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
5.3 TARGETING ERRORS OF EXCLUSION AND INCLUSION
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MMASY cards, because they supported the opposition of the ruling party. Dissatisfaction was also reported by respondent households of Kumargaon village in the similar ground.
Some example on the status of land ownership of MMASY and completely excluded households and multiple cardholding households will give a clear picture about it.
For example, the sample survey data shows that in Chaudhurirchar village, out of total 11 MMASY households, 7 households have their own land and among these households 6 are such households which has regular earning source in a month. The regular earning source includes salaried government service or owner of grocery shop or does some skilled labour.
On the other hand, out of 6 completely excluded household only 2 household holds small size of land, remaining 4 households possesses neither any land nor have any regular income source.
The pattern is similar in Kumargaon village, where sample data shows 8 MMASY households and 3 households with no card. Out of 8 MMASY households 5 households have their own operational holding of land, 3 households have regular income source like government job. In opposition to that, among 3 excluded households, one is landless, another one has taken 8 bigha cultivable land on lease and the remaining only salaried household have own land. There are also such households which possessed more than one type of card. These are categorised as ‗multiple card holder households‘ e.g. one APL household has MMASY card too and this household has own land and also has 3 regular salaried adult earning members. This shows the presence of selection bias though at a lesser degree, but this again proved that income targeting always leads to exclusion.
households are classified as more prone to seasonal food insecurity (MP_SFI) households and less prone to seasonal food insecurity (LP_SFI) households rather than strict poor and non-poor classification. The classification has made based on two basic economic indicators of the households i.e. possession of total operational holding of land and principal occupation of the member of the household. In the tabular analysis these criterions are named as ‗landholding‘ and ‗occupation‘ in brief. As the studied villages suffer from seasonal food crisis during flood season of the year therefore households are categorised for seasonal food secure and seasonal food insecure. Thus two errors of targeting has been calculated as,
• N= MP_SFIi+ MP_SFIe+ LP_SFIi+ LP_SFIe
Where, N= total number of sample household
• MP_SFIi= More prone to seasonal food insecurity households included in TPDS, in per cent
• MP_SFIe= More prone to seasonal food insecurity households excluded from TPDS, in per cent
• LP_SFIi= Less prone to seasonal food insecurity households included in TPDS, in per cent
• LP_SFIe= Less prone to seasonal food insecurity households excluded from TPDS, in per cent
Thus,
• Error of wrong exclusion is=MP_SFIe/N = MP_SFIe' = Type I
• Error of wrong inclusion is = LP_SFIi/N = LP_SFIi'= Type II
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Size of operational holding of land and principal occupation of the household members are found to be main determinant of the household‘s economic well being. Therefore these two has been selected as principal criteria to calculate targeting errors in both the villages.
Moreover , only BPL and AAY households are considered as TPDS beneficiary households as only these households receives food regularly and other households such as APL, MMASY and households without ration cards are not considered as TPDS beneficiary households while carrying out inclusion and exclusion error exercise. The criteria used in the calculation of targeting errors have been mentioned as follows.
More prone to seasonal food
insecurity(MP_SFI) Less prone to seasonal food insecurity(LP_SFI) Operational holding of land
Landless households, households
possessing marginal operational holding Households possessing medium and large operational holding Principal occupation of the household members
Small cultivators, hired manual labour in agriculture and non-agricultural sector, wood collectors, petty traders like puffed
rice sellers and potters, unemployed
Cultivators having medium size of operational holdings, livestock holders,
salaried, self-employed in non- agriculture such as owning a grocery
shop, hotel, pharmacy
Table 5.8Estimates of targeting errors in Chaudhurirchar and Kumargaon Chaudhurirchar
Criterion MP_SFIi MP_SFIe LP_SFIi LP_SFIe N MP_SFIi´ MP_SFIe´ LP_SFIi´ LP_SFIe´
Landholding 62 51 2 12 127 49 40 2 9
Occupation 56 44 8 19 127 44 35 6 15
Kumargaon
Landholding 53 41 7 10 111 48 37 6 9
Occupation 51 39 9 12 111 46 35 8 11
Source: Survey data, 2015
Table 5.8 explains the estimates of targeting errors in Chaudhurirchar and Kumargaon village. Table 5.9 shows the Type I and Type II error of targeting in both the studied villages. In Chaudhurirchar village, Type I error is 40 per cent and Type II error is 2 per cent for landholding criteria. Similarly it is type I and type II error is 35 per cent and 6 per cent respectively for occupation criteria. In Kumargaon village, Type I error is 37 per cent and Type II error is 6 per cent for landholding criteria whereas it is 35 per cent and 8 per cent respectively for the occupation criteria. Thus it was seen that ‗exclusion error‘ is far higher than that of ‗inclusion error‘ in both the studied villages.
