Career Move
This exercise asks a manager to compare a San Francisco job offer with staying in Indiana using weighted career and family outcomes. The author reports preferring to stay, but the solution and its utility assignments are absent; the stated probabilities alone do not support that conclusion.
THE IDEA
EXERCISE 49 Career Move
You are a midlevel industrial manager who has just been offered an executive position in San Francisco with a significant increase in salary and related perquisites. The problem is that you reside in a small town in Indiana and commute daily to your present job in Indianapolis. The move to San Francisco will uproot your family. Your spouse is a Ph.D. biologist with a full-time career position in a pathology laboratory five miles from your home. Your two teenage children are in the ninth and tenth grades and fully engaged in high school academics, sports, and leadership activities. They have lived their entire lives in this one town and this one home. Your spouse, who has misgivings about forfeiting hard-won opportunities for advancement in the pathology lab, has agreed nonetheless to move to San Francisco if that’s what you want, the two of you having faithfully supported each other’s professional ambitions throughout your marriage of eighteen years.
You determine that your decision to accept or not accept the job offer rests on three factors (three classes of outcomes) which you have weighted with their importance:
- Career advancement potential of the new job (.4). There are three outcomes: worse than, same as, better than the potential of your current position.
- Potential for your spouse’s career advancement in San Francisco (.3). There are three outcomes: worse than, same as, better than the potential of your spouse’s current position.
- Your children’s intellectual and social development through high school activities that enhance their academic standing, self-assurance, leadership skills, physical fitness, and overall maturity (.3). There are three outcomes: worse than, same as, better than the development possible where you now live.
You estimate that, if you move to San Francisco, the probabilities of your career potential will be 0 worse, 0 same, and 1.0 better; if you stay in Indiana, the probabilities will be .1 worse, .8 same, .1 better. Whether you move or not, the probabilities of your spouse’s career potential and the children’s development will be .1 worse, .8 same, and .1 better.
Using utility analysis with multiple classes of outcomes, analyze the problem and determine which of the two options —accepting the San Francisco job or rejecting it—is preferable. Assign utility values as you see fit.
BEFORE CONTINUING, WORK THE PROBLEM.
The author reports that the solution to Exercise 49 (page 344) favors declining the offer. That solution and its utility assignments are not included here. With the same utilities for corresponding outcomes, and better career prospects valued above worse or unchanged prospects, the probabilities given favor moving. A preference to stay would require additional differences in how the outcomes are valued or a change to the stated assumptions.
When to use it
Evaluating job offers that involve relocation and family considerations.
Making decisions with multiple conflicting objectives, such as balancing professional growth and personal life.
Applying utility theory to complex personal or business choices where outcomes have different probabilities and weights.
Teaching multi-attribute utility analysis in decision science or management courses.
FROM THEORY TO EVERYDAY LIFE
In practice
Job Relocation Decision
A manager must choose between a high-paying executive job in San Francisco and staying in Indiana, weighing career advancement (weight .4), spouse's career potential (weight .3), and children's development (weight .3). Probabilities for career advancement if moving: 1.0 better; if staying: 0.1 worse, 0.8 same, 0.1 better. For spouse and children, probabilities are identical regardless of move: 0.1 worse, 0.8 same, 0.1 better. The author reports preferring to decline, but the calculation is not supplied. If the same outcome utilities apply to both options and better prospects are preferred, these probabilities favor moving.
A MOMENT TO REFLECT
Put it to work.
How would you assign utility values to the outcomes in this exercise? What factors influence your assignment?
If the probabilities for your spouse's career were more favorable in San Francisco (e.g., 0.5 better), would that change your decision?
What other factors might be important in such a career move decision, and how would you incorporate them into the analysis?
CONTINUE THE CONVERSATION
Source texts
- The Thinkers Toolkit by Morgan D Jones