	Urban	Rural	Urban	Rural	Urban
Non-poor households	398.2	534.9	468.9	653.4	3.0	3.7
Poor households	217.1	260.4	243.2	282.1	2.0	1.4
Total	322.5	464.4	410.8	602.6	4.6	5.0

Source: Authors’ estimation from NSSO unit level data of CES of 61st and 68th rounds.

Table 6.

MPFCE of Poor and Non-poor Households by Their Nutrition Status in Rural and Urban India, 2004–2005 and 2011–2012

Category of the Households (HHS)	Status of Nutrition	2004–2005		2011–2012
Category of the Households (HHS)	Status of Nutrition	Rural	Urban	Rural	Urban
Non-poor HHs	Nutrition secure	495.9	652.9	509.7	743.1
	Nutrition insecure	347.4	434.9	374.1	480.2
	Total	398.2	534.9	468.9	653.4
Poor HHS	Nutrition secure	260.6	307.7	265.2	311.8
	Nutrition insecure	215.1	255.4	233.1	272.3
	Total	217.1	260.4	243.2	282.1

Source: Authors’ estimation from NSSO unit level data of CES of 61st and 68th rounds.

Poverty and Nutrition Insecurity in Relation to Public Distribution System Benefit

Of all the safety net programmes that exist in India, the most far-reaching in terms of coverage as well as public expenditure on subsidy is the PDS. PDS is operated under the joint responsibility of the central and the state governments in India. The drought and food shortages of the mid-1960s highlighted the need for strengthening and continuing with a system of food distribution and the PDS was made a universal scheme in the 1970s. Corruption and high operational costs were among the reasons that were used to justify the move to the revamped public distribution system (RPDS) (Khera, 2011). The RPDS was launched in June 1992 in 1,775 blocks throughout the country which was later replaced by the TPDS in 1997 (GoI, 1997). The TPDS was long lasting, that is, for more than one and a half decade in India till 2013. The households who did not own any card declined from 19 per cent in 2004–2005 to 14 per cent in 2011–2012. The proportion of households holding below poverty line (BPL) or Antyodaya Anna Yojana (AAY) cards increased from 36 per cent to 42 per cent during this period. Much of this increase was due to the expansion of the AAY programme. The food subsidy has also increased from ₹237.93 billion in 2004–2005 to ₹723.71 billion in 2011–2012 (GoI, 2016). The exclusion error has also declined during this period (Dreze & Khera, 2015; Khera, 2011). All these evidence indicate that there was a remarkable upsurge in PDS benefits during 2004–2005 and 2011–2012.

Table 7 reveals that in 2004–2005, about 27 per cent of all households purchased cereals from the PDS whereas by 2011–2012, this proportion had raised to 52.3 per cent. This expansion of PDS benefits is also reflected from the monthly contribution of PDS (i.e., percentage share of consumption from PDS to total consumption of the households) in consumption of rice and wheat as well as from the percentage share of household’s consumption from PDS. Monthly per capita PDS benefits almost doubled in 2011–2012 as compared to 2004–2005 in both rural and urban areas. The contribution of PDS also increased during this period in both rural and urban India. It increased from 13.2 per cent to 29.9 per cent in case of rice and from 7.3 per cent to 17.3 per cent in case of wheat during 2004–2005 to 2011–2012 in rural areas. These shares although increased in case of both rice and wheat but were little lower in urban areas. The aggregate consumption of rice and wheat declined during this period indicating the diversification of the consumption basket. Dreze and Khera (2013) estimated that PDS reduces the poverty-gap index of rural poverty by 18 per cent to 22 per cent in India. The corresponding figures were much larger for states with a well-functioning PDS, for example, 61 per cent to 83 per cent in Tamil Nadu and 39 per cent to 57 per cent in Chhattisgarh.

