the effectiveness of targeting Analysing under AB PM-JAY in India March 2022 Policy brief Policy brief: Analysing the effectiveness of targeting under AB PM-JAY in India ISBN: 978-92-9021-002-3 © World Health Organization 2022 Some rights reserved. This work is available under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO licence (CC BY-NC-SA 3.0 IGO; https://creativecommons.org/licenses/by-nc-sa/3.0/igo). Under the terms of this licence, you may copy, redistribute and adapt the work for non-commercial purposes, provided the work is appropriately cited, as indicated below. In any use of this work, there should be no suggestion that WHO endorses any specific organization, products or services. The use of the WHO logo is not permitted. If you adapt the work, then you must license your work under the same or equivalent Creative Commons licence. If you create a translation of this work, you should add the following disclaimer along with the suggested citation: “This translation was not created by the World Health Organization (WHO). WHO is not responsible for the content or accuracy of this translation. The original English edition shall be the binding and authentic edition”. Any mediation relating to disputes arising under the licence shall be conducted in accordance with the mediation rules of the World Intellectual Property Organization (http://www.wipo.int/amc/en/mediation/rules/). Suggested citation. Analysing the effectiveness of targeting under AB PM-JAY in India. Licence: CC BY-NC-SA 3.0 IGO. Cataloguing-in-Publication (CIP) data. CIP data are available at http://apps.who.int/iris. Sales, rights and licensing. To purchase WHO publications, see http://apps.who.int/bookorders. To submit requests for commercial use and queries on rights and licensing, see http://www.who.int/about/licensing. Third-party materials. If you wish to reuse material from this work that is attributed to a third party, such as tables, figures or images, it is your responsibility to determine whether permission is needed for that reuse and to obtain permission from the copyright holder. The risk of claims resulting from infringement of any third-party-owned component in the work rests solely with the user. General disclaimers. The designations employed and the presentation of the material in this publication do not imply the expression of any opinion whatsoever on the part of WHO concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Dotted and dashed lines on maps represent approximate border lines for which there may not yet be full agreement. The mention of specific companies or of certain manufacturers’ products does not imply that they are endorsed or recommended by WHO in preference to others of a similar nature that are not mentioned. Errors and omissions excepted, the names of proprietary products are distinguished by initial capital letters. All reasonable precautions have been taken by WHO to verify the information contained in this publication. However, the published material is being distributed without warranty of any kind, either expressed or implied. The responsibility for the interpretation and use of the material lies with the reader. In no event shall WHO be liable for damages arising from its use. 1Acknowledgements This report presents key findings from a study on ‘assessing the effectiveness of targeting mechanisms under PMJAY’. It provides a detailed analysis of potential inclusion and exclusion errors in two select states in India (Haryana and Uttarakhand) to inform National Health Authority’s (NHA) policy and approach around beneficiary targeting. The WHO study team would like to acknowledge Oxford Policy Management Ltd. for their contribution to the execution of this study. The study team are grateful to concerned officials/experts at the NHA and Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) for their inputs at the inception phase of the project. Additionally, the study team would also like to thank NHA, State Health Agencies (SHAs), and Chief Medical Officials in the study districts for their support in carrying out the primary data collection. Project team World Health Organization • Dr Grace Achungura • Mr Jaidev Singh Anand • Dr Hilde DeGraeve Oxford Policy Management Ltd. • Ms Shriya Bubna • Dr Arpita Chakraborty • Mr Udayan Rathore • Ms Kirti Gupta • Ms Vinaya Padmanabhan • Ms Suneha Kandpal • Ms Rituparna Sanyal • Ms Bibha Mishra • Ms Sunayana Walia • Dr Nayan Jumar • Mr Vipul Singhal • Dr Ludovico Carrao • Ms Madhumitha Hebbar National Health Authority • Dr Vipul Aggarwal • Dr Ruchira Agrawal • Dr Kameshwar Rao
