Chapter 8: In conclusion: rural households of Assam require continuous food based interventions provides a commentary on the role and significance of food based welfare
6.5 IMPACT OF TARGETED PDS ON FOOD CALORIE CONSUMPTION
REVIEW OF SELECT STUDIES
Various studies in India have assessed the impact of PDS on household calorie consumption. Kishore and Chakrabarti (2015) found an improvement in diet quality due to increase in cereal subsidy in certain states of India. The study was carried out by dividing the states as ‗treated‘(Andhra Pradesh, Chhattisgarh, Tamil Nadu, Odisha and West Bengal where rice was supplied to the majority of their population at a very low price by using the state‘s own budgetary resource) and ‗non-treated‘ (other than the above mentioned states) groups. An average of 3kg more rice per month was purchased by the households of the treated states than their non-treated counterparts due to a more inclusive TPDS policy.
In Chhattisgarh, money saved out of rice was used to purchase pulses, vegetables, edible oil and sugar which had positive impact on household level nutrition. This study found a positive impact of increase in TPDS subsidy on calorie intake and dietary quality of the households. The study emphasized on ensuring better performance of TPDS. It further recommended that without carrying out administrative reforms to deal with widespread corruption, progressive policies such as NFSA may not be successful. Similar studies on TPDS having significant impact on household food consumption are also concluded in
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Mahadevan and Suardi (2013), Rahman (2014, 2016), Bhattacharya et al, (2016) based on NSSO data, primary surveys used in the studies of Khera (2011) and Chatterjee (2014).
However contradictory results also emerge that show TPDS not having significant impact on household food consumption. For e.g. Tritah (2003) explained that the benefit of the food subsidy accrued to the poor, generates more food expenditure than generating real income, therefore, suggested a new method of poverty estimation as ‗food equivalent poverty line‘. Similarly, Kochar (2005) concludes that very small number of poor could avail TPDS subsidies and also the elasticity of calorie intake with respect to the value of food subsidies are very low for rural households because of shortfall in the quantities of food purchased by the households than their actual entitlements.
Another reason for the poor performance of the TPDS was low financial support by the government. However, the scholar mentions that it was because of the number of beneficiaries were reduced which led to fall in the optimal amount of FPS procurement, had an adverse effect on the food supply of BPL households after the economic reforms process. Other reasons behind this relative failure of TPDS as mentioned may be the process of decentralization where village level functionaries play pivotal role in identifying beneficiary households. However, that needs proper cross sectional evidence; in the absence of which, the author is more positive about ‗regional targeting‘.
Kaushal and Muchomba (2013) draw similar conclusion but for two groups of districts. One group is that where wheat and rice are staple food and the other group is that where coarse grains are the staple food. It was found that increase in income resulting from the food price subsidy changed the consumption pattern but had no positive effect on nutritional level being measured by per capita calorie intake. Therefore, the income transfer resulting from increase in food price subsidy changed the consumption pattern of the rural households
towards subsidized but more expensive source of nutrition like wheat and rice and shifts away from the non-subsidized cheap coarse grain. Khera (2011) explained various aspects of the working of the TPDS in Rajasthan through primary data collected from eight villages of four districts from a random sample of 388 households. It was found that one third of the sample households did not utilize their full quota. She used the ‗dual pricing model‘ to address the problem of under purchase of TPDS commodities.
Table 6.8 shows some of the selected studies which measure the impact of TPDS on household food security. Most of the studies are on NSSO data with panel data analysis except few primary studies. The dependent variables used in most of the studies are per capita cereal consumption, households‘ quantity of rice consumed, per capita per day calorie intake and implicit subsidy from PDS. However, per capita consumption does not take into consideration intra-household food distribution. In order to adjust it Mahadevan and Suardi (2013) suggested ‗calorie gap‘ which is an adjusted value of per capita with NSSO consumer unit.
Bhattacharya et. al (2016) also used consumer unit with National Advisory Council‘s (NAC) recommendations of 7 kg cereal per capita per month and calculated foodgrain deviation from 7 kg. This deviation is taken as proxy to measure food security of the households.
However, this 7 kg is an undervaluation for a rural manual labourer and can be considered as a minimum need. In order to study how different ration cardholder households respond to this minimum requirement, ‗foodgrain deviation‘ is taken as a dependent variable.
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Table 6.8 :Some of the selected studies examining the role of PDS in household food security Study Data source and
method Dependent
Variables Independent variable PDS impact Tritah, 2003 Cross section,
NSSO 55th round,
propensity score matching
Real income
transfer Age, educational level, household asset ownership position, land owned, household size, income source of the household, access to electricity, social group, source of fuel
+, not significant
Kochar, 2005 Panel data, NSSO 50th (1993-94) and 55th (1999-2000) round, OLS and Instrumental Variable
Per capita
calorie intake PDS subsidy, BPL households, price of PDS rice and wheat
not significant
Swaminathan, 2009
Cross section, NSSO 61st round
N/A N/A
High exclusion error Khera, 2011 Cross section,
PDS survey, OLS, Quantile and Tobit
Per capita cereal
consumption
PDS subsidy, BPL households,
price of PDS rice and wheat Under purchase of commodities
are demand driven Jha et al, 2013 Cross section,
Primary survey, 2007-2008, OLS and Tobit regression
Implicit subsidy
Waiting time at FPS, land owned, land gini, proportion of BPL cardholders, ratio of PDS to market price, geographical distance, adult population, wage rate
+, not significant
Kaushal and
Muchomba, 2013 Panel data, NSSO 50th (1993-94) and 55th (1999-2000) thick round, OLS and IV
Per capita per day calorie intake
ration card dummy, MPCE, consumption of cereal and non- cereal in pre-TPDS expansion and Post-TPDS expansion
period, 0
Mahadevan and
Suardi , 2013 Cross section, NSSO thick round, OLS and Quantile
regression
Calorie gap Household size, female headed household, literacy status, land per capita, occupation, caste,
religion, ration card dummy + significant
Kishore and Chakrabarti, 2015
Panel data, NSSO 61st (2004-05) and 66th (2009-2010) thick round, difference in difference
Quantity of
rice consumed PDS coverage, Per capita PDS purchase of ration cardholder households and non-ration card holder households, Average price for rice from FPSs, leakage
+, significant
NCAER, 2015 Cross section,
targeting errors N/A N/A High exclusion
error in non- NFSA state Bhattacharya et
al, 2016
Panel data, NSSO thick round, Quantile regression
Foodgrain deviation
Effective subsidy, real income transfer, Food diversity index, real MPCE, Socio religious dummy
+, significant Bedamatta,2016 Time series,
Cross section, Government of Orrisa, 1980's to 2013, GoI, primary data, targeting errors
Long term view of state specific PDS policy in Odisha
N/A Geographical
targeting leads to large scale welfare losses
among the poorest households and
information distortion at the
ground level
Source: Compiled by the author from various studies Note: N/A implies not applicable
6.6 CONTRIBUTION OF TARGETED PDS TO HOUSEHOLD CEREAL