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Hunger / HDI | PDS/ Ration/ Food Security
PDS/ Ration/ Food Security

PDS/ Ration/ Food Security

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What's Inside

 
Key findings of the study titled: How Can Food Subsidies Work Better? Answers from India and the Philippines by Shikha Jha and Bharat Ramaswami, No. 221, September 2010, Asian Development Bank, http://www.adb.org/documents/working-papers/2010/economics
-wp221.pdf, are:

•    The present study estimates the effectiveness of targeting in terms of exclusion and inclusion errors; and the effectiveness of program delivery in terms of leakage from pilferage or illegal diversions, and leakage due to excess costs (relative to the private sector, or inefficiency of the public program).

•    The study informs that economic welfare of poor households in developing countries is sensitive to food prices. For many of the  poor, food-based safety net programs provide their only hope of survival in the event of steep price rises. Such programs can protect poor segments of society from major shocks, insure them against risks and associated income losses, and provide consumption smoothing. However, the performance of such programs varies widely, reflecting a number of shortcomings that undermine their effectiveness. As they often consume substantial budgetary resources, food subsidies also become a source of anxiety to the government seeking to reign in budgetary deficits. This is especially so in times of rising food prices.

•    The academic and policy literature recognizes that the gains to the poor depend on targeting as well as program delivery. However, most of the studies have only evaluated the targeting performance of subsidies. From this literature, it is well known that most transfer programs are costly because of substantial non-target beneficiaries.

•    From a survey of universal food subsidy schemes, Coady (2002) finds that the median targeting performance implied that the government spent $3.40 to transfer $1.00 to the poor.

•    In their metasurvey of income transfer programs, Coady, Grosh, and Hoddinot (2004) conclude that interventions that use some methods of targeting (e.g., means testing, geographic targeting or self-selection in public works) result in the target group receiving a greater share of benefits.

•    A standard policy prescription, especially from multilateral institutions, is to recommend that governments target subsidies toward the poor and not waste resources subsidizing the nonpoor.

•    There is no generalized theoretical presumption that policy should always aim to reduce inclusion errors. The literature offers examples where targeting is costly both administratively as well as in economic terms because of incentive effects (Besley and Kanbur 1993, Kanbur 2009). In addition, Gelbach and Pritchett (2000) argued that programs that are tightly targeted toward the poor (i.e., low inclusion errors) do not receive political support from the nonpoor and thus are ultimately endangered. In addition, there are the practical difficulties of targeting.

•    In their metasurvey of studies that evaluate income transfer programs, Coady, Grosh, and Hoddinot (2004) found very few studies that looked at both program costs and benefits. And even such information consisted only of administrative costs, ignoring the costs due to corruption or theft.

•    The mandates of TPDS are multiple, including price stabilization, ensuring food access by the poor, and supporting farm prices. TPDS deliver in-kind subsidies. It offers subsidies on rice and wheat.

•    India has government agencies that source, store, transport, and distribute the grain to designated retail outlets. The TPDS primarily sources grain from domestic procurement.

About the TPDS

•    Volume of grain distributed between 2004-2008 via TPDS was 32 million tons. TPDS' budgetary allocation as a percentage of GDP between 2004 and 2007 was 0.72 percent. The quota of foodgrain distributed is fixed per household.

•    In India, the central and state governments together run a marketing channel solely devoted to the distribution of the subsidized food. At the retail level, this involves a network of “Fair Price Shops” (FPS) that sell subsidized grain to consumers. Subsidized grain is not accessible elsewhere. The FPS is usually run by private agents who receive a fixed percentage as commission for their efforts. The FPS is often restricted to selling only subsidized grain. The central government is responsible for procurement, storage, transportation, and bulk allocation of foodgrains to different states. The state government is responsible for transporting and distributing the grain within the state through the network of FPS.

•    India introduced targeted food subsidies in 1997. The current regime is called targeted public distribution system. Subsidies depend on whether the household is classified as above poverty line (APL), below poverty line (BPL), or poorest of the poor (POP or the Antayodaya Yojana program).

