Dietary Diversity Under Public Distribution System: An Evidence From Tamil Nadu, India

The distribution of real income gain due to price subsidy was more confined with stable food of rice, millets, pulses and vegetables with respect to poor people, whereas mid[r]

DIETARY DIVERSITY UNDER PUBLIC DISTRIBUTION SYSTEM: AN EVIDENCE FROM TAMIL NADU, INDIA Umanath Malaiarasan1$ & R Paramasivam# $ Madras Institute of Development Studies, Chennai-020, Tamil Nadu # Kumara Guru Institute for Agriculture, Bhavani, Tamil Nadu Abstract Tamil Nadu is an innovative state in India for all kind of food security programs It has implemented variety of programs from mid-day meal schemes to universal free rice for all people The main aim of these programs are to reduce the state of food and nutritional insecurity among the households, irrespective of income status Since only limited studies addressed this problem in India with mixed results and no study was there for Tamil Nadu, the present study addressed the impact of rice supply at subsidised price through Public Distribution System on the dietary diversity and subsequent nutritional gain in Tamil Nadu as food intake and composition directly affect the malnutrition problems of households We used propensity score matching technique to estimate the real impact of the subsidy program on the food consumption pattern and nutrient intake with the help of survey data collected by National Sample Survey Organisation for the years 2004-05 and 2011-12 Estimated results revealed that price subsidy on rice significantly and positively affected the food consumption pattern and nutrient intake of all households in the state However, degree of impact of price subsidy on food and nutrient consumption is subject to vary with respect to type of income categories It is found that the distribution of gain due to price subsidy among poor people was narrow towards the staple food items of rice, millets, pulses and vegetable, whereas, for the middle income households, it was more diversified not only towards high value commodities such as fruits, processed food and livestock products but also higher gain of fat and calcium Hence, extending the price subsidy for nutritional rich food items, other than rice, is expected help the poor people for diversifying their diet towards healthy and nutrient dense foods Corresponding author: umanath@mids.ac.in Keywords: Food Security, Nutrient Intake, Dietary Diversity, Propensity Score Matching, Introduction Undernourishment and malnutrition have been the major problems affecting growth and development of many of developing countries over a period In fact, alleviating these problems is one of the Sustainable Development Goals (SDGs) of the United Nations Over the last four decades, the world has not witnessed the expected reduction in the absolute number of chronically hungry people In India, the burden of under nutrition, micro-nutrient deficiency and over-nutrition remains high (Misra, 2011), specifically children and women are the most vulnerable people who are frequently affected by these malnutrition problems (Kehoc et al., 2014; Aurino, 2016) For instances, around 15% of the people suffer from undernourishment and 39 and 20 per cent of the children under age remain stunted and wasted, respectively Children from low income group and rural areas are heavily affected than high income and urban areas Over-nutrition problems are mostly associated with high income and urbanization Moreover, there is decreased level of under-nutrition problem among adolescent, whereas overweight problem has increased significantly from 1.8 per cent in 1999 to 7.5 per cent in 2015 for boys and from 1.9 per cent to 6.1 per cent for girls during the same period Similarly, the problem of obesity, diabetes and over-nutrition shows increasing trend for all categories of people (FAO, 2017) While significant progress has been made over the last 50 years in improving food production to achieve sufficiency, most rural populations and communities continue to face uncertainty in food and nutrition security In aggregate, over one-fifth of India’s population still suffers from chronic hunger and India ranks 100th among 119 countries in the 2017 Global Hunger Index (von Grebmer et al., 2017) Malnutrition problems are associated with amount of food intake and type of food in the diet Literature emphasize the diversity in food consumption pattern so as to avoid the malnutrition problems of both over nutrition and under nutrition (Taren, & Chen, 1993; Hatloy et al., 2000; Arimond & Ruel, 2002; Ekesa et al., 2008) Some literature identified that lack of dietary diversity is the major reason for undernutrition (Chandrasekhar et al., 2017) Inadequately diversified diet in terms of amount and composition in the food basket is associated with the less optimal growth, development and long-term health outcome (Charmarbagwala et al., 2004) Food diversity including milk products, vegetables, fruits and pulses is important for a balanced diet and micro-nutrient intake There is less likelihood of being underweight or stunted