3Analysing the effectiveness of targeting under PMJAY in Haryana and Uttarakhand: evidence from a Primary Household Survey Highlights • Mixed methods studies based on quantitative analysis of household surveys aided by qualitative insights can provide valuable guidance in improving the effectiveness of targeting under PMJAY. This brief contributes to this direction and is based on a survey of 2121 households, 31 KIIs and IDIs, and 8 FGDs. • The overlap between intended beneficiaries’ (bottom 40%) recipient status (registration under PMJAY) is weak, with the chance of a bottom 40% household being registered under PMJAY being close to random. • Exclusion of the intended beneficiaries through design appears to be low for both the study states (10-11%). This suggests that the eligibility rules perform well in the identification of the intended beneficiaries • Overall errors in targeting are likely to be driven by implementation errors. Proximate estimates of implementation errors provide support to this finding. • As seen in Uttarakhand, expansion of eligibility rules has successfully reduced exclusion errors, but this has come at the cost of higher inclusion errors. • In the study region, no household in the bottom 40% had (i) a graduate degree, (ii) a motorised two, three or four-wheeled vehicle or (iii) at least 2.5 acres of irrigated land with at least one irrigation equipment. These vital conditions may be used to identify the beneficiaries of PMJAY. • Familiarity with the programme in both states is high, but a large majority are unaware of their eligibility status. • Public information campaigns to increase awareness around eligibility, the ability to self-select and register into PMJAY for the intended beneficiaries, and a mechanism for verification of eligibility status backed by a robust grievance redressal mechanism at the local level can improve targeting. 4Background India, in the last two decades, has positioned itself as one of the fastest-growing economies in the world. Despite the associated improvement in per-capita incomes, its distribution remains skewed, with a significant share of the population remaining poor and vulnerable to health shocks. These unforeseen health shocks often lead to further impoverishment and can keep these households mired in the poverty trap. In this context, Ayushman Bharat Pradhan Mantri Jan Arogya Yojana (PMJAY), launched by the Government of India in 2018, aims to provide resilience against such health shocks and intends to provide annual coverage of INR 500,000 for secondary and tertiary care to those households that are in the bottom 40% of the all-India population. Such targeting, especially in emerging and middle-income countries, falls under “progressive universalism”, whereby social protection for the most vulnerable is a starting point to long-term aspirations of universal health coverage. Currently, PMJAY identifies the bottom 40% of India’s population via proxies of economic well-being taken from the Socio-Economic and Caste Census (SECC 2011). In addition to the SECC 2011, the scheme includes the previously enrolled households under the earlier health programme (Rashtriya Swasthya Bima Yojana, RSBY). Further, many states and Union Territories (UTs) have horizontally expanded the eligibility criteria to cover additional beneficiaries either under PMJAY or under state-specific schemes. By virtue of not being universal, targeted programmes remain open to inclusion (households in the top 60% but covered by the schemes) and exclusion (households in the bottom 40% but not covered under the schemes) errors. These errors can potentially emerge at two stages. One, during the programme design, the eligibility conditions to identify the intended programme beneficiaries are finalised (henceforth design error). Two, during implementation where those deemed to be eligible (in-eligible) are provided (denied) programme benefits (henceforth implementation error). In this context, this policy brief summarises the findings from a primary survey conducted by Oxford Policy Management (OPM) in 2021 that was commissioned by the World Health Organization (WHO) to assess the targeting effectiveness of the PMJAY programme in the states of Haryana and Uttarakhand, respectively. Methodology The primary survey in the two states utilised a mix of quantitative and qualitative analysis to cater to its research objectives. While the quantitative data collection aimed to measure the tangible outputs against planned targets, the qualitative research was exploratory and designed to understand stakeholders’ diverse experiences to probe context-specific information. The primary survey (OPMPMJAY 2021) was conducted to quantify the magnitude of the design errors of inclusion and exclusion. Although the primary focus of the study was on design errors, the study also attempted to provide indirect estimates of the implementation errors. The implementation error, however, could not be directly studied given the confidentiality of the administrative data from 2011 (SECC 2011), which was a basis for PMJAY coverage during the time of the interviews. Under the