•    All households are entitled to a monthly quota of 35 kg of rice or wheat per month. In principle, the prices of subsidized grain are supposed to be fixed with reference to the government’s “economic cost”, i.e., the cost incurred by government agencies in procuring, storing, transporting, and distributing grain. BPL households are supposed to receive 50% subsidy (i.e., 50% of economic cost) while APL households are not supposed to be eligible for any subsidy at all. The prices for POP households are fixed below that of BPL households and not with reference to economic cost.

•    The APL households in 2008/2009 received a subsidy in excess of 50% of economic cost.

•    The list of BPL beneficiaries is prepared through a BPL census. In the latest census of 2002, households received scores based on 13 criteria. The BPL households were identified as those who fell below a cut-off score (decided by the respective state governments). If the total of BPL identified households exceeds that which is estimated by the central government, the subsidy on the excess households has to be borne by the state government.

Inclusion and Exclusion errors

•    The budgetary cost of food subsidy in India topped 1% of gross domestic product (GDP) in 2002 but later came down to around 0.65% toward the end of the decade. The decline happened because of the rapid growth in GDP since about 2003.

•    Inclusion errors do not differentiate households according to their distance from the poverty line. Furthermore, inclusion errors only tell us about how many recipients are nonpoor, not how much subsidies they get. If the inclusion error is zero then the poor receive the entire subsidy. At the other extreme, if the inclusion error is 100%, then the fraction of the subsidy reaching the poor is zero.

•    The share of the poor in the income transfer denoted by s is the targeting measure that is used most widely in studies evaluating income transfer programs and was therefore used by Coady, Grosh, and Hoddinot (2004) to compare targeting effectiveness across programs in a metasurvey of different studies. Ravallion shows that a targeting measure defined as the difference between the program’s participation rate for the poor and that for the nonpoor (called the targeting differential) performs better than the share measure.

•    Inclusion errors arise when a government spends $1 on provision of food subsidy, but poor households receive only a fraction of it. Such a diminution in the amount of subsidy that reaches households is called a targeting leakage. While it is generally agreed that a targeting leakage (due to inclusion errors) should be minimized, the debate in the income transfers literature is whether and how it can be done. The debate is enduring because minimizing inclusion errors can be costly administratively) and often leads to greater exclusion errors. With such a trade-off, optimal targeting depends on how much weight the government puts on inclusion error relative to exclusion error.

•    Evidence on the design and performance of social safety net programs from 47 countries across Africa, Asia, Eastern Europe, and Latin America shows that targeted programs achieve a high proportion of transfers to the poor, with the poor receiving, on average, around 25% more than they would without targeting (Coady 2003). In other words, the inclusion error in targeted programs is on average lower than in untargeted programs.

•    Based on the survey questions, a household is defined to be a recipient of food subsidies if it purchases subsidized rice or wheat or both during the survey reference period. While the targeted PDS was launched in 1997, it is generally agreed that targeting was not accomplished by 1999. Therefore the results from 1999/2000 (when the previous largescale expenditure survey was carried out) correspond to a pre-targeting regime, while those from 2004/2005 refer to a targeted subsidy regime.

•    Exclusion error in 1999-2000 was 64 percent and in 2004-05 was 70 percent. Inclusion error in 1999-2000 was 76 percent and in 2004-05 was 70 percent. Exclusion errors are uniformly high at 70% in both rural and urban areas while the inclusion errors are higher in rural areas (73 percent) as compared to urban areas (59 percent).

•    Exclusion errors could happen either because households chose not to participate in the program or because of mistargeting. Mistargeting could happen in two ways. First, a poor household may not be classified at all. In this case, the household does not receive the food eligibility card and cannot make purchases from the public distribution system. Second, even if a household receives a food eligibility card, it may be wrongly classified as an APL household and is not therefore entitled to the larger subsidy offered to households classified as BPL or POP.

•    For poor households in rural areas that hold either the BPL or POP eligibility card, the participation rate is 61%. This drops sharply to 13% for households with APL eligibility. For households without any eligibility, the participation rate is 4%. The associated weights are 0.4, 0.4, and 0.2 respectively. In other words, 60% of the poor are either classified incorrectly as APL or not classified at all (i.e., without eligibility to any subsidy).