for the children who received diets that consisted of at-least four food groups (Desai and Vanneman, 2015) However, people in countries like India are so vulnerable to access adequate and healthy food due to many reasons and this vulnerability to command necessary food has created great space between food and people This space can be filled by ensuring food availability, accessibility and affordability Science and technological developments have ensured food availability manifold over fifty years in many developing countries including India Also, 21st centuries’ major policy breakthrough such as liberalisation and globalization and associated improvements in transportation and storage facilities, rise of supermarkets have made easy accessibility of all kinds of food products from any parts of the world After developing a road map for availability and accessibility of food, affordability is a major constraint among the people especially for the poor whose income per capita is unable to buy a right quantity at right time It has become a challenge of malnutrition problems and affect SDGs in India - resulted in heavy burdens on health and development Subsidies on food in any form is a remarkable approach to alleviate the poverty, food and nutritional insecurity in many developing countries In particular, food subsidy programmes in India and Tamil Nadu have significantly reduced the hunger and malnutrition among the poor people According to economic theory, such a subsidy programmes not only help to access stable food for daily energy requirements but also increase the purchasing power of vulnerable people which can further help to allocate total consumption expenditure on nutritional food stuffs and other social development activities in household Therefore, quantifying the real gain in terms of both food security and nutrient intake of such food subsidy programmes is important to explore the efficiency of institutional framework associated with food distribution Public Distribution System (PDS) is one of subsidised and powerful policies to alleviate the hunger in India, specifically, Tamil Nadu is the pioneer for adopting universal PDS (supplying free rice at notified quantities to various categories of people) and also for all the food security programmes from mid-day meal scheme to free rice distribution through PDS Tamil Nadu state has implemented supplying the rice at the cost of Rs.2.00 per kg to the below poverty people in 2004 and extended the rice supply at free of cost in 2011 Many of the literature have discussed about the food security, hunger, diet and undernourishment and associated impacts on the health of women and children across India (Ali et al., 2013; Chandrasekhar et al., 2017) Pingali et al., (2017) stated that PDS has helped to rectify the problem of hunger but nutritional aspects are unclear These necessitate that quantifying the impact of food subsidy to make viable food policy measures In that respect, very few studies only in India have discussed the impact of food subsidies on income allocation on different food commodities, dietary diversity and nutrient intake empirically and exposed different mixed results (Kaushal and Muchomba, 2015; Kochar, 2005; Rahman, 2016; Prappurathu, et al., 2015) Few recent studies have found that expanded coverage of PDS not only increased calorie intake, but also improved dietary diversity through income effects (Kaul, 2018; Kishore and Chakrabarti, 2015; Krishnamurthy et al., 2017; Rahman, 2016) PDS may also cause for the substitution from more nutritionally superior coarse cereals and millets to subsidized wheat through PDS (Khera, 2014) Conversely, Desai and Vanneman (2015) stated that PDS system have no effect on the consumption of micronutrient rich foods Hitherto, Tamil Nadu as a pioneer state for all the food security programme in India, there is no study to reveal the impact of these programme in the state To fill this gap, the present study intended to explore the impact of price subsidy on rice and its supply through PDS system on food consumption pattern, extent of dietary diversity and nutrients intake in Tamil Nadu Methodology and Data 2.1 Propensity Score Matching technique The propensity score matching approach was used to examine the impact of price subsidy on food consumption pattern and nutrient intake in Tamil Nadu The method compares the welfare of households availing PDS-rice price subsidy (treatment group) with their counterfactual group who are not consuming PDS-rice (control group) The propensity score is defined P(Ti) as the conditional probability of receiving treatment and given pretreatment characteristics P(TI )  prob( DI  1/ TI )  E ( D / TI )  F (TI ) where TI denotes a vector of pre-treatment characteristics of household i: E is the expectation operator; and F(TI) represents normal or logistic cumulative distribution frequency The propensity score are predicted with logit model The assumption of the conditional independence of the score result extends the use of the propensity scores for the computation of the conditional treatment effect