The distribution of poor and non-poor households by their status of nutrition security for different levels of monthly per capita PDS benefit (kg) in terms of rice and wheat helps us to understand the importance of PDS in nutrition security. For the poor households, the percentage share of nutrition secure households had increased with the increase in per capita PDS benefits. Among the poor households with lower level of PDS benefits (e.g., less than 10 kg per month) 95 per cent were nutrition insecure in 2004–2005. The percentage share of nutrition insecure households was relatively low (58.3% in 2004–2005) for the poor households with higher level of PDS benefits (15.1 kg and above). In 2011–2012, 88.7 per cent of poor households with PDS benefits of 15.1 kg and above were nutrition secure. Thus, the expansion of PDS after 2004–2005 leads to the reduction of nutrition insecurity of the poor households to a great extent. Likewise, the PDS benefits also played an important role in reducing the nutrition insecurity among the non-poor households (Table 8). The TPDS has provided a critical nutritional supplement to the people in India. The estimated average per capita supplementary energy from TPDS was 453 kcal/day in rural areas and 159 kcal/day in urban areas in 2011–2012 while for the poor it was around 339 kcal/day (MoSPI & WFP, 2019).

Table 7.

Monthly per Capita Consumption of PDS and Its Contribution in Rural and Urban India, 2004–2005 and 2011–2012

	2004–2005			2011–2012
	Monthly per Capita Consumption (kg)		Contribution of PDS (%)	Monthly per Capita Consumption (kg)		Contribution of PDS (%)
	PDS	Other Source	Contribution of PDS (%)	PDS	Other Source	Contribution of PDS (%)
Rural
Rice	0.839	5.537	13.2	1.67	4.306	27.9
Wheat	0.307	3.885	7.3	0.744	3.544	17.3
Urban
Rice	0.53	4.181	11.3	0.882	3.605	19.6
Wheat	0.167	4.192	3.8	0.406	3.605	10.1

Source: Authors’ estimation from NSSO unit level data of CES of 61st and 68th rounds.

Table 8.

Distributions of Poor and Non-poor Households by Their Status of Nutrition Security for Different Levels of Monthly Per Capita PDS Benefit (in kg)

Households Type		2004–2005				2011–2012
Households Type		0–5 kg	5.1–10 kg	10.1–15 kg	15.1 kg and Above	0–5 kg	5.1–10 kg	10.1–15 kg	15.1 kg and Above
Poor households	Nutrition secure	5.1	5.2	11.1	41.7	13.8	26.0	50.1	88.7
	Nutrition insecure	94.9	94.8	88.9	58.3	86.2	74.0	49.9	11.3
	Total	100	100	100	100	100	100	100	100
Non-poor households	Nutrition secure	37.8	31.9	55.5	88.7	55.1	56.3	79.0	98.5
Non-poor households	Nutrition insecure	62.2	68.1	44.5	11.3	44.9	43.7	21.0	1.5
	Total	100	100	100	100	100	100	100	100

Source: Authors’ estimation from NSSO unit level data of CES of 61st and 68th rounds.

Status of Nutrition among Poor and Non-poor Households in India

The distribution of the households in respect of their status of nutrition with that of the status of poverty for the years 2004–2005 and 2011–2012 is presented in Figure 3. It was explored that the percentage share of non-poor_nutri secure households increased from 23.5 per cent in 2004–2005 to 53.3 per cent in 2011–2012. In 2004–2005, the percentage share of poor_nutri insecure households was 35.7 per cent in contrast with 38.8 per cent of non-poor_nutri insecure households. In the year 2011–2012, their corresponding shares came down to 15.5 per cent for the former and 24.4 per cent for the later. But the share of non-poor_nutri insecure was larger than poor_nutri insecure households. The fact which can be addressed is that a significant portion of non-poor households were nutritionally insecure which implies that even though they had purchasing power but they spent more on non-food items or else they do not have enough knowledge to select appropriate food items which gives them requisite calories.

Figure 3.

Source: Authors’ estimation from NSSO unit level data of CES of 61st and 68th rounds.

As regards to the location the poor_nutri insecure household decreased in both rural and urban areas. During 2004–2005 to 2011–2012 it declined from 40.0 per cent to 17.6 per cent in rural areas and from 23.2 per cent to 10.3 per cent in urban areas. Although the reduction was higher in the rural areas but still the percentage share remained high in the rural areas as compared to the urban areas. The percentage share of non-poor_nutri insecure also decreased in both rural and urban areas. A significant portion of the rural located households were non-poor_nutri insecure which accounted to be 38.3 per cent in 2004–2005 and 22.4 per cent in 2011–2012. In case of urban India, the corresponding shares were 40.2 per cent and 29.4 per cent respectively. That is, urban India inhabited a higher level of non-poor nutrition insecure household as compared to rural India (Table 9).

