Baltimore , Maryland
June 25, 2023
June 25, 2023
June 28, 2023
Engineering Economy Division (EED)
Diversity
10
10.18260/1-2--43186
https://peer.asee.org/43186
183
NEAL A. LEWIS, CPEM, received his Ph.D. in engineering management in 2004 and B.S. in chemical engineering in 1974 from the University of Missouri–Rolla and his MBA in 2000 from the University of New Haven. He has over 25 years of industrial experience at Procter & Gamble and Bayer. He is a full time faculty member of the online Master of Engineering Management program at the University of Nebraska - Lincoln. Previously, he taught at UMR, Marshall University, University of Bridgeport, University of New Haven, Fairfield University, and Oregon State University. He has over 100 publications and presentations, including 3 books, 6 best paper awards at conferences, the 2009 Grant award (TEE), and the 2005 Eschenbach award (EMJ). Neal is a Fellow of ASEM.
Dr. Ted Eschenbach, PE, is the principal of TGE Consulting, an emeritus professor at the University of Alaska Anchorage, and EMJ's founding editor emeritus. He is a Fellow of ASEE, ASEM, and IISE. His Ph.D in IE is from Stanford and his MCE from UAA. He has over 300 publications and presentations, including 21 editions of 4 engineering economy titles, 8 best paper awards at conferences, and the 2009 Grant award. In 2016 he received ASEE’s biannual National Engineering Economy Teaching Excellence Award.
Diversity, equity, and inclusion can be difficult to incorporate into an engineering economy course. However, there are products and services where diversity and demographics are directly linked with personal finance and economic equity. Engineering economy courses can cover useful qualitative perspectives for life, vehicle, and medical insurance. Engineering economy tools are required for investing for retirement and retirement planning.
Mortality data from the National Vital Statistics System (NVSS) show large differences in the “expectation of life at age x.” The tabulated value at birth for a male American Indian or Alaska Native (AIAN) is 63.8 years and for a female Asian it is 22.1 years longer at 85.9 years. At age 22 the gap between the same two groups is still 21.1 years. By age 67 (full retirement age for Social Security) the gap is 8.2 years, but the comparison is between the female Asian and Black male groups. These differences are substantial, of major social importance, and highly relevant for personal financial planning by students—now and as retirement nears.
The most recent NVSS data is available in 10 sub-categories—2 genders × 5 ethnic groups with aggregated totals. While ethnic group data cannot be used in setting rates or benefits, it is relevant to personal decision-making. Gender is legally considered for many types of insurance, annuities, and social security benefit determinations. Thus, averages for both genders and for the total population underlie discussions of how much should be saved, what costs will be, and when social security benefits should be started.
We assert that engineering economy courses should include the use of relevant demographic based information for personal financial decision making. Students will begin making financial decisions regarding insurance, investing, and retirement planning soon after entering the workplace, if they haven’t already. People need to understand how they compare to the “average” that is used to determine costs and benefits.
How long you expect to live is clearly part of planning how much to save for retirement and even when a person plans to retire. An average age at death for a student’s gender and ethnicity is only a rough estimate of a very uncertain event hopefully many decades away—but it is a starting point for planning. If a student plans for a 20-year retirement, then the investing period is average age at death minus a 20-year retirement minus their current age. If a person plans to retire at 67, then the average age at death determines the horizon for drawing down the investment.
For introductory courses the mortality distribution should be simplified to an average age at death (used in this paper). For more advanced courses the mortality distributions can be used to calculate expected values and standard deviations for retirement benefits (references provided). At either level demographic differences can be discussed as a proxy for differences due to a student’s heredity, health, or lifestyle.
Lewis, N. A., & Eschenbach, T. (2023, June), Diversity and Equity as Part of Personal Decision-Making Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--43186
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