The predicted propensity scores are used to estimate the treatment effect According to Becker and Ichino (2002), average treatment effect on the treated (ATT) is the parameter of interest in propensity score matching analysis Thus, we used ATT to assess the effect of price subsidy program on food consumption pattern and nutrient intake of households ATT is computed by matching PDS-rice beneficiaries and non-beneficiary households that are closest in terms of their propensity scores In this study, the treated groups are referred to as PDS-rice consumers and the ATT is calculated as follows: ATT  E(T/ I  1)  E(Y /1) / D  1)  E(Y (0) / D  where E (Y /1) / D  represents the expected outcome of PDS-rice consumption and E (Y (0) / D denotes the counterfactual outcome of non-beneficiaries of PDS-rice The counterfactual estimates represent what the welfare outcome of PDS beneficiaries would be, if they have not enjoying the PDS-rice price subsidy Conceptually, estimating the treatment effect in a quasi-experimental situation is relatively simple and involves predicting participation in a treatment by using a set of covariates, and then matching two respondents with similar propensity scores, including one from the treatment group and another from the control group However, the results tend to be sensitive to the quality of matching In order to maximise the quality of the match, we have used the nearest neighbour matching within calipers A number of matching techniques have been suggested in the literature to match treated and control groups of similar propensity scores to compute the ATT (Shehu and Sidique, 2014; Chirwa, 2010; Wordofa and Sassi, 2017) 2.2 Data Consumer expenditure survey on food and non-food commodities pertaining to the years 2004-05 and 2011-12 were used for the present study to capture both spatial and temporal variation The survey was conducted extensively over one-lakh sample households across India in each period Of which, Tamil Nadu accounts over 7000 sample household in each period In this paper, price response of demand is obtained on the basis of unit values We used state-wise poverty line to classify the entire sample size as low, middle and high income classes For this, poverty estimates, released by the Planning Commission, Government of India for 2004-05 and 2011-12 were used for each individual state Accordingly, the ‘low’ income class comprised households who have income level below the poverty line (BPL), between BPL and up to 150 per cent of BPL was grouped as ‘middle income’ and households having per capita income above 150 per cent of BPL were categorized as ‘high income’ group Results and Discussion 3.1 Food consumption pattern and nutrient intake in Tamil Nadu Food consumption pattern and nutrients intake across income groups, PDS rice consumer and non-PDS consumer are given in Table In general, PDS-rice beneficiaries consumed more amount of rice across income groups It is observed that PDS-rice beneficiaries among high and medium income people consumed more amount of rice, compared to low income category, whereas non-PDS beneficiaries of the high income category consumed rice lesser than the other categories Low income people consumed less amount of processed food items derived from rice and wheat, compared to high income people, while non-PDS beneficiaries of all the income categories registered higher amount consumption of these processed food items The PDS-rice beneficiaries consumed rice, processed foods, millets, roots and tubers, vegetables, meats, eggs, fish, pulses and oils more than the non-PDS beneficiaries, irrespective of income categories This indicating that PDSrice beneficiaries are able to allocate their disposable income to all kind of food groups Mixed consumption pattern can be observed for wheats, fruits and nuts among all the categories of people Regarding nutrient intake, generally the intake of all the nutrients was low for less income households, compared to middle and high income households Intake of energy, protein, iron and Vitamin A was more among the PDS rice consumers, irrespective of income groups Intake of fat and calcium was more among the PDS rice consumers in low income people, whereas it was less for middle and high income people 3.2 Determinants of PDS-rice consumption Income group wise estimated logit model and marginal effect for determinants of PDS-rice consumption are presented in Table Results revealed that increased market price of rice increased the probability of consuming rice at subsidised price provided by fair price shops functioning under Public Distribution System (PDS) among low income households, whereas there was an inverse relationship for middle and high income people For instance, if the price of rice in open market increases by Rs.10 per kilogram, the probability of consuming PDS rice would increase by 6.7 per cent, indicating that increased price for rice in open market forces the poor people to rely on the PDS for their rice consumption i.e., open market rice and PDS rice were treated as substitute among poor people But it is not true