Results and Cross-Cutting Issues
43
70 percent applied for enrolment. Regression analyses found that the following factors contributed to the probability of enrolment: (i) head of household is between 30 and 45 years of age; (ii) household has links to politicians and local authorities; and (iii) head of household completed primary education. There was no evidence of gender bias. Interestingly, health status was not a determinant of enrolment, suggesting that adverse selection may not be a factor affecting enrolment.13 Site visits and interviews by the authors suggest supply-side constraints to enrolment such as cost of transportation (to access enrolment site) and lost wages, incorrect information on identification documentation, and absence of head of household. Sun (2011) examined RSBY administrative enrolment data from 24 districts (17,000 villages) in seven states. He observed large variations in enrolment ratios in districts and villages, with an average take up rate of 45 percent. However, in-depth village-level analysis displayed even greater variation. For example, in 10 percent of villages there was no enrolment while in 2.5 percent full enrolment of BPL families was observed. The author suggests several reasons for these disparities, but the main problem may rest with the quality of BPL listings (see below). In some villages, no BPL families were listed. In other cases, the name of the village was incorrectly listed, such that individuals did not match up with villages. Distance and BPL village density may also affect enrolment. Enrolment agencies contracted by insurers are paid a flat fee per family enrolled, and therefore have little incentive to incur the extra cost of enrolling potential beneficiaries in remote villages or in villages with few BPL families.14 Insurers may also try to avoid villages where the probability of higher utilization is greater, such as villages with a high population of elderly and without access to basic primary care.15 In a subsequent analysis, Sun found some evidence that enrolment agencies focus on nearby and high BPL density villages but reported no evidence of cream skimming in terms of avoiding villages with higher ratios of elderly.16 To further examine the determinants of take-up ratios Sun combined RSBY administrative data with available household and village data from the Census. Of the potential eligible population of 11 million people, only 3 million were enrolled. He found the major determinant of take up was village BPL density. As suggested above, this makes sense in terms of the incentives that the enrolment agencies face. They are usually paid on a per family basis and therefore would incur lower enrolment costs in villages with high BPL density. Other determinants were [villager] access