Could Basic Life Insurance Help Reduce Poverty?
Better data can lead to better public policy outcomes
About the Author: Armed with a PhD in Social Work and a penchant for research and statistics, I set out on a course to better understand the world. I’ve spent the past 20 years helping enterprises better understand their customers, employees, and broader public sentiments, leading to better decision making. Not forgetting my social work beginnings, I wonder if we couldn’t ask new questions to come up with better solutions to some of the troubling public policy concerns that surround us, such as inter-generational poverty.
Understanding the upside: Governments across the globe have made much of consumer protection of late, standing up for the public interest against predatory marketing and sales practices. These protections are particularly important in defending the rights of individuals with modest means purchasing complex financial products and services. Consumers, in particular those who may lack the financial literacy required to fully appreciate the ramifications of such transactions, rightfully rely on government to help set standards on what constitutes good versus bad personal financial advice. Regulators quite rightly square off against unscrupulous practices, enforcing marketplace conduct standards in tandem with certification and licensing of individuals providing financial services. Enforce ever-higher standards on industry and expand the regulators reach and control. At first glance, what can be the downside?
Revealing the downside: There is intellectual folly in single-dimensional thinking. As ever more and tighter standards are imposed, an important trade off in the market goes unchecked. Compliance costs go up, making serving the small consumer ever less economical. While regulatory policing tries to ensure no maleficence, policy-makers are silent as to how many more people go un-served in the new more tightly regulated marketplace. Life Insurance products are a prime example, as the regulatory bodies have no mandate to ensure that moderate and low-income earners have access to protection products. Indeed, while this is the socioeconomic group most directly affected by tragic life events such as the death or terminal illness of a family breadwinner, they also happen to be the least served.
Regulatory frameworks are often built around weeding out what Social Policy and Decision Scientists call False Positives (a Type 1 statistical error). If you take the example of life insurance, the regulator protects the public against being insured “incorrectly” which in this instance attempts to prevent someone being placed in an inappropriate insurance policy through poor advice. Regulatory fiats, educational standards, exams, licensing, etc. all are built to ensure this doesn’t happen and that when it does, the culprits are punished. Regimes in place effectively attempt to prevent False Positives and as such, by this sole standard must be considered a success.
But what about False Negatives (Type 2 errors), when we exercise excess caution absent a major risk and as a consequence prevent insuring those in need? The result is personal financial catastrophe for those uninsured when the unthinkable happens — the death of a loved one. The numbers here, if they existed, would be damaging to consider.
We manage what we measure. Hence, a central problem arises in what we obsessively measure and in this case, fail to measure. So while we diligently log the number of complaints and abuses that occur, we currently have no adequate publicly available measurement of how many people should have purchased life insurance but were passed over.
Schools don’t provide sufficient financial literacy training and many people don’t know what they need to create personal financial stability for themselves and their families. In the mean time, industry incentive to serve lower income small accounts is decreasing as the cost of serving clients is increasing. This sadly large group in our society is not just one who warrants our protection on moral grounds, they are also the group who we should be most concerned are protected by private insurance in order to reduce financial burdens imposed on the state.
Families living just a modest distance from the border of poverty have little recourse but to seek government assistance when tragedy strikes. By contrast, although no one wants to be in the position of having to downgrade their home ownership, pull back on vacations, or say deprive their children of attending a more prestigious college, these are not primary concerns of the state in a free market system. As an issue for government to solve however, and specifically to avoid government having to pick up the proverbial tab for those who are not adequately insured, we need to embrace a wider view of reality. The family that lives “pay cheque to pay cheque” and then suffers not only the tragedy of the death of a loved one but is also no longer able to pay the rent, ought to represent a direct and enormous public policy concern.
How many families are there who could have avoided this scenario but find they now have to avail themselves of publicly funded services out there? Of course, we do not know. No mandate exists for policy-makers to measure this. No private life insurer reports on how their regulatory compliant business model is becoming a barrier to pursue lower income individuals for coverage. No regulator has a mandate to measure how many in the public are not insured. No measurement is taken on how many consumers are never provided a reasonable offer of life insurance coverage. In an era of Big Data ruling virtually every interaction and decision, it is curious why we don’t bother collecting any on this important front.
Regulator mandates and government mandates are not synonymous nor should they be confused with one another. While regulators are tasked with more narrowly protecting the public from wrong doing and bad advice, government has to look at public policy more broadly and therefore should feel a responsibility to ensure its citizens are afforded fair access to vital products such as life insurance. Both a low “False Positive” and a low “False Negative” rate on policy issues of importance should measure government’s effectiveness. A perfect policy environment would never let the public be mistreated due to poor regulatory standards while also never allowing somebody that would have greatly benefited from life insurance being systematically passed over.
The wealthy are well served in the area of financial advice and life insurance remains a vital part of that financial advisory mix. There are solid regulatory standards in place and many private services providers and advisors eager to serve them. By contrast, much of the middle class, and certainly the majority of families in lower socio-economic circumstances remain, sadly, under-advised and under-insured.
As a society, we share great fears of False Positives — in this case predatory marketing and sales leading to life insurance sales that are perhaps not based on an individual’s needs. We ought to be equally afraid of False Negatives. The asymmetry in public policy measurement and management needs to be addressed. Unintended consequences should not be confused with unanticipated ones. We all pay for False Negatives when families fall into poverty because their financial services needs have not been well served. The cost is unknown, tracking non-existent, but for the individuals affected, having no life insurance when tragedy strikes often means personal financial ruin. I believe starting a new public conversation is warranted: How many families are vulnerable to great financial hardship because they have no life insurance and how can we change this? It is time that we as the public, along with industry and public policy makers ask new questions. Solutions will follow.