for the middle and high income people, where both PDS and open market rice were treated as complement There is no significant impact on the PDS rice consumption among poor people with respect to income changes Nevertheless, increased income has significant and negative impact on the probability of consuming PDS rice among middle and high income people Increased income level by a unit would result in 50 and 44 per cent less likelihood of consuming PDS rice for middle and high income people, respectively There was a higher probability of consuming PDS rice while the household size is larger and if the household are availing LPG facilities In contrast, if the households are living in urban and possess higher level of education, there is a less likelihood of PDS rice consumption, irrespective of income groups Also, results revealed that there is positive tendency to consume PDS rice in 2011-12 over 2004-05 3.3 Average effect of PDS rice consumption on the food consumption pattern and nutrient intake in Tamil Nadu PDS rice at subsidised price is one of the method to reduce the hunger and malnutrition problem among in India as well as Tamil Nadu Since changes in consumption pattern and nutrient intake is affected by many factors such as income, price, household characters, we cannot infer that changes in consumption pattern is due to only PDS rice consumption i.e., there exists a sample selection bias in the aspects of dietary diversity and nutrient intake among PDS-rice consumers Hence, propensity score matching technique was applied to assess the real effect of the PDS rice consumption on dietary diversity and nutrient intake, we refer to this as the Average Treatment Effect (ATT)—we match treated cases, i.e., PDS beneficiaries, to counterfactuals with similar propensity scores In a series of bivariate logit models, we estimate the balancing scores for each pairwise comparison of the PDS beneficiaries As we are primarily interested in the response to the PDS rice consumption, we predict the marginal probability of opting for PDS rice consumption The treatment effect for the treated and robustness of the results were estimated by using nearest neighbour matching technique Before estimating the impact of PDS rice consumption, we tested the quality of matching process After estimating the propensity score for the PDS beneficiaries and nonPDS beneficiaries, we checked common support condition Figure show graph of propensity score distribution for poor people, comparing PDS beneficiaries and Non-PDS beneficiaries and whether a treated observation is on or off the common support The top part of the graph represents p-score for PDS beneficiaries, whereas the bottom half contains that of the Non-PDS beneficiaries The overlap in the distribution of propensity score of both household groups satisfied the common support condition In contrast, Figure for middle income and Figure for high income group illustrate imbalanced distribution between treatment and control households and the cases excluded from the analysis to avoid bad matches It also suggests the need for appropriate matching procedure so as to tackle the underlying bias Covariate balancing test was conducted to ensure that difference in covariate used to estimate the propensity scores between PDS beneficiaries and non-beneficiaries are eliminated This test also helps to ascertain that PDS beneficiaries and non-beneficiaries in the matched sample not differ in terms of observable characteristics, except in PDS rice consumption status Rosenbaum and Rubin (1985) proposed the mean absolute standardized bias (MASB) for covariate balancing The MASB was estimated for each variable before and after matching, and then the average for all variables was calculated When valid, the MASB between treated and non-treated after matching should not be more than 20% If the standardized difference is above 20% after matching, this implies that the matching process has failed and consequently leads to bad matches It is expected that there are significant differences between adoption groups before matching, but these differences are eliminated after matching The respective covariate balancing test results for each individual covariate for the matched and unmatched samples for poor, middle and high income people are presented in Figures from to All the figures indicate that absolute bias is reduced for all variables in the matched samples of all type of income groups Indeed, the percent standardized bias for all the farm groups are below 20 for each individual variable in the matched sample, indicating success of the matching process 3.4 Estimation of Average Effect of PDS rice consumption on food consumption pattern and nutrient intake After estimating the propensity scores and checking their matching quality, we estimated ATT by using nearest neighbourhood algorithm Table reports the average effect of subsidized price for rice through PDS on the food consumption pattern including rice, processed food items, millets, pulses, root and tubers, vegetables, fruits, meats, egg, fish, milk products, oil items