Table 5.9 Type I and Type II errors based on the two indicators in the Chaudhurirchar and Kumargaon villages
Criterion Chaudhurirchar Kumargaon
Type I error Type II error Type I error Type II error
Landholding 40 2 37 6
Occupation 35 6 35 8
Source: Survey data, 2015
The Programme Evaluation Organisation of Planning Commission (2005) draws attention to some crucial points on overall performance of TPDS in general and types and magnitude of the targeting error, extent of leakage and diversion of subsidized foodgrains in particular for 18 states of India. According to this study, not only both ‗inclusion error‘ and ‗exclusion errors‘ are very high in Assam but also ‗exclusion error‘ is the highest in Assam among all other 18 states of India. ‗Exclusion error‘ for the state was 47.3 per cent and ‗inclusion error‘
was 17.2 per cent. ‗Exclusion error‘ is further classified into ‗identification error‘ and ‗error due to administrative malpractices‘. First type of error arises because of wrong information about household characteristics; wrong methodology used for identification and deliberately excludes some groups by those who are in charge of identification of the households at the grass root level. Second type of error means straightly denying BPL cards to genuine BPL families. Errors based on both of these two classifications are very high in Assam and
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‗exclusion due to identification error‘ is reported to be highest in Assam (35 per cent) among all other states. ‗Exclusion due to malpractices‘ was reported as 12 per cent. This type of error shows the level of non-existence of BPL cards with the eligible BPL households despite its entry in the official records which is usually termed as ‗shadow ownership‘. The first type of classification indicates deprivation of BPL households whereas the second classification indicates in addition to deprivation there is leakage of PDS grain.
Swaminathan (2009) also indicates to the fact that there is high exclusion (87.7) in Assam as most of the households have either APL card or no cards. The percentage is also very high for agriculture labour households (69 per cent) and SC households (81 per cent). Assam is also among those top ranked states having very high ‗exclusion error‘ (11 percent) (ibid).Another evaluation study conducted on performance of TPDS in six states of India i.e.
Bihar, Assam, Chhattisgarh, Uttar Pradesh, Karnataka and West Bengal shows that
‗exclusion error‘ is the highest in Assam (71 per cent) and lowest in Chhattisgarh (2 per cent) among these states. Inclusion error is highest in West Bengal (47 per cent) and lowest in Bihar (18 per cent). But according to this report number of false card is small in Assam. It is also reported that leakage ‗extremely high‘ is in Assam for APL categories because monitoring and management of TPDS operation is a complete failure in the state (NCAER, 2015). As per this report Assam has been reported as ‗historically poor performing‘ state including UP and West Bengal, Chhattisgarh as ‗historically good performing‘ state and Bihar and Karnataka as ‗reviving state‘ on the basis of the overall performance of the TPDS in these state.
5.3.1 Status of inclusion and exclusion after the implementation of NFSA, 2013 in the studied villages NFSA 2013 was implemented in the studied villages after my field survey was completed.
The NFSA 2013 implementation process and distribution of cards to the households was partially started during the time of survey i.e. May and November 2015. The information on the implementation of NFSA 2013 in the village was derived by conducting a short field trip in August, 2017. In Chaudhurirchar village, there was no inclusion of new cardholder households and earlier BPL, APL and MMASY households were identified as PHs. Thus in Chaudhurirchar village, total number of AAY households is 20 and total numbers of PHs are 109, and households which did not possess any ration cards (18) remained excluded even after the implementation of NFSA 2013. On the other hand in Kumargaon village, apart from the AAY households, all the earlier BPL, APL and MMASY households were included as PHs and above all, other households which did not possessed any ration cards before NFSA were also included as PHs. Thus total number of AAY households in Kumargaon village was 37 and total number of PHs was 74. Thus all households are covered within the TPDS frame in Kumargaon village after the introduction of NFSA 2013.