quantitative workstream, OPM collected data from 2121 households (1159 in Haryana and 962 in Uttarakhand), while qualitative data collection covered a total of 31 interviews (combining key informant interviews (KIIs) and in-depth interviews (IDIs)) and eight focus group discussions (FGDs). To identify the bottom 40% of India’s population from these states of interest, the study used nationally representative thresholds for a standardised asset index and consumption expenditure, respectively. The former was taken from the National Family Health Survey, Round 4 (NFHS-4, 2015-16), whereas the latter was taken from the Consumption round of the 68th National Sample Survey (NSSO, 2011- 12) that was adjusted for inflation using the all-India Consumer Price Index for rural and urban areas, respectively to estimate the threshold of 2021. The questions on asset ownership and consumption expenditure in the OPM PMJAY 2021 survey were consistent with the representative surveys to ensure compatibility and ascertain if a household is from the bottom 40% of the population. In addition, the survey also collected information on a range of inclusion and exclusion conditions used to identify the PMJAY beneficiaries following the eligibility criteria in both the states (SECC 2011 for Haryana; SECC 2011, National Food Security Act (NFSA) database, government employees and pensioners for Uttarakhand). Finally, the household’s enrolment status in various other government schemes that were also based on SECC 2011 list was also recorded. As mentioned previously, this was done to provide proxy estimates for implementation errors. The OPM PMJAY 2021 survey could not be mapped to the actual SECC 2011 status, which was unobserved. This was because (a) households were unaware whether they were 5enlisted in the SECC 2011 database, and (b) household-level SECC 2011 data was not accessible to the study team due to confidentiality clauses. Using all the information collected in the survey, the study households were studied across five categories of interest. Sampling weights were computed at the household level to provide estimates representative of the population. 1. CE-Eligible 2021: Households identified as coming from the bottom 40% of the all-India population-based on consumption expenditure data 2. AI-Eligible 2021: Households identified as coming from the bottom 40% of the all-India population based on asset ownership in the form of a standardised asset index 3. Modified SECC-Eligible 2021 criteria: Households identified as “eligible” based on the SECC eligibility criteria as observed in the OPM PMJAY 2021 survey 4. PE-Eligibility 2011: Household under study identified as “eligible” in SECC 2011 using proxy measures (enrolment status in other schemes using SECC 2011 to target eligible) in 2011. 5. PMJAY recipient status: Actual recipient status (registration under PMJAY) based on any member in the household being registered under PMJAY during the survey in 2021. Key findings and policy implications Intended beneficiaries and PMJAY recipient status The overlap between intended beneficiaries’ (bottom 40%) recipient status (registration under PMJAY) provides an idea of how well the targeting under PMJAY is working as of 2021. Our findings suggest that about 35% of the households among the intended beneficiaries in Haryana had someone in the family registered under PMJAY. The corresponding figure for Uttarakhand was 48%. Amongst the top 60% of the households, 30% in Haryana had at-least one member in the household registered under PMJAY. In Uttarakhand, about 64% of the top 60% of households had at least one member in the family registered under PMJAY. Thus, preliminary evidence suggested that 4 to 5 out of 10 households in the bottom 40% are registered under PMJAY. The qualitative evidence was consistent with these results, and during these interviews, key service providers provided instances of how certain eligible households were systematically excluded, whereas those who were evidently rich found a way to get registered under the programme. This would then suggest that the chances of an eligible household being registered under the PMJAY are as good as random (Figure 1). “there are businessman, rich farmers, people who stay in multi-storied buildings, who are registered to the scheme. Even though they can pay for treatment, they are getting it free in private hospitals.” - PMAM staff from Uttarakhand Fig. 1: Coverage of bottom 40% & top 60% by asset classification under PMJAY (%) 35.21 29.56 47.87 63.72 64.79 70.44 52.13 36.28 0 20 40 60 80 100 120 Bottom 40% Top 60% Bottom 40% Top 60% Haryana Uttarakhand Someone in HH registered No one in HH registered Source: OPM PMJAY survey, 2021 Note: The bottom 40% and top 60% are based on asset thresholds that were taken in the form of the asset index. 