and nuts; and intakes of nutrients such as energy, protein, fat, Ca, Fe and vitamin ‘A’ Results reveal that, in over all, the consumption of food items such as pulse, roots and tubers, vegetables, meat, egg, fish and oil has increased among PDS-rice beneficiaries over non-beneficiaries For instance, after matching, the difference between PDS beneficiaries and non-PDS beneficiaries with respect to pulses consumption was 124 grams per month Contrastingly, there was negative gain for pulse consumption without matching Similarly, after matching, the consumption of root and tubers, vegetables, meat, egg, fish and oil has increased by 68, 506, 67, 22, 45 and 66 grams, respectively, if the households are beneficiaries of PDS-rice subsidy scheme The results also suggested that subsidy on rice has increased the real disposable income of the households of PDS-beneficiaries and modified the food consumption pattern towards more consumption of rice as well as number of other food commodities significantly Specifically, considerable increment in the consumption of high value commodities such as vegetable, meat, egg, fish and oils seems to be virtuous for further nutritional enrichment in the dietary pattern under subsidised PDS-rice system These results also confirm the existence of both substitution and income effect due to the policy intervention of price subsidy on PDS-rice as the quantity consumption of rice and other food commodities has increased Regarding nutritional gain, intake of energy, protein, fat, iron and vitamin ‘A’ has increased significantly among PDS-rice beneficiaries over non-beneficiaries For instances, after matching, energy intake has increased by 285.54 kilo calories if the households are PDS-rice consumer It is not surprise that there exists an income effect on the quantity consumption of stable food of rice, for which the price was subsidized, the energy calorie intake has not declined across income groups Similarly, the intake of protein and fat has increased by 6.92 and 2.21 grams, respectively, and intake of iron increased by 37.37 micro gram and vitamin ‘A’ by 18.42 microgram among PDS rice beneficiaries over nonbeneficiaries This clearly indicated that food subsidy on stable food of rice resulted in more gain of nutrient intake It can also be observed that changes in the food consumption pattern after price subsidy intervention is not same for all the income groups The average effect was significant for most of the commodities for PDS-rice beneficiaries of middle income people, whereas the average effect was found significant only for few commodities in case of poor and high income people This indicates that middle income people has become more diversified in the diet due to price subsidy on PDS-rice Specifically, middle income households has a significant effect due to PDS-rice price subsidy on the quantity consumption of processed food items, fruits and milk products, compared to the other households In case of poor people, there is no significant effect on the quantity consumption of processed foods, fruits, meats, fish, milk products and nuts It is interesting to note that the significant effect was observed on the quantity consumption of millets for only poor people, compared to the other income households Quantity consumption of roots and tubers, vegetables, pulses and oils has increased after the intervention of the price subsidy, irrespective of income groups In the aspects of nutrients intake, gain in the intake of energy, protein, iron and vitamin ‘A’ can be seen irrespective of income groups Since there is an increment in the quantity consumption of pulses and vegetables substantially, all income groups showed significant improvement in the protein intake However, it is noticed that price subsidy on PDS-rice has significant positive effect on the quantity intake of fat for middle and high income households, whereas the fat gain was insignificant for poor people, indicating clearly that subsidised households among middle and high income categories seek to add taste to their diet simultaneously All these indicate that middle income households are in progress towards the dietary diversity and consuming high value commodities of fruits, vegetables, milk, meat, fish and processed food items if the middle income households avail PDS-rice at subsidised price Conclusion We empirically proved that price subsidy intervention on rice through Public Distribution System influenced the food consumption pattern and nutrient intake of households in Tamil Nadu The real income gain due to subsidy among Tamil Nadu households was reallocated to demand more amount of rice as well as other food commodities However, price subsidy on the stable food of rice has different effect on the consumption pattern for poor, middle and higher income people The distribution of real income gain due to price subsidy was more confined with stable food of rice, millets, pulses and vegetables with respect to poor people, whereas middle income people distributed the gain of less price of rice towards all kind of sort food groups, specifically