I Lost My Job at Age 60! Can I Retire or Do I Have to Go Back to Work?

Not all of us retire when we plan. Sometimes the decision is made for us. Today we look at the situation of Hank and Becky. Hank was laid off right before his 60th birthday. Can they make it work, or will they be forced to find a job somewhere else?

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Meet Hank and Becky

Hank and Becky are both 60 years old, and just before Hank's 60th birthday, he lost his job. Now he is wondering if he can retire or if he will have to go back to work. Here are some basic facts.

Assets - Hank and Becky have roughly $350,000 in 401(k) accounts and $200,000 in cash-type assets not in retirement plans.
Expenses - Their estimated spending is $67,800 per year, $16,800 is for health insurance until they are eligible for Medicare.
Social Security - At age 62, Hank's benefit, is estimated to be $2,018 per month, and Becky's is estimated to be $835 per month.

Can they make it work, or will they need to go back to work? Let's take a look.

A Tough Situation...

Hank and Becky face an uphill climb, especially if they live longer than their statistical life expectancy. They can be confident that—without changes to their spending—their savings will last until age 82. But if they live longer, they face an increased risk of running out of money.

In fact they have a 22% chance their assets will last to age 90. To think of it another way, they have a 78% probability of running out of money by age 90.

So what can they do? Will they have to find another job? What about reducing some expenses? Let's take a look.

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Option 1: Adjust Their Lifestyle and Reduce Their Spending

The financial side of retirement is a complex, intertwined cash flow problem. If you are spending more than you can reasonably earn, it is going to put a lot of stress on your retirement assets. What happens to Hank and Becky if they reduce their spending by just $300 per month or $3,600 per year?

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One of the more interesting things financial planning can do is attempt to model the future. We use something called Monte Carlo Simulation. This allows us to evaluate 1,000 different lifetimes. There are a lot of variables factoring into the output, but it gives us a good idea as to whether or not you are taking on too much risk in your plans.

Here is the impact we see from reducing their expenditures by $300 per month. Their probability of success jumps from 22% to 77%. That means given the circumstances we know about today, they have a 23% chance of running out of money at age 90.

Our planning software tells us there is a better than 82% chance their assets will last until age 88.

Option 2: Going Back to Work

Going back to work may not seem like an ideal choice for either Hank or Becky at this point in their lives. But it could be a step they need to take to boost their success rate. This is especially true if the job they secure provides health insurance benefits. Remember they would be spending over $16,000 each year for this expense (and it WILL most likely go up each year).

The other aspect is that it will allow one or both of them to delay their Social Security benefits. Each year they delay their Social Security benefits shrinks the discounts they face to claim them early.

This paints a much better picture of their future retirement. Between reducing their expenses significantly, and delaying Social Security, they have improved their chances of success significantly.

The salary earned means they may not need to rely on their savings as much for a few years as well. The combination of these factors would help them significantly in the years ahead.

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Asking the What If Questions...

We all know the future is unpredictable. There will always be unexpected and unplanned events that impact our lives. One of the benefits of the financial planning process is being able to ask those What If type questions.

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What if Hank Dies at Age 73

There are dozes of questions you can ask, and this should be one of the common ones. What if Hank dies at 73? This would have a significant impact on Becky. It reduces the total amount of Social Security benefits received. It could also mean lower living expenses. It can also mean higher taxes.

What happens to Becky, in this instance?

Her probability of success is 75% at age 90. This is probably not as high as we prefer to see, but it is still reasonable.

All about the inputs...

This is a hypothetical case. Hank and Becky are not real people. But it also goes to show both the benefits and limitations of the planning process. The better information we have about our clients, the better the output. We also have to make some guesses about the future. Things like the rate of inflation, future returns, life expectancies, and more. We tend to lean towards a conservative if not pessimistic side of things. Real Life outcomes look a whole lot better (the blue area in the graphs above) when the actual results are better than what we plan.

Experience, Knowledge, and The ability to apply it...

Many firms have access to financial planning software (including the one we use). But where we set ourselves apart is with our knowledge of how to use it, our ability to understand the complexities of how money works within your life, and the experience to help you apply it to your life. If you would like to learn more about how we can help you. Fill out the form below.

Appearing in this video...

Julie Daley, RICP®

Julie is a financial advisor in St. Clairsville, Ohio

Vince McManus

Vince is a financial advisor in Parkersburg, West Virginia.

Neal Watson, CFP®

Neal is a financial advisor in Marietta, Ohio.

Disclaimer

This Monte Carlo analysis illustrates the potential results of your financial plan using up to 1000 randomly generated market returns and volatility called trial runs. In each trial run, the mean and standard deviation of a selected benchmark index for each account or portfolio is used for a randomly chosen year. This hypothetical investment performance is combined with the detailed cash flow and tax calculations for your plan. The trial runs produce a range of potential results and are one way of illustrating and evaluating the statistical probability of your planning strategies.

IMPORTANT: The projections generated by this Monte Carlo simulation regarding the likelihood of various investment outcomes do not reflect or guarantee future results and may vary with each use and over time. Calculations are based upon market index and growth rate assumptions in your plan. Refer to the Assumptions Summary and Monte Carlo Assumptions reports for details.

A Monte Carlo Analysis seeks to approximate actual investment market volatility by adding random investment returns to your financial plan. The result of introducing random investment volatility to the analysis produces a range of values that demonstrates how changing investment markets may impact your future plans.

This Monte Carlo simulation uses randomly selected return and volatility data of market indexes and applies cash flow and tax calculations based on the facts and assumptions you have provided to produce a trial run. The market indexes are assigned to investment accounts and portfolios to represent component asset classes. In each trial run, a rate of return is generated for each asset class using the mean and standard deviation of the market index in the randomly chosen year. Up to 1000 trial runs are calculated resulting in a range of values that is further analyzed to produce a statistical probability for your planning strategies. Carefully consider the high, low and average values in terms of how comfortable you would be with those results. Keep in mind it is impossible to predict future investment results and this analysis should be monitored over time.

Monte Carlo Assumptions

The following fixed growth rates were used in the simulation:

AssetPre-Retirement RatePost-Retirement RateEmergency fund0.50%0.50%

All other rates were varied statistically according to historical data.

Monte Carlo Definitions

Mean: Simple average, equal to the sum of all values divided by the number of values.
Maximum: The largest value of the distribution.
97.5 Percentile: The value of the distribution that 97.5% of the values fall below.
Median: The middle value of a distribution, above and below which lies an equal number of values.
2.5 Percentile: The value of the distribution that 2.5% of the values fall below.
Minimum: The smallest value of the distribution.
Monte Carlo Simulation: A statistical analysis model generally used to analyze the effect of varying inputs on the outputs of a model. The Monte Carlo simulation randomly applies values for uncertain variables over and over to simulate a model.
Standard Deviation: A statistical measure of the volatility based on the distribution of a set of data from its mean (average value). Example: A portfolio with an average return of 10% and a standard deviation of 15% would return a result between -5% and +25% the majority of the time (68% probability or 1 standard deviation), almost all the time the return would be between -20% and +40% (95% probability or twice the standard deviation). If there were 0 standard deviation then the result would always be 10%. Generally, more aggressive portfolios have a higher standard deviation and more conservative portfolios have a lower standard deviation.