Enhancing nutrition security 
via India’s National Food 
Security act: Using an axe 
instead of a scalpel? 
Sonalde Desai & Reeve Vanneman
Repeated Cross-Sections, Longitudinal 
Panels and Cohort Studies 
• Description of changes over time 
• Useful snapshots when social/policy 
changes taking place 
Repeated 
Cross Section 
• Effects of early life experiences on later 
outcomes 
• Single cohort not suitable for studying 
social/policy changes 
• Attrition a major challenge 
Cohort 
Studies 
• Combination of repeated cross-section and 
cohort studies 
• Addition of new cohorts, within family analysis 
• Useful in changing context 
• Reinterviews major challenge, panels get old 
Panel Studies 
of households
India’s National Food Security Act, 
2013 
 Justification: High undernutrition and hunger 
 Solution: Two main pillars 
 Expansion of Public Distribution System (PDS) 
to cover 67% of the population for 5 kg of cereals 
at Rs. 1-3 per kg 
 Enhancement to Integrated Child Development 
Schemes (ICDS) to ensure children receive take 
home rations and hot cooked meals
Crucial Question: 
 Is NFSA the best strategy for 
addressing undernutrition? 
 Implicit assumptions: 
GDP growth by itself is not sufficient. 
Undernutrition is caused by hunger. 
 Therefore, solution is more calories.
Our empirical storyline 
1. Our best estimate of current undernutrition. 
 About 37% underweight 
2. PDS use? Results presented here 
 Increased cereal consumption, 
 Reduced milk & fruit consumption 
 No improvement in level of undernutrition 
3. ICDS access? Results not presented, available 
on request. 
 Somewhat reduced undernutrition 
 But poor outreach
India Human Development 
Surveys I and II 
 Largest nationwide panel survey in India 
 Full rounds 2004-5 & 2011-12 
 83% recontact rate after 7 years 
 90% rural recontact 
 72% urban recontact 
 Similar questionnaires as IHDS-I 
 Urban sample refreshed by addition of 2,134 
new households
IHDS-II (2011-12) Sample 
33 States and UT 
1474 villages and 
988 urban 
blocks 
42,152 
households 
215,754 
individuals 
39,264 ever 
married 
women 15- 
49 
12,148 
kids 
age 8- 
11 
12153 
Youth 
age 
15-18
For this analysis the sample is: 
1. 42,152 households for household based PDS 
analysis 
2. 10,715 children ages 0-60 months for analysis 
of underweight, limited to: 
 Children for whom both month and years of 
birth are available 
 Whose weight-for-age z scores range from -6 to 
+5 SD
Public Distribution System 
(PDS) 
 Given to people holding ration cards for 
purchase through fair price shops 
 Subsidies for people holding Below Poverty 
Line (BPL cards) 
 PDS purchase by BPL card holders grew from 
21% of the households in 2005 to 37% of the 
households in 2012 due to program expansion 
and increased efficiency in distribution 
 But still poorly targeted, allows for 
comparisons
Improvement in targeting & 
efficiency between 2005 and 2012 
Use of PDS 
by poor 
households 
also 
increased 
In 2004-5 
poor 
households 
using PDS 
formed 20% 
of the 
population 
but by 2012 
it was 37% 
60 
50 
40 
30 
20 
10 
0 
Distribution of Households with 
BPL/Antyodaya Card by Income Decile 
1 2 3 4 5 6 7 8 9 10 
Percent 
Income Decile 
IHDS-I (2004-5) 
IHDS-II (2011-12) 
Leakage 
40% excluded
Do we expect PDS to reduce 
malnutrition? From the literature.. 
 Yes: increased caloric intake using NSS: 
 Lower decline in caloric consumption for the PDS 
users over time Himanshu and Sen (2013) 
Higher caloric intake in areas where and when 
value of subsidy is higher Kaul (2014) 
 No: small or no impact on calories/weight: 
 Extremely low caloric elasticities (Kochar, 2005) 
 Introduction of TPDS did little to diminish 
nutritional outcomes in AP (Tarozzi, 2005) 
 But most of these studies are based on 
inadequate cross-sectional data
Propensity Score Matching: 
 Matching Households on: 
 State of residence, urban/rural residence, highest education 
level obtained by an adult above 21 in the household, 
household income and a squared term for income, number of 
adult equivalents in the households, number of married 
women in the household as a proxy for household structure as 
well as time availability, caste/religion categories (forward 
caste, OBC, SC, ST, Muslim, other religions), whether 
household has any toilet and whether it has indoor piped 
water. 
 When matching children add: 
 Child and mother characteristics including child’s gender, 
age, a dummy variable for infants, and number of children 
borne by the mother.