Table 9.

Percentage Shares of Households by Their Status of Nutrition in Relation to the Status of Poverty in Rural vis-à-vis Urban India, 2004–2005 and 2011–2012

Category of Households	Rural		Urban
Category of Households	2004–2005	2011–2012	2004–2005	2011–2012
Non-poor_nutri secure	19.9	51.9	34.1	56.9
Non-poor_nutri insecure	38.3	22.4	40.2	29.4
Poor_nutri secure	1.8	8.1	2.5	3.4
Poor_nutri insecure	40.0	17.6	23.2	10.3

Source: Authors’ estimation from NSSO unit level data of CES of 61st and 68th rounds.

Table 10.

Percentage Shares of Households by Their Status of Nutrition in Relation to the Status of Poverty Across Social Castes in India, 2004–2005 and 2011–2012

Category of households	ST		SC		OBC		General
Category of households	2004– 2005	2011– 2012	2004– 2005	2011– 2012	2004– 2005	2011– 2012	2004– 2005	2011– 2012
Non-poor_nutri secure	11.9	40.4	16.4	47.8	23.0	52.0	31.9	63.3
Non-poor_nutri insecure	28.2	19.0	32.7	22.3	39.2	26.1	45.1	24.9
Poor_nutri secure	2.9	13.0	2.8	10.2	2.0	6.3	1.1	3.2
Poor_nutri insecure	57.1	27.6	48.1	19.8	35.7	15.6	21.8	8.7

Source: Authors’ estimation from NSSO unit level data of CES of 61st and 68th rounds.

Across social groups, higher percentage of poor_nutri insecure households belonged to STs and SC. This share has declined across all social castes during 2004–2005 to 2011–2012 and the decline was the highest among the STs (29.5 percentage points) followed by SCs (28.3 percentage points). In contrast the percentage share of non-poor_nutri insecure households was relatively high among General castes and OBCs (Table 10).

Econometric analysis of the Status of Nutrition at the Household Level in India

The present section analyses the determinants of the status of nutrition in relation to the status of poverty of the households in India. The factor hypothesised to influence the variation in the status of nutrition with that of the status of poverty can be grouped into four categories: cultural, social, demographic and economic.

Cultural factor is specified as average years of education (AVED) of the households. Better educated households have better chances of getting higher wage or salaried jobs and have better knowledge how to absorb food. Hence for a balanced diet, knowledge of nutrition and good dietary practices are important which is influenced by the level of education.

The social factors are specified by the castes of the households which are defined by three dummy variables: ST, SC and OBC. Upper caste households have better access to physical capital and different dynamics of development thus enjoying higher nutritional status. But at the same time, special emphasis is given for the development of socially backward castes (STs and SCs). Therefore, STs and SCs may also experience better nutritional outcomes.

Three demographic factors are used in our analysis: size of the households (HHSZ), female headed households (FHS) and age of the head of the household (AGEH). Large sizes household has more members and more member means greater chances to be nutrition insecure due to the adverse effect on per capita dietary intake of energy (Srivastava & Chand, 2017). Higher age of the head denotes that he or she will be more rational in decision making hence being nutritionally secure. In case of FHS the prevalence of nutrition insecurity is higher (Negesse et al., 2020).

Economic factors are specified as employment status, MPFCE in real terms, per capita cultivable land (PCLAND) and food safety net programme. If the household has regular employee (RE) than there is more chance of being nutrition secures. The MPFCE may directly affect nutrition security. To articulate the PDS benefits households are classifying into four categories (very low, low, medium and high) on the basis of their per capita monthly PDS consumption and hence three dummies are considered. If the households obtained higher level of benefit from PDS, then they are more likely to be nutrition secure.