•    If this kind of mistargeting is eliminated and all poor are classified as either BPL or POP, the participation rate would improve. If the participation conditional on eligibility remains invariant, then the participation rate would nearly double from 31% to 61% in the rural sector. Hence mistargeting is a major reason for the high exclusion error. Notice, however, that participation does not reach 100% because nearly 40% of poor households do not participate despite eligibility. This underscores the fact that there are factors other than eligibility that are also barriers to participation. The analysis for the urban sector is similar: here the gains from correct targeting are greater as the participation rate would rise from 30% to 77%.

•    As per capita grain consumption for all poor and nonpoor households varies between 10 and 12.5 kg per month, the TPDS on average accounts for about 40% of total grain consumption of the households that receive subsidies. Note also that for an average family of five, total household monthly consumption is nearly 20 kg, which is much less than the entitlement of 35 kg per month.

•    In the rural areas the share of poor in population is 28 percent and their share in subsidized food grains is 31 percent. In the urban areas the share of poor in population is 26 percent and their share in subsidized food grains is 46 percent. In aggregate, the share of poor in subsidized food grains is 33 percent whereas their share in total population is 27 percent.

Diversions and Leakages

•    Because of the price difference between subsidized grain and grain sold through regular marketing channels, there are powerful incentives to arbitrage and make illegal profits. Leakages are the illegal diversions of subsidized grain to regular market channels. They are typically estimated by comparing the distribution of subsidized grain from administrative records to the receipt of grain by households calculated from survey data.

•    For India, using data from 1986–1987, Howes and Jha (1992) estimated the average ratio of PDS consumption to supply in 18 major states to be 65%, ranging from 5% in Haryana to 94% in Jammu and Kashmir. That is, on an average there was 35% diversion. There does not seem to have been much of an improvement since then as similar estimates have been derived by other researchers. For example, Ahluwalia (1993) estimated that in 1986/1987, 37% of the supply of subsidized rice and 38% of the supply of subsidized wheat were illegally diverted. Dutta and Ramaswami (2001) estimated these figures for 1993/1994 for the states of Andhra Pradesh and Maharashtra. They found illegal diversions to be of the order of 15% for rice in Andhra Pradesh and 30% and 19% respectively, for rice and wheat in Maharashtra. A study by Tata Consultancy Services (1998) found illegal diversions to be 31% and 36% for rice and wheat at the all-India level in the late 1990s. The Planning Commission of India (2005) study that examined leakages in India after the implementation of the targeted PDS concludes that illegal diversions of rice and wheat at the all-India level in 2003/2004 was 37% of the total supply of subsidized grain meant for the BPL category.

•    In 2004-05, the per capita consumption of subsidized foodgrains was 1.03 kg per month while the per capita supply of subsidized food works out to be 2.27 kg per month. This works out to a leakage of 55% of subsidized foodgrains supply. In 1999/2000, these numbers were 1.01 kg and 1.61 kg per month, respectively. These discrepancies are large and suggest a serious problem with diversions.

•    In 2004-05, the aggregate leakage for rice is 40% and expectedly diversions are greatest from POP allocations (72 percent) and least for APL allocations (5 percent). The aggregate leakage for wheat is 73% and the diversions are high for all the categories. In 2004-05, the total cost of illegal diversion of rice was Rs. 38875.5 million and wheat was Rs. 48219.96 million.

Excess Costs

•    All government agencies incur costs in purchase, transport, and distribution of subsidized food. Since this is an activity also done by private agents, it is useful to compare government costs with private costs to ascertain the efficiency of government interventions. In their review of literature about distribution costs, Jha and Srinivasan (2004) show that private traders operate at costs lower than those incurred by the government agency in the areas of marketing, storage, trade, and transport despite several controls and restrictions imposed upon them.

