Week 39: “What’s the biggest threat to life and health insurance business in Kenya [Sub-Saharan Africa] today?”

In 2001, a guest lecturer in my 1st year "Introduction to Actuarial Science" SAC 101 course unit asked this question. He then worked as an underwriter for what was then the insurer with the largest life book in Kenya.

In the context of a formal insurance and social security system in Sub Saharan Africa with a coverage of under 10%, the invitation was tantalizing—to find the 90%!

I recall the bland textbook responses we provided, often circling around financial literacy, expanding formal capacities, and resolving underlying poverty. He turned to us and simply said:

“It is a small restaurant nestled in the Kenyatta International Convention Centre (KICC), where every single evening folks gather to contribute towards death or disease within their family and social networks.” 

He argued then, that the monies raised every single evening often surpassed the collective premiums paid by those seated in the room to cover themselves and their nuclear families. We collectively pondered the apparent disconnect. Was it a lack of trust in formal systems? Or were there inefficiencies such that formal systems could not move at the speed that met social and cultural expectations? Was it that the idea of sharing risk was oxymoronic in the Kenyan psyche? Clearly, the idea of risk pooling is alive and thriving within the target clientele.

Or was it something deeper? Was it a case of getting stuck in the square peg round hole trap where the dominant ways of looking at the issue were misfit? Were we witnessing a social revolt that rejects what the formal offer is at an ideological level? Could it be that the way formal insurance conceives of life, health, and social security shocks simply did not strike a chord with the populace? That was quite a heavy doze to ponder!  

He ended his message with a caution that technology was simply going to put this threat of collective “off-grid” contributions on steroids. Then, hardly in my 20’s I could not see the social revolt he spoke to. I did not have invitations to contributions in restaurants. I was just interested in growing my gravitas as a potential resource for his business. I must admit the response felt a bit like a mock to the tough actuarial formulas, we’d been bursting our mental faculties to derive just prior to this “soft” social class.

Fast forward nearly 2 decades, and in 2017, 

I am delivering a unit on “case studies in Actuarial science”. I’m now on the other side of the table inviting my students to ponder on where the 90% market of life and health insurance was “lost”.  The needle had not moved even the slightest – still at below 10%—and the question was as relevant as it was in 2001. I revisited the same social question again with my class “What’s the biggest threat to life and health insurance in Kenya [Sub-Saharan Africa] today?”

This time, true to the prophecy, technology put "off-grid" social meetups on "steroids.” Enter M-PESA. To the outside eye, it solved a money transfer problem. A deeper scrutiny reveals M-PESA as a fast-growing risk sharing platform (Ahmed & Cowan, 2021; Alinaghi, 2019; Jack & Suri, 2011, 2014). 

Déjà vu and my class regurgitated a similar thread of answers to that of 2001—financial literacy, poverty… but with two new twists: First, microinsurance. Taunted as a game changer that would dramatically scale up the scope of insurance by solving the “elephant in the room” of low incomes. If the barrier was the difficulty to raise the lumpsum monthly or annual premiums – let’s match the demanded payments to the daily or weekly routines that are the reality of the majority; Second, advanced technology –  the realms of nano technology and driverless vehicles that would revolutionize the mortal risks; and Artificial intelligence (AI) that would allow copious risk and underwriting data to be analyzed and rendered on neat dashboards that aid in the development of products based on personalized risk assessments such as weather indexed insurance products for Sub Saharan farmers. AI could also improve internal systems helping detect underwriting and claim anomalies on a manager’s dashboard that could reduce fraud and leakages in the insurance system – a big hurdle that chains the industry.

Now early in my academic and research career, by appearance, I was a “lucky” one. I had two private health insurance covers as benefits from my employment and that of my partner. In addition, I also participated in the national health cover, as well as the national social security fund. Admittedly, I am a bit of an insurance junkie (blame it on education). I also purchased three additional life endowment policies (a payoff if I survive or die up to a certain date) and a death policy (a payoff only if I die before a certain date). I joked to my partner then, at that stage of my career, with my monthly pay slip battered with repaying huge education debt and paying for increasing household costs - I was worth more dead than alive!

I was now receiving invitations to the “off-grid” insurance meetings in restaurants, church and other social grounds contributing to death and diseases. As predicted technology was further fueling this “parallel” insurance making it more accessible and accountable. Enter WhatsApp. Today when a shock event occurs the first form of organization is digital, often on WhatsApp.  This virtual meetup is buttressed with physical meetups that also serve the psychosocial need to grieve and console within the family and social network. The youthful excitement of micro insurance and AI from my students 6 years ago has progressively been tapered off in my lived reality where I increasingly experience the ideological disconnects “square peg in round hole” challenge.