Mean, standard deviation (SD) and the notations used for the dependent and independent variables are listed in Table 11. The multinomial logit (mlogit) model is specified as follows:

\begin{matrix} Y_{i t} = β_{1} + β_{2} A V E D_{i t} + β_{3} S T_{i t} + β_{4} S C_{i t} + β_{5} O B C_{i t} + β_{6} H H S Z_{i t} + β_{7} F H S_{i} t + β_{8} A G E H_{i t} \\ + β_{9} R E_{i t} + β_{10} M P F C E_{i t} + β_{11} P C L A N D_{i t} + β_{12} T P D S_{i t} + α_{13} T D_{i t} + ε_{i t} \end{matrix}

where i is the number of households: 124,644 in 2004–2005 and 101,662 in 2011–2012 and t is the number of years (2004–2005 and 2011–2012).

Table 11.

Notation, Specification, Mean and SD of the Variables Used in the Multinominal Logit Model (2004–2005 and 2011–2012 combined)

Notation	Specification	Mean	SD
Dependent Variables
Y	Status of nutrition in relation to the status of poverty of the households. It takes 0 for ‘non-poor_nutri secure’, 1 for ‘non-poor_nutri insecure’, 2 for ‘poor_nutri secure’ and 3 for ‘poor_nutri insecure’
Independent Variables
Cultural Factors
AVED	Average years of education of the households	5.3	3.7
Social Factors
ST	Whether the household belongs to STs (yes = 1, no = 0)	0.1	0.3
SC	Whether the household belongs to SCs (yes = 1, no = 0)	0.2	0.4
OBC	Whether the household belongs to OBCs (yes = 1, no = 0)	0.6	0.5
Demographic Factors
HHSZ	Household’s size (i.e., number of family members)	4.7	2.4
FHS	Whether the head the family is female (yes = 1, no = 0)	0.1	0.3
AGEH	Age of the head of the household	46.1	13.5
Economic Factors
RE	Whether household has at least one regular employee (yes = 1, no = 0)	0.3	0.5
MPFCE	Monthly per capita food consumption expenditure (in ₹)	638	508
PLAND	Per capita cultivable land of the household (in decimal)	0.5	2
PDS_Low	Is monthly per capita PDS benefit within 5–10 kg (yes = 1, no = 0)	0.12	0.32
PDS_Medium	Is monthly per capita PDS benefit within 10–15 kg (yes = 1, no = 0)	0.03	0.17
PDS_High	Is monthly per capita PDS benefit above15 kg (yes = 1, no = 0)	0.01	0.08
Other Variables
TD	Time dummy (2004–2005 = 0, 2011–2012 = 1)	2.4	0.5

Source: Authors’ estimation from NSSO unit level data of CES of 61st and 68th rounds.

Table 12.

Results of Multinomial Logit Model for the Status of Nutrition in Relation to the Status of Poverty of the Households

Number of observations = 226,306
LR χ²(42) = 108884.88
Prob > χ² = 0.0000
Pseudo R² = 0.2091
Log likelihood = −205920.86
Base outcome = 0 (non-poor_nutri secure)
	1 = Non-poor_nutri insecure			2 = Poor_nutri secure			3 = Poor_nutri insecure
	Coef.	Z-stat	p > \|z\|	Coef.	Z-stat	P > \|z\|	Coef.	Z-stat	p > \|z\|
AVED	−0.052	−30.5	0.00	−0.259	−48.6	0.00	−0.305	−108.6	0.00
ST	0.289	16.0	0.00	0.713	16.5	0.00	0.690	29.2	0.00
SC	0.045	2.6	0.01	0.599	15.0	0.00	0.549	25.4	0.00
OBC	0.097	7.6	0.00	0.441	12.4	0.00	0.370	20.6	0.00
HHSZ	0.218	77.2	0.00	0.278	44.4	0.00	0.421	121.6	0.00
FHS	0.106	6.0	0.00	0.192	4.9	0.00	0.334	14.9	0.00
AGEH	−0.010	−22.9	0.00	−0.011	−11.6	0.00	−0.018	−32.7	0.00
MPFCE	−0.002	−88.8	0.00	−0.004	−55.1	0.00	−0.003	−86.9	0.00
PDS_Low	−0.050	−2.9	0.00	0.509	15.6	0.00	0.388	18.5	0.00
PDS_Medium	−0.661	−19.1	0.00	0.549	10.2	0.00	−0.379	−8.8	0.00
PDS_High	−3.130	−20.1	0.00	0.493	5.6	0.00	−2.569	−17.2	0.00
RE	0.071	5.7	0.00	−0.528	−13.7	0.00	−0.429	−22.8	0.00
PCLAND	−0.145	−36.5	0.00	−0.441	−23.2	0.00	−0.489	−59.6	0.00
TD	−0.564	−43.2	0.00	1.060	34.6	0.00	−0.915	−52.6	0.00
Constant	0.845	31.8	0.00	−0.908	−13.7	0.00	1.081	31.2	0.00

Source: Authors’ estimation from NSSO unit level data of CES of 61st and 68th rounds.

