Including Risk in a Case Study of When to Start Social Security Benefits

Case studies introduce students to real world analysis but searching out the data usually requires only reading the case. In contrast, choosing when to start collecting social security is real world—thus students can be required to search out data on social security and/or real people with financial, health, motivation, etc. details. The case statement can be as simple as: "Your grandparents or parents need advice." The many details and possible situations allow flexibility to match assignments to student maturity and course time constraints. Engineering economy and the time value of money are part of choosing when to start Social Security. Though benefits may begin in any given month, the exemplar choices are collecting reduced benefits as early as age 62, receiving a primary insurance amount (PIA) by waiting until full retirement age (currently 66, with increases beginning with those who born in 1955 or later), or delaying up to age 70 for enhanced payments. In an introductory course, a possible case statement would be: "Obviously the interest rate and the predicted age at death are both uncertain. Analyze choices for a variety of interest rates and expected lives. Present results so they make sense to someone who understands the meaning of present worth and/or rate of return, but who cannot calculate them." Many recipients of social security do not understand probability nor the time value of money. Yet risk can still be part of their decision-making. While this may be qualitative rather than quantitative, identifying specific situations and conflicting objectives is part of real-world applications of engineering economy. This paper summarizes viewpoints of risk that may drive the choice of when to start benefits. In advanced or demanding engineering economy courses mortality distributions can become part of the analysis. Mortality distributions can be supplied in the needed format of conditional probabilities, for example the P(dying at 83|alive at 66). Or students can be expected to convert mortality distributions to conditional distributions. Note that mortality distributions vary by gender and other differences. Or if students search out sources of mortality data, then credibility of different sources and consistency of results can be explored. Student searches for data may subdivide the population differently offering potential new viewpoints. Mortality distribution data supports the paired metric of expected value and standard deviation of benefits. Like investments higher returns and lower risks are preferred. Results for a single individual shows that differences in expected NPVs between starting ages are small, but delaying benefits significantly increases risk. This contradicts the conventional wisdom that delaying benefits is better. This case can be extended in masters or doctoral research into other defined benefit programs. It can also be extended to couples where there are two additional benefit types (spousal and survivor's) and many different combinations of ages and earning histories.