Business Case for Health Microinsurance: Examples from Kenya and India Richard Koven MicroInsurance Centre Jakarta November 2013
What Drives HMI Business Case?
India: the highest concentration of HMI programs in the world
SAS Poorna Arogya Healthcare 4
Voluntary, private models struggle with low scale and high expenses government subsidized health programs reach scale more quickly and crowd out private HMIs Average enrollment in health microinsurance programs Public Private >4M ~ 70,000 = 100,000 lives
Uptake is a function of participation rate and the size of the target population HMI Target Enrollment Uptake % Yield Population Mode BASIX 524,000 8% 41,135 Individual Shepherd 111,206 18% 20,017 Individual Yeshasvini 12,280,000 25% 3,070,000 Group SEWA 300,355 33% 99,117 Individual ICICI RSBY 22,686,275 51% 11,570,000 Group ME 333,333 57% 190,000 Group SAS 62,902 58% 36,483 Group Artisans 1,500,000 91% 1,365,000 Group Weavers 6,119,681 94% 5,752,500 Group Uplift 124,458 100% 124,458 Group 100 HMIs Composite 418,812,608 23%
USD USD 3 2,5 2 1,5 1 0,5 0-0,5-1 -1,5-2 6,0 5,0 4,0 3,0 2,0 1,0 0,0-1,0-2,0 Sample Private HMI Unit Costs Across Value Chain 2.39 PREMIUM CONTRIBUTION 2,81 0.19 2,62 Premium Contribution 1.11 INSURANCE CO. SHEPHERD NET Distribution fees 1.28 1.48 Yeshasvini Net INTERNAL EXPENSE 0.03 0.04 Internal Expense 1.28 0.04 1.02 CLAIMS ADMIN FEE DONOR GAIN/LOSS Expenses are low 4.22 1.84 0.50 100% 46% 54% 62% 54% 2% 43% (21%) Sample Public HMI Unit Costs Across Value Chain Expenses are high 0.17 Admin fee Claims Donor Gain/Loss 100% 7% 93% 1% 1% 150% 65% 6%
14.000 12 Members in Thousands 12.000 10.000 8.000 6.000 4.000 2.000 11 Years 2 Years 6 Years 10 Years 8 Years 4 Years 6 Years 8 Years 8 Years 5 Years 10 8 6 4 2 Program Age in Years - Program Age and Scale in India no clear link 0
With or without public subsidy, Indian HMIs are struggling to find a workable business model Program age appears to have little correlation with the ability to scale or a move toward financial sustainability The support of a government subsidy drives scale if not business case Achieving scale appears to drive down unit costs Competition also drives down costs Expense ratio and not loss ratio is the primary driver of losses and lack of sustainability Achieving scale is in part a function of uptake which requires a plan design that balances low premiums and attractive coverage. While scale is a key component of the business case for microinsurance, scale in and of itself does not does not guarantee a business case RSBY will drive the evolution of health microinsurance in India for years to come
Kenya HMI: A lot of activity
But little enrollment 1.1 M * 1.37 M 30,000 MFI members Total insurance industry premiums Active microinsurance policies Few Kenyans have health insurance coverage HMI polices Health 10% Health is small part of overall market for insurance 10% 0 and the NHIF accounts for the majority of that coverage 88% *MIX, 2011 0%
0 Private vs. Public HMIs NHIF dominates the market 1400 1200 1000 800 600 400 200 Premium (KSH) 13B 48M Private HMIs NHIF Active policies NHIF Private HMIs Was 300,000+ Now 2,000 the largest single program appears to have been largely scuttled when Jami Bora become a bank 930,000 30,000
20.000 18.000 16.000 14.000 12.000 10.000 8.000 6.000 4.000 2.000 1.5% - 1,144-76,252* Avg Annual income (KSH) [6,354 per month] Selected Kenyan HMIs Premium (KSH) 1.5% Willingness to pay amongst persons in an area where an HMI scheme was operating, according to an independent study in India *Kenya National Bureau of Statistics, 2011
HMI is not profitable in Kenya as of yet HMI is bundled with life/funeral which somewhat offsets HMI losses Most programs are IP only and the opportunity to employ OP as a compliment to NHIFs IP is missed Commissions and expenses seem to be relatively contained (10% commissions, 15%-30% expenses) Managing eligibility is a big issue; smart cards are mostly not present but much needed
Comparing India and Kenya Population GDP/capita India Kenya 1.2 Billion 41 million USD 3,876 USD 1,766 India Kenya Expense Ratio HMI Premium Per Policy Lower in Kenya 25%-40% Exceed 50% in India USD 3.50 India RSBY USD 21.50 Kenya NHIF Expense ratios are lower in Kenya but premiums for HMIs are higher
9,00 8,00 7,00 Kenya & India Selected HMIs: Premiums, Claims, Expenses & Loss Ratios Kenya Composite 133% 41% Expenses 6,00 Premium PMPY USD 5,00 4,00 92% Claims India Composite 127% Admin PMPY 3,00 80% Expenses Claims PMPY 2,00 1,00 0,00 47% Claims Kenya Example Composite Private Premium $8,36 $3,21 Admin $3,46 $2,56 Claims PMPY $7,73 $1,51
What can we learn from Historical Analogs?
Voluntary Private Insurance not the norm in developed (OECD) economies 98% of the population covered by public insurance 28% of the population covered by private insurance (OECD averages, excluding US) 5% Private health insurance = Small % of total health spending
Health Insurance in the US the exception 25% of the population covered by public insurance 35% 72% of the population covered by private insurance Private health insurance = one third of total health spending 49% people receive health insurance from an employer Only 5% buy health insurance individually
Income level an important determinant of private health insurance purchase in virtually all OECD countries, private health insurance is predominantly purchased by high income individuals Ireland Spain 70% 30% Highest income decile Highest income group 8% 2% Lowest income decile Lowest income group Purchase private health insurance
Summing Up Individual voluntary enrollment does not lead to significant and sustained participation, and thus is not a viable strategy- especially for private HMIs targeting relatively small populations Without scale, and without government subsidy, administrative costs are too high to support attractive coverage at affordable premiums However community based programs that achieve high uptake rates and share resources are more likely to succeed
Thank You! Rick Koven Rkoven@microinsurancecentre.org Co-Authors India: Denis Garand, Taara Chandani Kenya: Joseph Jamwaka, Anne Kamau, Donna Swiderek Research Assistance Emily Zimmerman, Katie Biese