6These differences could emerge from two sources: (a) the eligibility rules used to identify the poor (design error), or/and (b) the implementation of the programme that aims to provide access to those who have been identified as beneficiaries (implementation error). Design errors Design errors occur when there is a mismatch between the programme design to identify intended beneficiaries and the eligibility rules as per the modified SECC-Eligible 2021. Estimates of exclusion errors using asset measures of economic well-being appear to be low for both states. This suggests that about 89% to 90% of the intended beneficiaries are, in fact, eligible as per the eligibility rules outlined under modified SECC-eligible 2021 criteria. Thus, this suggests that eligibility rules used from the SECC (modified SECC 2021) do a reasonably good job of identifying the intended beneficiaries. Overall, this suggests that the exclusion errors of design for both states are low, and the overall errors of exclusion are likely to come from the implementation rather than design. As in the literature on targeting, the asset-based approach is preferred to the consumption approach as this is found to be more robust on account of (a) measurement errors, and (b) sensitivity to shocks. We find that inclusion errors of design are higher for both states but more so for Uttarakhand. For Haryana, these range from 47% to 53%. In contrast, for Uttarakhand, these are between 75% to 77%, depending on whether we use an asset or a consumption-based approach to identify the bottom 40% of all India’s population. These findings suggest that there is room for efficiency gains through methods that could reduce inclusion errors in design. One potential approach to this would be the identification of automatic exclusion conditions, which can keep the above 60% of the all-India population out of the eligible population but do not impinge on the identification of the bottom 40% population. Juxtaposing both these errors, it appears that Uttarakhand has been able to keep exclusion errors in design low by expanding the eligibility criteria for identification of the bottom 40% by adding those households that are covered under the NFSA as well as those with government jobs to the existing conditions of SECC-2011 eligibility. However, an associated cost of expanding the eligibility criteria appears to have been incurred in the form of higher inclusion errors. This is confirmed by glancing at exclusion and inclusion errors of design for Uttarakhand if only the SECC 2011 eligibility conditions are used to verify eligibility (Figure 3) Fig. 2: Exclusion and inclusion errors in design for Haryana and Uttarakhand (%) 10.15 47.25 57.33 53.40 0 10 20 30 40 50 60 70 80 90 100 Exclusion error of Design Inclusion error of Design Exclusion error of Design Inclusion error of Design Mismatch in AI-Eligibile 2021 & modified SECC Eligible 2021 Mismatch in CE-Eligibile 2021 & modified SECC Eligible 2021 Haryana 11.24 77.20 10.41 75.40 0 10 20 30 40 50 60 70 80 90 100 Exclusion error of Design Inclusion error of Design Exclusion error of Design Inclusion error of Design Mismatch in AI-Eligibile 2021 & modified SECC Eligible 2021 Mismatch in CE-Eligibile 2021 & modified SECC Eligible 2021 Uttarakhand Source: OPM PMJAY 2021 survey, OPM’s calculations Note: Exclusion error of design refers to % of poor (CE-eligible 2021/AI- eligible 2021) who are ineligible as per SECC-eligibility 2021 criteria. Meanwhile, inclusion error of design refers to % of non-poor (as per CE-eligible 2021/AI- eligible 2021) who satisfy SECC-eligibility 2021 criteria. 7Fig. 3: Exclusion and inclusion errors in design for Uttarakhand with different design rules (%) 29.77 38.74 11.24 72.87 0 10 20 30 40 50 60 70 80 90 100 Exclusion error of Design Inclusion error of Design Exclusion error of Design Inclusion error of Design Mismatch in AI-Eligibile 2021 & SECC Eligible 2021 Mismatch in AI-Eligibile 2021 & SECC Eligible 2021 + NFSA registration Uttarakhand Source: OPM PMJAY 2021 survey, OPM’s calculations Note: Exclusion error of design refers to % of poor (AI- eligible 2021) who are ineligible as per SECC-eligibility 2021 criteria. Meanwhile, inclusion error of design refers to % of non-poor (AI- eligible 2021) who satisfy SECC-eligibility 2021 criteria. In the figure above, we present these errors with (a) just the original set of design rules from SECC 2011 and; (b) adding just the NFSA registration status to (a) Overall, access to NFSA by itself maps to SECC eligibility status with only limited success. In Haryana, only 47% of the SECC eligible households have an NFSA card. The figure for Uttarakhand stands at 64% (Table 1). Table 1: Contingency table for SECC eligibility and access to NFSA cards (%) Haryana NFSA card: No NFSA card: Yes SECC eligible: No 62.13 37.87 SECC eligible: Yes 53.22 46.78 Uttarakhand NFSA card: No NFSA card: Yes SECC