high value commodities of fruits, milk, meats, fish and processed food items This indicates that PDS beneficiaries of poor people were stimulated towards the items of cheaper one rather than go for high value commodities like fruits, milk, meats and fish Results of the study also revealed that this kind of different food consumption pattern has further affected the level of nutrients intake, where there is no significant improvement in the intake of fat and calcium among the poor people, compared to the middle income people Since rice is treated as normal good, the substitution effect and income effect led not only for more consumption rice but also more energy, irrespective of income groups Since subsidy on rice caused for switching away from high value commodities of milk, meat, fish and fruits and thereby reducing the dietary diversity for poor people, extending the price subsidy for nutritional rich food items, other than rice, is expected help the poor people for diversifying their diet towards healthy and nutrient dense foods Reference Ali, D., Saha, K K., Nguyen, P H., Diressie, M T., Ruel, M T., Menon, P., & Rawat, R (2013) Household food insecurity is associated with higher child undernutrition in Bangladesh, Ethiopia, and Vietnam, but the effect is not mediated by child dietary diversity The Journal of nutrition, 143(12), 2015-2021 Arimond, M., & Ruel, M T (2002) PROGRESS IN DEVELOPING AN INFANT AND A CHILD FEEDING INDEX: AN EXAMPLE USING THE ETHIOPIA DEMOGRAPHIC AND HEALTH SURVEY 2000 (No 583-2016-39595) Aurino, E (2016) Do boys eat better than girls in India? Longitudinal evidence from young lives Becker, S O., & Ichino, A (2002) Estimation of average treatment effects based on propensity scores The stata journal, 2(4), 358-377 Chandrasekhar, S., Aguayo, V M., Krishna, V., & Nair, R (2017) Household food insecurity and children's dietary diversity and nutrition in India Evidence from the comprehensive nutrition survey in Maharashtra Maternal & child nutrition, 13, e12447 Charmarbagwala, R., Ranger, M., Waddington, H., & White, H (2004) The determinants of child health and nutrition: a meta-analysis Washington, DC: World Bank Chirwa, T G (2010) Program evaluation of agricultural input subsidies in Malawi using treatment effects: Methods and practicability based on propensity scores Desai, S., & Vanneman, R (2015) India human development survey-ii (ihds-ii), 2011-12 icpsr36151-v2 Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 31 Desai, S., & Vanneman, R (2015, August) Enhancing Nutrition Security via India's National Food Security Act: Using an Axe instead of a Scalpel? In India Policy Forum:[papers] India Policy Forum Conference (Vol 11, p 67) NIH Public Access Ekesa, B N., Walingo, M K., & Abukutsa-Onyango, M O (2008) Influence of agricultural biodiversity on dietary diversity of preschool children in Matungu division, Western Kenya African journal of food, agriculture, nutrition and development, 8(4), 390-404 FAo, I F A D., & UNICEF (2017) WFP, WHO (2017) The state of food security and nutrition in the world 2017 Building resilience for peace and food security FAO, Rome URL: http://www fao org/3/a-i7695e pdf (Accessed 16 May 2018) Hatløy, A., Hallund, J., Diarra, M M., & Oshaug, A (2000) Food variety, socioeconomic status and nutritional status in urban and rural areas in Koutiala (Mali) Public health nutrition, 3(1), 57-65 Kaul, T (2018) Household responses to food subsidies: evidence from India Economic Development and Cultural Change, 67(1), 95-129 Kaushal, N., & Muchomba, F M (2015) How consumer price subsidies affect nutrition World Development, 74, 25-42 Kehoe, S H., Krishnaveni, G V., Veena, S R., Guntupalli, A M., Margetts, B M., Fall, C H., & Robinson, S M (2014) Diet patterns are associated with demographic factors and nutritional status in S outh I ndian children Maternal & child nutrition, 10(1), 145-158 Khera, R (2014) Cash vs in-kind transfers: Indian data meets theory Food Policy, 46, 116128 Kishore, A., & Chakrabarti, S (2015) Is more inclusive more effective? The ‘New Style’public distribution system in India Food Policy, 55, 117-130 Kochar, A (2005) Can targeted food programs improve nutrition? An empirical analysis of India’s public distribution system Economic development and cultural change, 54(1), 203-235 Krishnamurthy, P., Pathania, V., & Tandon, S (2017) Food price subsidies and nutrition: evidence from state reforms to India’s public distribution system Economic Development and Cultural Change, 66(1), 55-90 Misra, A., Singhal, N., Sivakumar, B., Bhagat, N., Jaiswal, A., & Khurana, L (2011) Nutrition transition in India: Secular trends in dietary intake and their relationship to diet‐ related non‐ communicable diseases Journal of diabetes, 3(4), 278-292 Parappurathu, S., Kumar, A., Bantilan, M C S., & Joshi, P K (2015) Food consumption patterns and dietary diversity in eastern India: evidence from village level studies (VLS) Food Security, 7(5), 1031-1042 Pingali, P., Mittra, B., & Rahman, A (2017) The bumpy road from food to nutrition security–Slow evolution of India's food