•    Dutta and Ramaswami (2001) used the above methodology to demonstrate that in 1993/1994, 27% of government budgetary expenditure on food subsidy in the state of Andhra Pradesh was wasted by inefficiency of government agencies. The figure for the state of Maharashtra in the same year was 16%. A more recent study by the Planning Commission of India (2005) finds that in the year 2003/2004, delivery through the private sector was more efficient in all states except Kerala. The evidence indicates that at the all-India level, the government’s food subsidy costs would have been lower by 35% if the government costs matched that of the private sector.

•    In 2004/2005, the central government’s economic cost of distributing rice and wheat were Rs. 13.29 and Rs. 10.19, respectively. To this must be added margins for wholesalers and retailers and transportation charges at the retail level. We do not have estimates of these costs for 2004/2005. A comparison of economic costs with retail prices will therefore give a lower bound to the “excess” costs incurred by the government. The NSS consumption expenditure data for 2004/2005 provides information about quantities and expenditures on various items by households. A unit value can be derived from this information. As richer households buy higher-quality grain, their unit values are higher. Because of large quality variations in rice prices, purchase costs for rice are lowest for POP households and highest for APL households. In wheat, mean prices are about the same between BPL and APL households but are lower for POP households.

•    As TPDS grain quality is generally considered to be below average, we take the price paid by BPL households to be representative for such quality grain. Comparing with the economic costs of the state agencies in 2004/2005 (Rs. 13.29 per kg for rice and Rs. 10.19 for wheat) we obtain the difference as excess cost. The excess cost for rice is Rs. 2.80 per kg, and Rs. 0.85 per kg for wheat. The total excess cost of rice was Rs. 46033.34 million and of wheat was Rs. 10956.5 million.

•    It is interesting to note that India’s TPDS, despite being a targeted program, brings only one third of the total subsidy to the poor.

•    In 2004-05, total Income Transfer to Poor was Rs. 21351.55 million and to non-Poor was 38219.79 million. Total illegal diversion cost was Rs. 87095 million and excess cost was Rs. 56990. Total cost of subsidy in 2004-05 was Rs. 203657 (approx). 

•    The share of subsidy going to the poor is 11% in India.

Conclusion

•    If inclusion errors were minimized to zero, the share of the poor would rise at most to 29 percent in India.

•    The debate on a targeted versus universal transfer scheme misses the point that there are huge savings to be had from trimming diversions and excess costs, i.e., program waste. The Indian state of Chhattisgarh has claimed significant reduction in corruption by computerizing the supply chain, from paddy procurement to the distribution of rice in 2007/2008; and by making public the movement of grain from warehouses to retail outlets. It is suggested that this has improved transparency and governance (Dhand et al. 2009).

•    An alternative to in-kind transfers are food coupons or restricted cash transfers. As opposed to general cash transfers, food coupons are conditional or tied grants that allow consumers to purchase limited quantity of foodgrains at a subsidized price.

•    In the Indian case, a food coupon alternative would eliminate the dual marketing system (of private and government), which would resolve the endemic issue of the viability of the government marketing system.

•    If there are staples other than rice (or wheat), a food coupon system could easily accommodate it without the need for physical and institutional infrastructure (procurement and distribution) that is specially set up for that purpose. In parts of India, poor consume “inferior” coarse grains such as sorghum and pearl millet, which are not subsidized by the current regime.

•    Food coupons could allow consumers to spend their budget on their preferred commodities and would therefore be less distortionary in consumption, reducing their costs of participation. This could also happen through improved economic access as consumers would be able to use these coupons at a more convenient retail outlet. While there are potential issues of fraud in food coupons as well in terms of counterfeiting and improper use, it seems far easier to track and audit numerically coded coupons than to do so for physical stocks of grain. Governments sometimes balk at the costs of investing in technologies such as smart cards. The payoffs must, however, be seen in relation to the resources lost in diversions and excess costs.

•    Conditional cash transfers are another alternative to food subsidies. Such transfers have been widely and successfully used in many Latin American countries. In these transfers, the conditionality is of a different form to that of food coupons—relating to the use of social programs of education and health. Here cash transfers are conditional on attendance in schools and health clinics. Program benefits are designed to contribute to long-term human capital development and to provide immediate poverty relief. These benefits are in effect like negative user fees that are paid instead of charged to program participants who attend schools or visit clinics.