No improvement in weight for children from 
households that use PDS 
PDS Users Non-Users Difference S.E. T-stat 
Z score for weight-for-age 
Matched -1.614 -1.542 -0.071 0.042 -1.70 
Moderate Underweight (<2 SD) 
Matched 0.400 0.373 0.027 0.013 2.01 
Severe Underweight (<3 SD) 
Matched 0.160 0.149 0.011 0.010 1.10
Two possible scenarios with PDS 
subsidies for cereals 
Cheap Rice & 
Wheat 
Substitution 
Effect 
More calories 
from Cereals 
Less calories 
from 
fruits/milk 
Income Effect 
Same amount 
of cereal 
consumption 
Savings used 
for milk & 
fruit 
?
Age Adjusted Intake Per Person in 
matched sample 
PDS 
Users 
Non- 
Users 
Difference S.E. T-stat 
Quantity cereal (Kg) 
Matched 20.473 19.276 1.197 0.166 7.19 
Quantity milk (ltr) 
Matched 4.372 5.254 -0.882 0.114 -7.77
But what if we are doing a poor job 
of matching? 
 PDS use is a function of program efficiency 
and household time availability 
 In many remote villages the PDS shops may 
not be available 
 Program use has been particularly poor in 
areas where most shopping is done by men 
 How do we match on unobservables? 
 Role of longitudinal data
2012 consumption for matched samples 
based on prior PDS use 
Used PDS in 
2012 
Did not Use 
PDS in 2012 
Difference 
Cereal Consumption in 
2012 (kg/mo per adult 
equivalent) 
Used PDS in 2005 20.08 18.33 1.75 
Did not Use PDS in 2005 20.73 19.61 1.12 
Milk Consumption in 2012 
(Ltr/Mo per adult 
Eqivalent) 
Used PDS in 2005 3.95 4.10 -0.15 
Did not Use PDS in 2005 4.41 5.37 -0.96
Key Debate: Is cash transfer better than food subsidy? 
(Law allows for cash transfers, strong govt. interest) 
 Possibly yes for food: A four country study by Hoddinott suggests 
that where markets function well, cash is likely to increase food 
security (Hoddinott 2013) 
 Possibly not so much for nutrition: An analysis of 15 programmes, 
combining conditional cash transfers and unconditional cash 
transfers, shows an average effect of 0∙04 in height-for-age Z score 
(Ruel and Alderman, 2013) 
 We really need better understanding of the key issues in the Indian 
context before undertaking any policy reformulation 
 Classic Behrman and Deolalikar (1987) paper is a sobering reminder 
that for India nutrient elasticity of income used to be near zero (and 
only .3-.4 for other countries) 
 Ongoing analysis compares the role of income gain to changes in 
PDS access in shaping… 
 Household consumption patterns 
 Anthropometric growth curves for children over time

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Enhancing nutrition security via India’s national food security act

  • 1. Enhancing nutrition security via India’s National Food Security act: Using an axe instead of a scalpel? Sonalde Desai & Reeve Vanneman
  • 2. Repeated Cross-Sections, Longitudinal Panels and Cohort Studies • Description of changes over time • Useful snapshots when social/policy changes taking place Repeated Cross Section • Effects of early life experiences on later outcomes • Single cohort not suitable for studying social/policy changes • Attrition a major challenge Cohort Studies • Combination of repeated cross-section and cohort studies • Addition of new cohorts, within family analysis • Useful in changing context • Reinterviews major challenge, panels get old Panel Studies of households
  • 3. India’s National Food Security Act, 2013  Justification: High undernutrition and hunger  Solution: Two main pillars  Expansion of Public Distribution System (PDS) to cover 67% of the population for 5 kg of cereals at Rs. 1-3 per kg  Enhancement to Integrated Child Development Schemes (ICDS) to ensure children receive take home rations and hot cooked meals
  • 4. Crucial Question:  Is NFSA the best strategy for addressing undernutrition?  Implicit assumptions: GDP growth by itself is not sufficient. Undernutrition is caused by hunger.  Therefore, solution is more calories.