eligible: No 43.38 56.62 SECC eligible: Yes 36.25 63.75 Source: OPM PMJAY 2021 survey, OPM’s calculations Note: rows sum to 100 (%) and reflect what % of those who are SECC eligible/in-eligible have/do not have access to NFSA cards Design errors at the top and bottom of the pyramid We note that the design errors may not treat those at the bottom and top of the ladder equally. To check for such disparities, we divided respondents from each state into quintiles, which are five equal-sized groups clustered on the basis of economic status (asset ownership). In the following figure, Q1 represents the bottom 20% of the households (poorest), whereas Q5 represents the top 20% of the households (richest). For the bottom-most quintile (Q1), which constitutes the most vulnerable households, we found that about 67% and 92% of these were modified SECC-eligible (2021) in Haryana and Uttarakhand, respectively. Thus, Figure 4 further reinforces our earlier point of Uttarakhand achieving lower exclusion errors of design by broadening the eligibility conditions. For the most well-off segment (Q5), 70% of these households were modified-SECC eligible in Uttarakhand, as against 39% in Haryana. In fact, a larger share of households from Q5 in Uttarakhand (70%) were eligible as per the modified SECC-eligibility 2021 as against the households in Q1 from Haryana (67%). Given that the overall economic status in both these states is above India’s average and that Uttarakhand does not lag behind Haryana by much, these numbers are suggestive of higher inclusion errors in Uttarakhand. 8Fig. 4: State-wise design errors for different consumption quintiles 0 20 40 60 80 100 Q1 Q2 Q3 Q4 Q5 Q1 Q2 Q3 Q4 Q5 Haryana Uttarakhand Modified SECC eligible 2021: Yes Modified SECC eligible 2021 : No Source: OPM PMJAY 2021 survey, OPM’s calculations. Note: Quintiles are computed state-wise on the basis of monthly per-capi- ta consumption expenditure for the household. Fig. 5: State-wise design errors for different asset quintiles 0 20 40 60 80 100 Q1 Q2 Q3 Q4 Q5 Q1 Q2 Q3 Q4 Q5 Haryana Uttarakhand Modified SECC eligible 2021: Yes Modified SECC eligible 2021 : No Source: OPM PMJAY 2021 survey, OPM’s calculations; Note: Quintiles computed state-wise on the basis of a standardised asset index using Principal Component Analysis (PCA) for the household. The quintiles were computed using the distribution of the standardised asset index (0-100) for each state separately. Q1, Q2, Q3 and Q4 thresholds for Haryana (Uttarakhand) are 35.95 (23.77), 49.96 (39.37), 60.41 (52.62), 69.50 (64.50), respectively. Thus, at each quintile threshold, respondents in Uttarakhand are on average poorer than those in Haryana. Proximate estimates of implementation errors Given the limitations in accessing the SECC 2011 household-level administrative data, we are unable to provide robust estimates of implementation errors. However, given that a similar set of eligibility conditions from SECC-2011 were used to extend benefits under various other government programmes, we aimed to provide proxies of these estimates that are subject to inclusion and exclusion errors within these other government programmes. Given the inherent weakness in this comparison, we also analysed how eligibility status in 2021 fares with PMJAY recipient status for the respondents. Mismatched pairs in the former (PE-eligible 2011 and PMJAY recipient status) provide a proximate idea about the existing levels of implementation errors. The percentage of PE-eligible 2011 households that were not covered under PMJAY (exclusion errors of implementation) were about 53% and 38% for Haryana and Uttarakhand, respectively. Thus, unlike exclusion errors of design, which were low at around 10% to 11%, exclusion errors of implementation appear to be relatively higher. This would suggest that exclusion errors are more of an implementation challenge than a design problem. Meanwhile, the percentage of PE-ineligible 2011 households that are covered under PMJAY (inclusion errors in implementation) stood at 24% and 53% for Haryana and Uttarakhand, respectively. Again, just 9as in the case of inclusion errors in design, inclusion errors in implementation were found to be lower for Haryana than Uttarakhand (Figure 6). Fig. 6: Proximate implementation errors of exclusion and inclusion (%) 52.96 24.46 37.59 52.67 0 20 40 60 80 100 Exclusion error of Implementation Inclusion error of Implementation PE eligible 2011 & PMJAY registration status Haryana Uttarakhand 61.30 20.98 40.81 62.20 0 20 40 60 80 100 Exclusion error of Implementation Inclusion error of Implementation Modified SECC eligible 2021 & PMJAY registration status Haryana Uttarakhand Source: OPM PMJAY 2021 survey, OPM’s calculations To better understand the impediments to getting registered under PMJAY, the OPM-PMJAY 2021 survey also asked why not being registered under