policy Global food security, 15, 77-84 Rahman, A (2016) Universal food security program and nutritional intake: Evidence from the hunger prone KBK districts in Odisha Food policy, 63, 73-86 Rahman, A (2016) Universal food security program and nutritional intake: Evidence from the hunger prone KBK districts in Odisha Food policy, 63, 73-86 Shehu, A., & Sidique, S F (2014) A propensity score matching analysis of the impact of participation in non-farm enterprise activities on household wellbeing in rural Nigeria UMK Procedia, 1, 26-32 Taren, D., & Chen, J (1993) A positive association between extended breast-feeding and nutritional status in rural Hubei Province, People's Republic of China The American journal of clinical nutrition, 58(6), 862-867 von Grebmer, K., Bernstein, J., Hossain, N., Brown, T., Prasai, N., Yohannes, Y., & Foley, C (2017) 2017 Global Hunger Index: the inequalities of hunger Intl Food Policy Res Inst Wordofa, M., & Sassi, M (2017) Impact of Farmers’ Training Centres on Household Income: Evidence from Propensity Score Matching in Eastern Ethiopia Social Sciences, 7(1), Tables Table 1: Income group wise estimated logit model and marginal effect for determinants of PDS-rice consumption - Poor people Middle income High income - Non-PDS PDS Non-PDS PDS Non-PDS PDS items in kg 0.316 0.297 0.730 0.696 1.447 1.158 Millets in kg 0.215 0.341 0.118 0.149 0.122 0.116 Roots & Tubers in kg 0.224 0.250 0.357 0.421 0.522 0.599 Vegetables in kg 2.059 2.291 3.118 3.563 4.492 5.240 Fruits in kg 0.322 0.296 0.779 0.894 2.302 2.050 Meats in kg 0.131 0.171 0.295 0.378 0.444 0.725 Egg in kg 0.054 0.068 0.143 0.163 0.221 0.316 Fish in kg 0.067 0.105 0.132 0.229 0.216 0.365 Pulses in kg 0.513 0.596 0.918 0.957 1.242 1.325 Milk products in kg 1.557 1.604 4.685 3.664 7.997 6.176 Oil & Fat foods in kg 0.325 0.377 0.550 0.575 0.784 0.864 Nuts in kg 0.005 0.004 0.021 0.033 0.088 0.084 Energy in kcal 1332.585 1453.825 1749.711 1850.119 2301.723 2363.445 Protein in g 31.493 34.747 44.440 47.358 59.897 63.277 Fat in mg 19.398 19.814 35.362 33.780 57.326 53.919 Calcium in mg 6260.531 7336.659 13219.890 12465.710 21425.150 19473.610 Iron in mg 173.497 201.014 258.190 281.629 375.002 396.402 Vitamin A in µg 69.824 77.605 105.975 117.880 169.996 179.201 Processed food Table 2: Income group wise estimated logit model and marginal effect for determinants of PDS-rice consumption Poor people Middle income Marginal Variables High income Marginal All income Marginal Marginal Logit Effect Logit Effect Logit Effect Logit Effect 0.656** 0.067** -0.982*** -0.136*** -1.482*** -0.237*** -1.088*** -0.153*** (0.262) (0.027) (0.159) (0.022) (0.206) (0.032) (0.112) (0.016) Ln Monthly Per-capita -0.081NS -0.008NS -3.615*** -0.501*** -2.787*** -0.445*** -2.163*** -0.305*** Consumer Expenditure (0.287) (0.029) (0.219) (0.029) (0.274) (0.042) (0.142) (0.019) 0.368NS 0.037NS 1.396*** 0.193*** 1.584*** 0.253*** 0.967*** 0.136*** (0.217) (0.022) (0.126) (0.017) (0.187) (0.029) (0.089) (0.012) 0.363*** 0.037*** 0.109*** 0.015*** 0.188*** 0.03*** 0.193*** 0.027*** (0.041) (0.004) (0.022) (0.003) (0.031) (0.005) (0.016) (0.002) 0.028*** 0.003*** 0.013*** 0.002*** 0.002NS 0NS 0.011*** 0.002*** (0.005) (0) (0.003) (0) (0.003) (0.001) (0.002) (0) 0.154NS 0.016NS 0.195** 0.026** -0.137NS -0.022NS 0.068NS 0.01NS (0.162) (0.016) (0.096) (0.013) (0.122) (0.019) (0.067) (0.009) - - - - - - - - -0.037NS -0.004NS 0.561NS 0.077NS 0.367NS 0.06NS 0.408NS 0.056NS (0.694) (0.07) (0.377) (0.056) (0.536) (0.088) (0.28) (0.041) -0.292NS -0.03NS 0.174NS 0.026NS -0.142NS -0.023NS 0.029NS 0.004NS (0.69) (0.07) (0.371) (0.056) (0.522) (0.086) (0.275) (0.04) -0.268NS -0.027NS -0.226NS -0.035NS -1.072** -0.17** -0.57NS -0.089NS Ln price of rice Ln non-food expenditure Household size Age of household head Gender of household head Social Groups (Scheduled Tribes ) Scheduled Caste Other Backward Class Other Class Owned land Regular salary earners Owning dwelling unit Education level (illiterate) Non-intuitional education Primary High school Higher secondary Collegiate Food away from home Home production of rice Presence of refrigerator (0.768) (0.078) (0.404) (0.062) (0.547) (0.089) (0.294) (0.044) 0.901*** 0.091*** 0.215** 0.03** 0.036NS 0.006NS 0.362*** 0.053*** (0.137) (0.014) (0.09) (0.013) (0.133) (0.021) (0.064) (0.01) 1.251NS 0.127NS 0.037NS 0.005NS 0.131NS 0.021NS 0.119NS 0.017NS (0.883) (0.09) (0.132) (0.018) (0.1) (0.016) (0.077) (0.011) 0.582NS 0.059NS 0.652*** 0.088*** 0.322** 0.053** 0.193** 0.027** (0.497) (0.05) (0.143) (0.019) (0.148) (0.025) (0.092) (0.013) - - - - - - - - -1.039NS -0.105NS 0.28NS 0.033NS -0.358NS -0.058NS -0.089NS -0.012NS (0.676) (0.068) (0.571) (0.064) (0.839) (0.139) (0.388) (0.052) -0.099NS -0.01NS -0.117NS -0.015NS -0.729*** -0.121*** -0.251*** -0.034*** (0.148) (0.015) (0.094) (0.012) (0.18) (0.029) (0.071) (0.009) -0.467*** -0.047*** -0.429*** -0.059*** -0.999*** -0.168*** -0.592*** -0.085*** (0.162) (0.016) (0.096) (0.013) (0.172) (0.028) (0.071) (0.01) -0.491NS -0.05NS -1.015*** -0.155*** -1.474*** -0.25*** -1.092*** -0.168*** (0.384) (0.039) (0.147) (0.024) (0.204) (0.033) (0.105) (0.017) -1.333*** -0.135*** -1.302*** -0.206*** -2.106*** -0.355*** -1.612*** -0.262*** (0.389) (0.039) (0.14) (0.024) (0.187) (0.03) (0.095) (0.016) -0.356NS -0.036NS 0.266*** 0.036*** 0.003NS 0.001NS 0.039NS 0.005NS (0.2) (0.02) (0.083) (0.011) (0.092) (0.015) (0.057) (0.008) -0.903NS -0.091NS -0.393** -0.058** -0.493NS -0.078NS -0.497*** -0.074*** (0.569) (0.058) (0.2) (0.031) (0.27) (0.042) (0.152) (0.024) 0 -0.476NS -0.071NS 0.172NS 0.027NS 0.027NS 0.004NS Presence of LPG Year 2011-12 Urbanisation Income class (poor) Middle income High income Constant (omitted) (omitted) (0.402) (0.064) (0.213) (0.034) (0.187) (0.026) 2.254** 0.228** 0.789*** 0.116*** 0.519*** 0.081*** 0.601*** 0.089*** (1.045) (0.106) (0.121) (0.018) (0.132) (0.02) (0.082) (0.012) 2.262** 0.229** 3.15*** 0.413*** 2.54*** 0.426*** 2.57*** 0.338*** (1.063) (0.108) (0.18) (0.019) (0.173) (0.023) (0.115) (0.013) -0.594*** -0.06*** -0.37*** -0.052*** -0.735*** -0.122*** -0.566*** -0.082*** (0.145) (0.015) (0.073) (0.011) (0.09) (0.015) (0.051) (0.007) - - - - - - - - -0.15NS -0.021NS (0.082) (0.011) -0.523*** -0.077*** (0.136) (0.02) - - - - -6.362*** (1.892) - - - - 17.81*** - (1.054) - - - - 14.074*** - (1.302) 11.448*** - (0.664) Note: *** indicates significant at one per cent level: ** indicates significant at per cent level: figures in the parentheses are standard errors Table 3: Average effect of subsidized price for rice through PDS on the food consumption pattern - LI Outcome variables MI HI OAI Unmatched Matched Unmatched Matched Unmatched Matched Unmatched Matched 334.075*** 236.692*** 90.305*** 291.812*** -16.19NS 272.66*** -186.673*** 285.545*** (19.428) (32.035) (12.564) (24.861) (25.219) (37.873) (11.922) (25.193) 7.943*** 5.257*** 2.977*** 7.683*** 2.783*** 7.46*** -4.633*** 6.919*** (0.448) (0.764) (0.315) (0.629) (0.733) (1.118) (0.335) (0.689) 2.909*** 0.696NS -1.748*** 2.072*** -3.486*** 3.339*** -11.285*** 2.208*** (0.406) (0.596) (0.349) (0.646) (0.719) (1.135) (0.355) (0.74) 1609.108*** 532.894NS -269.393NS 939.074*** -426.136NS 422.212NS -3712.371*** 602.209NS (196.751) (283.261) (160.28) (327.36) (353.57) (526.257) (158.287) (335.751) 56.028*** 33.624*** 33.853*** 45.186*** 48.237*** 38.881*** -9.105*** 37.37*** (3.373) (5.93) (2.782) (5.557) (6.471) (10.295) (2.683) (5.981) 20.536*** 10.349** 15.71*** 22.228*** 25.048*** 24.342*** -6.728*** 18.416*** Vitamin A in µg (3.191) (4.387) (2.336) (3.846) (4.352) (7.102) (1.886) (3.619) Processed food 0.018NS 0.021NS -0.046** 0.081** -0.157*** -0.155NS -0.315*** 0.008NS items in kg (0.024) (0.044) (0.019) (0.039) (0.036) (0.064) (0.015) (0.036) 0.152*** 0.108** 0.026NS -0.008NS 0.016NS -0.004NS 0.06*** 0.007NS (0.039) (0.044) (0.016) (0.037) (0.014) (0.023) (0.011) (0.017) 0.062*** 0.032** 0.075*** 0.071*** 0.155*** 0.078*** 0.02*** 0.068*** (0.011) (0.015) (0.009) (0.016) (0.016) (0.029) (0.007) (0.014) 0.56*** 0.326*** 0.583*** 0.571*** 0.992*** 0.566*** 0.07NS 0.506*** Vegetables in kg (0.056) (0.085) (0.047) (0.086) (0.089) (0.15) (0.039) (0.082) Fruits in kg -0.058*** 0.000NS 0.035NS 0.105** -0.088NS 0.111NS -0.532*** 0.019NS Energy in kcal Protein in g Fat in g Calcium in mg Iron in mg Millets in kg Roots & Tubers in kg Meats in kg Egg in kg Fish in kg Pulses in kg Milk products in kg Oil & Fat foods in kg Nuts in kg (0.021) (0.032) (0.025) (0.045) (0.061) (0.094) (0.025) (0.051) 0.035*** -0.014NS 0.084*** 0.085*** 0.305*** 0.182*** 0.026NS 0.067*** (0.009) (0.012) (0.011) (0.017) (0.04) (0.058) (0.014) (0.018) 0.016*** 0.014** 0.034*** 0.037*** 0.086*** 0.064*** -0.002NS 0.022*** (0.005) (0.007) (0.006) (0.009) (0.01) (0.017) (0.004) (0.008) 0.037*** 0.036NS 0.087*** 0.065*** 0.195*** 0.104** 0.052*** 0.045** (0.013) (0.019) (0.012) (0.018) (0.022) (0.042) (0.009) (0.018) 0.141*** 0.055*** 0.086*** 0.152*** 0.18*** 0.15*** -0.062*** 0.124*** (0.014) (0.022) (0.011) (0.021) (0.022) (0.033) (0.01) (0.02) 0.100NS -0.252NS -0.819*** -0.084** -1.279*** -0.409NS -2.315*** -0.197NS (0.089) (0.133) (0.077) (0.153) (0.157) (0.232) (0.07) (0.15) 0.092*** 0.032*** 0.046*** 0.053*** 0.11*** 0.105*** -0.038*** 0.066*** (0.009) (0.013) (0.007) (0.013) (0.015) (0.023) (0.006) (0.013) 0.000NS 0.000NS 0.008*** -0.002NS 0.015*** 0.006NS -0.014*** 0.002NS (0.001) (0.001) (0.003) (0.005) (0.006) (0.011) (0.002) (0.005) Note: *** indicates significant at one per cent level: ** indicates significant at per cent level: figures in the parentheses are standard errors Figures ... development and cultural change, 54(1), 203-235 Krishnamurthy, P., Pathania, V., & Tandon, S (2017) Food price subsidies and nutrition: evidence from state reforms to India? ??s public distribution. .. The ‘New Style? ?public distribution system in India Food Policy, 55, 117-130 Kochar, A (2005) Can targeted food programs improve nutrition? An empirical analysis of India? ??s public distribution system... For instances, around 15% of the people suffer from undernourishment and 39 and 20 per cent of the children under age remain stunted and wasted, respectively Children from low income group and rural