Note: Z is estimated on the basis of robust standard error.

In the estimated results of mlogit (as given in Table 12) the positive coefficient means that as the value of the regressor increases, we are more likely to choose alternative outcomes than the base outcome and vice-versa for negative coefficient. The interpretation of the coefficient of regressors for three alternative outcomes is given below.

Non-poor_nutri Insecure Relative to Non-poor_nutri Secure

For non-poor, the INI decreased over time (as time dummy [TD] is statistically significant). Non-poor households with higher AVED, MPFCE, age of head of the households (AGEH) and PCLAND are more likely to be nutritionally secure relative to nutritionally insecure. If AVED increases by 1 year, the multinomial log odds for non-poor_nutri insecure relative to non-poor_nutri secure is expected to decrease by 0.052, that is, non-poor households with higher education are more likely to be nutritionally secure. Whereas if MPFCE increases by ₹1, non-poor_nutri insecure relative to non-poor_nutri secure households declined by 0.002. The coefficients of PDS benefits signify that the households consuming more PDS benefits are likely to be non-poor_nutri secure relative to non-poor_nutri insecure. If PDS_High increases by one unit, the multinomial log odds for non-poor_nutri insecure relative to non-poor_nutri secure is expected to decrease by 3.13. The log odds are relatively low for PDS_Medium and PDS_Low. Moreover, non-poor households having RE are not assured of being nutritionally secure as the coefficient is negative. Whereas, non-poor households with larger family size (HHSZ), ST, SC, OBC and FHS are likely to be nutritionally insecure.

Poor_nutri Secure Relative to Non-poor_nutri Secure

Households with higher AVED, MPFCE, AGEH and PCLAND are less likely to be poor_nutri secure relative to non-poor_nutri secure. Households with RE are also less likely to be poor_nutri secure relative to non-poor_nutri secure. One year increase of AVED, the multinomial log odds for poor_nutri insecure relative to non-poor_nutri secure is expected to decrease by 0.259, that is, the households with higher level of education are more likely to be non-poor_nutri secure. Whereas if MPFCE increases be one rupee, poor_nutri secure households relative to non-poor_nutri secure households is expected to decrease by 0.004. HHSZ is likely to be poor_nutri secure than non-poor_nutri secure. SC, ST, OBC and FHS are more likely to be poor_nutri secure relative to non-poor_nutri secure. Households with more PDS benefits are likely to be poor_nutri secure relative to non-poor_nutri secure. This relationship of PDS with the status of the households is not unusual. PDS helps the households to acquire food grains with subsidised price which lowers the value of MPFCE. A portion of PDS benefited households are generally nutrition secure as they get the opportunity of consuming PDS food grains at the same time they are also poor because their MPFCE lies below the poverty line.

Poor_nutri Insecure Relative to Non-poor_nutri Secure

Households with higher AVED, MPFCE, AGEH and PCLAND are less likely to be poor_nutri insecure relative to non-poor_nutri secure. Over time, households are more likely to be non-poor_nutri secure than poor_nutri insecure (as TD is statistically significant). Households having RE are less likely to be poor_nutri insecure relative to non-poor_nutri secure. With 1 year increase of AVED, the multinomial log odds for poor_nutri insecure relative to non-poor_nutri secure is expected to decrease by 0.305, that is, households with higher education are more likely to be non-poor_nutri secure and vice-versa. With the increase in MPFCE by ₹1, poor_nutri insecure relative to non-poor_nutri secure household’s status declines by 0.003. Higher level of PDS benefits elevates the status of the household from poor_nutri insecure to non-poor_nutri secure. The multinomial log odds for poor_nutri insecure relative to non-poor_nutri secure is expected to decrease by 0.149 for PDS_High and 0.043 for PDS_Medium. Low benefits from PDS (PDS_Low) has no significant impact in reducing nutrition insecurity of the poor households. At the same time, HHSZ is likely to be poor_nutri insecure relative to non-poor_nutri secure. SC, ST, OBC and FHS are also likely to be poor_nutri insecure relative to non-poor_nutri secure.