  • 5. Our empirical storyline 1. Our best estimate of current undernutrition.  About 37% underweight 2. PDS use? Results presented here  Increased cereal consumption,  Reduced milk & fruit consumption  No improvement in level of undernutrition 3. ICDS access? Results not presented, available on request.  Somewhat reduced undernutrition  But poor outreach
  • 6. India Human Development Surveys I and II  Largest nationwide panel survey in India  Full rounds 2004-5 & 2011-12  83% recontact rate after 7 years  90% rural recontact  72% urban recontact  Similar questionnaires as IHDS-I  Urban sample refreshed by addition of 2,134 new households
  • 7. IHDS-II (2011-12) Sample 33 States and UT 1474 villages and 988 urban blocks 42,152 households 215,754 individuals 39,264 ever married women 15- 49 12,148 kids age 8- 11 12153 Youth age 15-18
  • 8. For this analysis the sample is: 1. 42,152 households for household based PDS analysis 2. 10,715 children ages 0-60 months for analysis of underweight, limited to:  Children for whom both month and years of birth are available  Whose weight-for-age z scores range from -6 to +5 SD
  • 9. Public Distribution System (PDS)  Given to people holding ration cards for purchase through fair price shops  Subsidies for people holding Below Poverty Line (BPL cards)  PDS purchase by BPL card holders grew from 21% of the households in 2005 to 37% of the households in 2012 due to program expansion and increased efficiency in distribution  But still poorly targeted, allows for comparisons
  • 10. Improvement in targeting & efficiency between 2005 and 2012 Use of PDS by poor households also increased In 2004-5 poor households using PDS formed 20% of the population but by 2012 it was 37% 60 50 40 30 20 10 0 Distribution of Households with BPL/Antyodaya Card by Income Decile 1 2 3 4 5 6 7 8 9 10 Percent Income Decile IHDS-I (2004-5) IHDS-II (2011-12) Leakage 40% excluded
  • 11. Do we expect PDS to reduce malnutrition? From the literature..  Yes: increased caloric intake using NSS:  Lower decline in caloric consumption for the PDS users over time Himanshu and Sen (2013) Higher caloric intake in areas where and when value of subsidy is higher Kaul (2014)  No: small or no impact on calories/weight:  Extremely low caloric elasticities (Kochar, 2005)  Introduction of TPDS did little to diminish nutritional outcomes in AP (Tarozzi, 2005)  But most of these studies are based on inadequate cross-sectional data
  • 12. Propensity Score Matching:  Matching Households on:  State of residence, urban/rural residence, highest education level obtained by an adult above 21 in the household, household income and a squared term for income, number of adult equivalents in the households, number of married women in the household as a proxy for household structure as well as time availability, caste/religion categories (forward caste, OBC, SC, ST, Muslim, other religions), whether household has any toilet and whether it has indoor piped water.  When matching children add:  Child and mother characteristics including child’s gender, age, a dummy variable for infants, and number of children borne by the mother.
  • 13. No improvement in weight for children from households that use PDS PDS Users Non-Users Difference S.E. T-stat Z score for weight-for-age Matched -1.614 -1.542 -0.071 0.042 -1.70 Moderate Underweight (<2 SD) Matched 0.400 0.373 0.027 0.013 2.01 Severe Underweight (<3 SD) Matched 0.160 0.149 0.011 0.010 1.10
  • 14. Two possible scenarios with PDS subsidies for cereals Cheap Rice & Wheat Substitution Effect More calories from Cereals Less calories from fruits/milk Income Effect Same amount of cereal consumption Savings used for milk & fruit ?
  • 15. Age Adjusted Intake Per Person in matched sample PDS Users Non- Users Difference S.E. T-stat Quantity cereal (Kg) Matched 20.473 19.276 1.197 0.166 7.19 Quantity milk (ltr) Matched 4.372 5.254 -0.882 0.114 -7.77
  • 16. But what if we are doing a poor job of matching?  PDS use is a function of program efficiency and household time availability  In many remote villages the PDS shops may not be available  Program use has been particularly poor in areas where most shopping is done by men  How do we match on unobservables?  Role of longitudinal data
  • 17. 2012 consumption for matched samples based on prior PDS use Used PDS in 2012 Did not Use PDS in 2012 Difference Cereal Consumption in 2012 (kg/mo per adult equivalent) Used PDS in 2005 20.08 18.33 1.75 Did not Use PDS in 2005 20.73 19.61 1.12 Milk Consumption in 2012 (Ltr/Mo per adult Eqivalent) Used PDS in 2005 3.95 4.10 -0.15 Did not Use PDS in 2005 4.41 5.37 -0.96
  • 18. Key Debate: Is cash transfer better than food subsidy? (Law allows for cash transfers, strong govt. interest)  Possibly yes for food: A four country study by Hoddinott suggests that where markets function well, cash is likely to increase food security (Hoddinott 2013)  Possibly not so much for nutrition: An analysis of 15 programmes, combining conditional cash transfers and unconditional cash transfers, shows an average effect of 0∙04 in height-for-age Z score (Ruel and Alderman, 2013)  We really need better understanding of the key issues in the Indian context before undertaking any policy reformulation  Classic Behrman and Deolalikar (1987) paper is a sobering reminder that for India nutrient elasticity of income used to be near zero (and only .3-.4 for other countries)  Ongoing analysis compares the role of income gain to changes in PDS access in shaping…  Household consumption patterns  Anthropometric growth curves for children over time