Note:

BPL: Below Poverty Line
APL: Above Poverty Line
POP: Poorest of the Poor


According to the report titled: Social Protection for a Changing India, The World Bank, http://www-wds.worldbank.org/external/default/WDSContentSe
rver/WDSP/IB/2011/04/20/000333037_20110420235516/Rendered/
PDF/612750v10ESW0P1rt0Volume0I01PUBLIC1.pdf:

•    During 2009-10, Rs. 42490 crore was allocated for food and Rs. 2866 crore was allocated for kerosene/ LPG under the public distribution system (PDS). It covered 23.3 percent of all households (above poverty line-APL and below poverty line-BPL in 2004-05). Annapurna scheme covered 1.7 percent households with elderly.

•    While PDS consumes almost 1 percent of GDP and has wider coverage than other safety net programs – between 20-25 percent of the population in the mid-2000s based on actual drawing of grains by beneficiaries, and closer to 40 percent based on administrative numbers on BPL households - its impact on the poor is very limited in many states, particularly a number of lagging states.

•    This is due to a combination of high leakage of grains (estimated by the Planning Commission to be around 58 percent nationally in the early 2000s and even higher based on estimates using NSS data), a range of demand and supply side issues in program design and implementation, and considerable leakage of subsidies to the non-poor. Although many of the shortcomings of PDS and its very poor performance have been known for some time, it continues to absorb substantial public resources with limited benefits for the poor.

•    The percentage of BPL grain that got leaked (during early 2000s) in Andhra Pradesh, Assam, Bihar, Gujarat, Haryana, Himanchal Pradesh, Karnataka, Kerala, Madhya Pradesh, Maharastra, Orissa, Punjab, Rajasthan, Tamil Nadu, Uttar Pradesh, West Bengal and India are: 57.6, 53.7, 91.1, 47.1, 66.7, 45.9, 70.9, 39.0, 66.0, 34.5, 36.4, 89.5, 35.0, 65.6, 67.5, 27.0 and 57.9, respectively.

•    The percentage of BPL grain that got diverted (during early 2000s) in Andhra Pradesh, Assam, Bihar, Gujarat, Haryana, Himanchal Pradesh, Karnataka, Kerala, Madhya Pradesh, Maharastra, Orissa, Punjab, Rajasthan, Tamil Nadu, Uttar Pradesh and West Bengal are: 20.6, 41.7, 81.5, 42.1, 55.7, 31.4, 43.4, 21.7, 62.4, 26.5, 23.4, 76.5, 32.0, 15.7, 61.3, 19.2 and 36.4 respectively.

•    In Andhra Pradesh, it is estimated that the introduction of food coupons has reduced leakage in the PDS by up to 25 percent.

•    The poor long run performance of the PDS in many states suggests that the medium term vision of a reformed PDS for most groups should be cash-based, though this would face substantial resistance in light of the ongoing debate around the Right to Food legislation. A reformed PDS could still provide food-based support for specially vulnerable groups (consistent with Supreme Court orders), and in specific areas facing chronic or acute food shortages, but for most areas and most people, a cash-based social assistance system seems a more efficient and transparent means of providing an income floor.

•    An intermediate solution currently being mooted in the 2010 Economic Survey is to transfer the subsidy directly to households (rather than the PDS store owner) through food coupons with a lumpsum entitlement that can be exchanged at any PDS store.

•    Methods such as self-targeting (in public works), mixed methods of identifying the poor (as in social pensions) have notably better targeting efficiency and inclusion of the poorest, while some states rely on community wealth ranking and verification.

•    While the benefits of PDS to households are spread across India, the main beneficiaries of public procurement of grains to feed the PDS are concentrated among farmers in a few states: Punjab, Haryana, some parts of Uttar Pradesh, and Andhra Pradesh to a lesser extent.

•    While only around 60 percent of eligible households in Bihar had been provided with food coupons in the first year of implementation, access among them dramatically increased as a result of the reform, rising from only 2 percent to around half of BPL households. Bar coded coupons/ration cards have been introduced under TPDS in six states.
 

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