Conclusion

Downward trend of per capita consumption of calories was observed in rural India up to 2009–2010 and revived thereafter in 2011–2012 but similar trend was not followed in urban India. Along with the reduction in the incidence, depth and severity of poverty it has also experienced reduction in the percentage share of nutrition insecure households. Even though the rural and urban gap narrowed down during 2004–2005 to 2011–2012, still INI and DNI were higher in rural areas. On the other hand, SNI which reflects the inequality of calorie consumption of nutritionally insecure people was higher in urban areas.

Although poverty as well as nutrition insecurity has declined overtime, still a significant portion of non-poor households was found to be nutritionally insecure. It implies that they had purchasing power, yet they either spent more on non-food items or were unaware to select appropriate food items that contain requisite calories. These non-poor nutrition insecure households were comparatively more in urban areas than in its rural counterpart. Moreover, for the non-poor households, the growth rate of MPFCE was higher in the urban areas while that for poor it was higher in the rural areas. It indicates that the urban non-poor households spent more on food items, but due to diversification of consumption pattern (in favour of protein and fat items) they experience lower value of calorie intake resulting in higher nutrition insecurity. Higher percentage share of non-poor as well as nutrition insecure people belongs to OBCs and General caste.

Higher average years of education, monthly per capita food consumption expenditure, AGEH and per capita cultivable land have favourable impact on the nutrition status of the households. Households having larger family size and FHS are likely to be nutrition insecure. Households having RE can overcome the incidence of poverty but fail to ensure nutrition security. Even though, low level of PDS benefits has no significant impact in reducing nutrition insecurity of the poor households but higher level of PDS benefits enhance the status of both poor and non-poor households from nutrition insecurity to nutrition security. Thus, substantial increase in PDS benefits has the potential to overcome the nutrition insecurity to a great extent.

Based on the above findings the present study emphasises creating awareness among the people regarding the selection of the appropriate food basket in order to achieve nutrition security. With the increase in the education level of the households, the possibility of reduction of nutrition insecurity is higher. Hence, public policy initiatives in this regard must be taken to enhance the education level of the people. Apart from that, higher PDS benefits leads to poverty reduction and nutrition security. As a result policies regarding proper implementation as well as expansion of PDS must be taken into consideration. Increase in MPFCE does not ensure nutrition security but increase in PDS benefits in kind can overcome the nutrition insecurity. Therefore, the study strongly recommends the continuation of PDS benefits in kind to achieve the SDGs in respect of nutrition security.

Footnotes

Acknowledgements

The earlier versions of the article were presented at various seminars: collaborated seminar by ISI-Kolkata, Ramakrishna Mission and The Heritage College-Kolkata at Central University of Punjab, Rabindra Bharati University and Vidyasagar University. Authors are thankful for the suggestions made by eminent scholars and anonymous referees of this journal. However, normal disclaimers apply.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

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Are the Non-poor Households Nutritionally Secure? An Assessment from NSSO Unit Level Data in India Between 2004–2005 and 2011–2012

Abstract

Keywords

Introduction

Literature Review

Database and Methodology

Estimation of Poverty

Estimation of Nutrition Insecurity

Multinomial Logit Model to Analyse the Status of the Households

Status of Poverty and Nutrition Insecurity in India

Food Consumption Expenditure by the Status of Poverty and Nutrition Insecurity

Poverty and Nutrition Insecurity in Relation to Public Distribution System Benefit

Status of Nutrition among Poor and Non-poor Households in India

Econometric analysis of the Status of Nutrition at the Household Level in India

Non-poor_nutri Insecure Relative to Non-poor_nutri Secure

Poor_nutri Secure Relative to Non-poor_nutri Secure

Poor_nutri Insecure Relative to Non-poor_nutri Secure

Conclusion

Footnotes

Acknowledgements

Declaration of Conflicting Interests

Funding

References