Give an example: The matriarch falls ill. We are quickly summoned to risk sharing arrangements, sometimes with specified targets. The relational boundaries are clearly wider than what the insurer would consider. It includes 2nd and even 3rd or 4th households from polygamous arrangements. It includes 2nd and 3rd-level cousins. It encompasses non-biological relations such as farmhands. This presents a much wider span in defining “insurable interest” in the African context compared to the neat 1 man, 1 wife, and biological children span that underpins formal insurance.

The contributions made also serve broader socio-economic coordinates beyond present medical bills sometimes including intergenerational considerations. For instance, if the matriarch was taking care of some financial obligations like school fees for some of her grandchildren left behind, there would be an understanding that the payments should go towards meeting these generational and redistributive obligations.

Additionally, my presence and participation within these “off-grid” networks also serve as a form of membership and solidarity within the wider risk – sharing community beyond the financial limitation to unlock the repertoire of social and cultural benefits. For instance, in the case of death, there is a culturally determined sharing of obligations, often along non-financial lines of solidarity and reciprocity, such as those charged with preparing gravesites, those who will prepare the meals, and those who will animate the traditional and religious rites.  

This is the remaking of the social revolt, countering the formal systems accused of creating social fractures that produce inequities, exclusions, and hierarchies along socio-economic lines within wider family groups; and that further falls short in the basic insurance law of large numbers—that majority are left outside and do not contribute to sharing the risk. 

I therefore lean into this communities, which hold the "memory,” so that when my time of need comes, they may remember.

Fast forward to present moment. March 2024. The Government is once again flogging the dead horse of attempting to find the elusive 90%.

First stop, Health Insurance. Enter Social Health Insurance Fund (SHIF). Health has some positive gains with a coverage of 26% in Kenya (one in every four Kenyans). With SHIF the government hopes to transform the 1 out of 4 to be 4 out of 4.

Some pundits have argued that this is structurally and logically dead from the start.  Structurally, it does nothing to respond to healing the fragmented health system. Literary as it prepares to start, doctors are preparing to down their tools. The government claims it cannot mobilize the monies needed to absorb medical interns begging the question, just who will take care of the anticipated additional patients now able to access medical facilities leveraging on the social side of the fund?

Logically, it heightens the flight risk of those able to pay and the cheese may be moved from the simplistic government projections of how to fund the scheme. At 2.75% of gross incomes, SHIF more than double premiums of higher income earners justified as subsidizing for majority of low- and no-income earners. An urban formally salaried Kenyan earning a gross income of 200,000 would be paying Kshs. 5,500 monthly up from Kshs. 1,700 (323% increase). Annually, this works out to Kshs. 66,000. The Kenya Demographic and Health Survey of 2022 estimates those living in urban areas spend annually on average Kshs. 61,774 on inpatient and outpatient expenditure combined. This higher income earner, positioned as the financial backbone for the scheme is now faced with a real dilemma. The risk pooling advantage of insuring is no longer manifesting to self. Self-insurance is now a real choice. Going “dark” in the sense of choosing informality and becoming invisible from the system becomes a considered path. The government machinery can only be stretched to a finite end in its policing endeavors and the tipping point is soon reached where those subsidizing the scheme are too few to hold it together.  

Here is another way to look at it. Estimates from the Kenya National Bureau of Statistics (https://www.unfpa.org/data/world-population/KE) as of 2023

Total population in Kenya = 55.1 million

Working age population (Age 15 to 64) = 60% = 33 million

Total employed (as of 2022) = 19. 1 million

[Derived] unemployment rate = 42%

Distribution of employed:

In local and national government, and parastatals = 1 million (5.2%)

In private salaried employment = 2 million (10.5%)

In informal trade with a trading area = 6 million (31.4%)

In casual labor with no regular income no assets = 10 million (52.9%)

If the math works out, SHIF is heavily relying on 15.7% (regular income public and private) to subsidize SHIF for the 52.9% casual laborer's hard to enforce payments. special systems will have to be put in place to ensure compliance with 31.4% informal traders.

Simply put (1:6) every one salaried Kenyan household is expected to subsidize the cost of 6 other Kenyan households. A very steep ladder!