PMJAY. For the bottom 40% of the all-India population, using both consumption and asset-based methods, we find that most households were aware of the PMJAY programme. However, not being aware of their eligibility status was reported as the most vital reason for not being able to register under PMJAY. This holds true for both Haryana and Uttarakhand (Figure 7). Fig. 7: Awareness about PMJAY and reasons for not being registered, as reported by heads of households in the bottom 40% 94.18 45.54 16.88 1.87 15.04 17.51 72.00 51.12 27.26 0.00 9.87 5.57 0 10 20 30 40 50 60 70 80 90 100 Have heard of PMJAY Not sure about their eligibility Had no time Hospital or place of registration too far Tried but got rejected Applied but registration status unknown Responses by bottom 40% households by consumption Haryana Uttarakhand 79.55 58.21 16.59 0.00 6.72 12.24 79.66 56.90 27.17 17.42 2.17 0.00 0 10 20 30 40 50 60 70 80 90 100 Have heard of PMJAY Not sure about their eligibility Had no time Hospital or place of registration too far Tried but got rejected Applied but registration status unknown Responses by bottom 40% households by asset Haryana Uttarakhand Source: OPM PMJAY 2021 survey, OPM’s calculations 10 Profile of design errors In both Haryana and Uttarakhand, the bottom 40% population (as compared to the top 60%) were characterised by: (i) a higher share of household heads with no formal education (in fact, none in the bottom 40% of households were graduates); (ii) higher coverage by MGNREGA, old-age / widow / disabled pensions, (iii) higher dependency ratio, (iv) higher percentage of scheduled caste and scheduled tribe groups, (v) no or low ownership of motorised two / three / four wheeler vehicle and (vi) minimal ownership of irrigated lands. Additionally, in Uttarakhand, no household belonging to the bottom 40% had access to safety nets provided by regular salaried jobs like Employees Provident Fund (EPF) and Gratuity. Moreover, no economically poor households in the state reported having invested in financial instruments like fixed or recurring deposits. In both the states, the comparison of those in the bottom 40% population that could not fulfil the eligibility rule (exclusion error by design) with the bottom 40% reveals that this subset has a higher share of households where (i) someone earns at-least INR 10,000 a month, (ii) ownership of some land other than homestead land (only in Haryana), (iii) access to clean cooking fuel, (iv) reside in urban areas. Thus, it is important that these characteristics are not used as part of the design rules to exclude the potential beneficiaries. The comparison of the top 60% with those in the top 60% who satisfied the eligibility rules (inclusion error by design) shows that for the latter (i), a larger share of these households reports having Jan Dhan accounts (in both states), (ii) has a lower share of households owning land other than a homestead, (iii) lower access to clean cooking fuel, (iv) lower share with motorised two, three or four-wheeler vehicles or (v) a pucca house with at least three rooms.1 Thus, it is likely that focusing on these correlates contributes to inclusion errors in design. Profile of implementation error While comparing PE-eligible 2011 with those that are PE-eligible 2011 but not registered under PMJAY (exclusion error by the implementation) in Haryana, the results show that a higher share of excluded households (i) owns land other than a homestead, (ii) have a pucca house with at-least three rooms and (iii) reside in urban areas, however with a lower share of these households reporting (i) having a Jan Dhan account (in both states), and (ii) belonging to SC/ST social groups. The comparison between the households that are PE-ineligible in 2011 with those that are PE-ineligible but registered under PMJAY (inclusion error by the implementation) in Haryana shows that a higher share of households that are PE-ineligible but registered under PMJAY have (i) household heads with no formal education, (ii) access to Jan Dhan accounts and (iii) residing in urban areas; and a lower share of these households’ report (i) owning land other than a homestead, (ii) living in pucca houses with at-least three rooms, (iii) having access to clean cooking fuel, (iv) own motorised two, three or four-wheeled vehicles, and (v) having access to Kissan Credit Card (KCC) with the credit limit of at-least INR 50,000, which could be one vital condition that may be used to keep out PE-eligible households from PMJAY coverage. In the case of Uttarakhand, the PE-ineligible but registered under PMJAY had a higher share of (i) access to a ration card, (ii) pension benefits for the vulnerable, (iii) access to clean cooking fuel, ownership of land, other than a homestead, (iv) pucca house with at-least three rooms and (v) appliances like refrigerator. 