We live in a world of data. Data are the grist for the analytical mill. Data is a scary word to some, can be poorly collected, and can offer amazing insights. Analysis done well contributes to our knowledge. Analysis done poorly misuses data. Do you plant yourself in the camp of the data-phobic or the data-tranquil?
If you are the former, let today’s read be uplifting. I came across the most clearly written education on the topic of how any person can think effectively about data. Emily Oster, one of the major economic contributors to our world, Brown economist, and writer of ParentData, offers a tutorial for her non-statistical clientele.1
The data literacy principles offered by Emily Oster are fundamental to financial planning and personal finance. Here is my twist for 3 of the 4 lessons offered by Emily.
Data Literacy for Personal Finance Economics Readers
Where does the data come from? If you are interested in retirement and are advised to use the “4% rule,” does your advisor’s recommendation come from a sample of retirees where the best living standard and happiness are measured? If so, was 4% the best and most satisfying outcome? When I read personal finance articles in the popular press, I am curious to read the writer asking “the expert” such a question. It never happens. More broadly abused is the 10% savings recommendation. Financial institutions generated advisory services and wealth management. We need them. But, when you think about it, the 10% rule is a bit too self-serving. For instance, among a sample of households deeply in debt, have they been shown to be better off when they save 10%, too?
Correlation is not causality. Most academic studies on financial literacy and financial behaviors use correlation to assert a relationship. For instance, there is a correlation between lower levels of financial literacy and certain ethnic groups. One could never argue intellectually that being a member of a specific ethnic group causes a higher or lower level of financial literacy. Similarly, there may be a negative correlation between higher levels of payday loans and lower levels of financial literacy, but that doesn’t mean raising the level of financial literacy will cause less use of payday loans.2 Statistical associations are informative, but public policy decisions about financial literacy education need to be justified with care for causation when tax resources are at stake.
Be Bayesian. Financial planning writers and advisors always come up with a new special sauce for investments. Flavor with our natural affinity for greed, and the sellers of the next great investment idea meet a receptive audience. Emily O. asserts that to be like Bayes is to keep the record in mind. Modern portfolio theory exists today in practice for a reason. Harry Markowitz won the Nobel Prize for this work. The investment literature is robust, with studies that support the impossibility of consistently beating the market using information based on public data. If your advisor calls with an investment idea, don’t discount what we know about earning an abnormal investment return for the risk we bear. Listen and make a more informed decision.
Be Skeptical. This last point is my add-in. If you are a customer, don’t be afraid to ask a financial advisor questions and require justification for their answers if you are left wondering about their recommendations. Experienced financial planners are pros who will respect the exchange, and a better outcome will result. Unless the financial question is solely legal, the answer should be an economic interpretation that evaluates the trade-offs among viable solutions or against those pesky and outdated rules of thumb. If you are on the financial planning side, be prepared to defend your guidance. In your practice, bias against loose rules of thumb.
Emily’s Oster’s Post
A link is included in the panel just above.
Disney, R., & Gathergood, J. (2013). Financial literacy and consumer credit portfolios. Journal of Banking & Finance, 37(7), 2246-2254.