Analysing the effectiveness of targeting for flagship health insurance programme in India

The project objective was to assess the effectiveness of the targeting mechanisms under the Ayushman Bharat Pradhan Mantri Jan Arogya Yojana (PMJAY), the flagship programme of the Government of India. Launched in September 2018, PMJAY provides a health cover of INR 500,000 per family per year for secondary and tertiary healthcare. The programme intends to reach the bottom 40% of the all-India population based on measures of economic well-being, as identified via the Socio-Economic Caste Census (SECC) 2011, along with some state-specific selection criteria, like National Food Security Act (NFSA) data base. The study, implemented in Haryana and Uttarakhand states of India, aimed to assess the effectiveness of how well the target population, that is the bottom 40% by the economic status, was covered in each state under the PMJAY scheme.


PMJAY scheme used the SECC 2011 / NFSA database to identify the eligible households. However, due to the data security and safeguarding norms, the study team could not access the actual SECC / NFSA data to identify the household’s eligibility. Hence, proxy measures were used to identify the possible eligibility of the schemes.

Our approach

The primary survey in the two states utilized a mix of quantitative and qualitative analysis. 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 (OPM-PMJAY 2021) was conducted to quantify the magnitude of the design errors (occurring due to the targeting through SECC / NFSA data sets) or implementation errors (occurring due to the scheme implementation challenges by the agencies) of inclusion (non-poor, but covered under PMJAY) and exclusion (poor, but not covered). The quantitative survey covered 2121 household interviews, while the qualitative survey included 21 key informant interviews / focussed group discussions.

Using the nationally representative data (National Sample Survey and National Family Health Survey), we estimated the threshold levels of bottom 40% of the population, which were also collected from the households, along with information on inclusion and exclusion conditions used to identify the PMJAY beneficiaries.

Based on these, we estimated the proportion of households, who were part of the (i) inclusion error due to design, (ii) exclusion error due to design, (iii) inclusion error due to implementation, and (iv) exclusion error due to implementation.


Overall, as far as getting intended beneficiaries registered under the PMJAY is concerned, the study found 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 do a reasonably good job of identifying the intended beneficiaries.

Additionally, we found that in the bottom 40% of the population from both states, no household head had (i) a graduate degree, (ii) a motorised vehicle or (iii) at-least 2.5 acres of irrigated land with at-least one irrigation equipment. These are some vital conditions that may be used to identify the potential beneficiaries of PMJAY.

For inclusion errors of design, we find that these are on a higher side, especially in Uttarakhand. Further analysis in Uttarakhand reveals that although access to NFSA does map to the bottom 40% population, there is almost no overlap between the eligible group and someone in the household having a regular government job. Thus, excluding these households from the eligibility would reduce the inclusion errors of design without adversely impacting the exclusion errors.

With respect to implementation, we find exclusion errors of implementation to be quite high in comparison to similar errors from design. Thus, overall exclusion errors are more of an implementation challenge than a design problem.

We would like to thank Shriya Bubna, Suneha Kandpal, Rituparna Sanyal, Bibha Mishra, Sunayana Walia, and Dr. Ludovico Carrao for their contribution to this study.

Areas of expertise