1 Point (ii) – (v) are from Haryana only 11 Box 1: Summary of recommendations based on the OPM-PMJAY 2021 survey Recommendation 1: Design rules, as observed during the survey, perform well in the identification of the bottom 40% of households. Household-level eligibility to these rules needs to be updated regularly, especially as some of these conditions are likely to change over time and were last recorded in the SECC of 2011 Recommendation 2: As seen in Uttarakhand, additional design rules (such as NFSA registration) can help in further lowering exclusion errors of design. Thus, the active role of state governments (SHAs) is vital to account for state-specific realities and requirements. However, provisions need to be inbuilt as including NFSA can lead to high inclusion error. Recommendation 3: To reduce inclusion errors of design, which were found to be higher than exclusion errors, it is vital to identify conditions that can allow the identification of in-eligible households. In the survey, we found that no household in the bottom 40% satisfied the following conditions, which could be used as exclusion conditions 1. Household head with a graduate degree; 2. A motorised two, three or four wheeled vehicle; 3. At-least 2.5 acres of irrigated land with at-least one irrigation equipment Recommendation 4: Some design rules, like access to government jobs in a household is certain to increase inclusion errors without making any dent on reducing exclusion errors. In the survey, we found that all such to be in top 60% of the population. Thus, from a financing point of view, the NHA and the SHAs need to be on the same page on the following: 1. Potential for expanding coverage and intended programme beneficiaries under the broader net; 2. Division of the financial burden for those households that are included from the top 60% of the population Recommendation 5: Although a large majority of the households were familiar with the PMJAY scheme, most of them were not aware of their own eligibility status. Public information campaign to inform the respective residents of the eligibility conditions and simplifying registration is likely to further improve access. With the recently launched “Aap ke Dwar Ayushman” campaign, more focus can be given on the eligibility criteria and enrolment processes to enable people self-enroll into the scheme. Recommendation 6: Given the budgetary costs associated with a Census exercise to identify potential beneficiaries, granting the ability to eligible households to self-select and register into PMJAY, when coupled with a robust verification process and grievance redressal mechanism, is one potential way to reduce implementation costs and reduce errors Recommendation 7: Inability to access to the confidential administrative data prevents accurate estimation of implementation error, which is suggestively high. National and state authorities may consider review of its data sharing and privacy policies to ensure more accurate estimates implementation errors and identifying the levers that are most strongly correlated with errors of implementation. 12 Summary This brief provides a snapshot of the OPM-PMJAY 2021 survey aimed at analysing the effectiveness of targeting the PMJAY programme in Haryana and Uttarakhand, respectively. As part of the study, we interviewed 2121 households in the two states and conducted multiple KIIs and FGDs to gauge the potential gaps in targeting and the context in which these challenges emerge. Overall, as far as getting intended beneficiaries registered under the PMJAY is concerned, we find that the exclusion of the intended beneficiaries through design appears to be low for both the study states. This suggests that the set of eligibility rules used from the SECC (modified SECC 2021) does a reasonably good job of identifying the intended beneficiaries. However, household status to these conditions may change over time, which may require more frequent tracking and verification. The proximate estimates of implementation errors suggest that these are of a more serious concern than design issues. The results also seem to suggest a trade-off whereby expansion of design rules can stem exclusion errors at the cost of incurring higher inclusion errors. In general, most households were familiar with the PMJAY programme, but a majority were unaware of their eligibility status. Public information campaigns to increase awareness around eligibility criteria, the ability to self-select and register into PMJAY for the intended beneficiaries, and the mechanism for verification of eligibility status backed by a robust grievance redressal mechanism at the local level can potentially be leveraged to improve targeting.
This policy brief assesses the effectiveness of targeting of beneficiaries under the Ayushman Bharat Pradhan Mantri Jan Arogya Yojana (AB PM-JAY) in the states of Haryana and Uttarakhand.