When to Worry, When to Wing It: Withdrawal Rate Case Studies – SWR Series Part 47

August 18, 2021

In my post two weeks ago I outlined my approach to retirement planning: In light of significant uncertainty in retirement, I like to do a more careful, robust, and scientific analysis. Not because I could ever undo any of the existing uncertainties but because I don’t want to add even more uncertainties through “winging it” in retirement.

But how much detail is really required? I can already hear objections like “you can never know your future spending month-by-month, so why go through all this careful analysis with a monthly withdrawal frequency?” To which I like to answer: Well, maybe that’s the part where you can indeed use the “wing it” approach! So, today I want to go through a few case studies and learn how much of a difference it would make in my safe withdrawal strategy simulations if we a) carefully model the whole shebang in great detail, or b) just wing it and use a rough average estimate for the spending path. For example…

  • Does the intra-year distribution of withdrawals matter? In other words, how much of a difference does the withdrawal frequency make: monthly vs. quarterly vs. annual?
  • What if there are fluctuations in my annual withdrawals around the baseline average budget, due to home repairs, health expenses, etc.?
  • What if those fluctuations have an upward bias?
  • What if there is a slow (upward) creep in withdrawals?
  • What about nursing home expenses later in retirement?

Where can I safely wing it? And which are the ones I should worry about? Let’s take a look…

1: Intra-year fluctuations in withdrawals? Wing it!

In my withdrawal rate simulations, I always assume that you a) withdraw monthly and b) have a flat withdrawal profile in every year. That’s a slight deviation from the Trinity Study. They only have annual return data, hence, they are bound to make their model annual and thus the withdrawals have to be annual as well: at the beginning of each year, to be precise. I don’t quite get why anyone would do that, i.e., withdraw an entire year’s worth of retirement spending all in one big chunk and then slowly draw that down over the next 12 months, but so be it. Though I can also poke a few holes into my assumption: What if your withdrawals aren’t exactly flat throughout the year? People might spend more than one-twelfth of their annual budget during the summer months and then again during the holiday traveling season. And people might have large “chunky” expenses during specific months due to property tax bills and/or estimated federal income tax payments in January, April, June, and September.

So, this raises the question: How much of a difference does it make if we alter not the total annual withdrawals every year but the distribution of the spending and thus the withdrawal amounts intra-year? Let’s take a look at the following three scenarios with a 4% withdrawal rate, 75% Stock, 25% Bond portfolio over 30 years:

  1. Baseline: 12 equal monthly withdrawals of 0.333% of the initial balance (adjusted for inflation) at the beginning of each month. The standard 4% Rule simulation in my toolkit.
  2. Quarterly withdrawals: only 4 annual withdrawals at the beginning of month 1, 4, 7, 10, 13, 16, 19, 22,…, 355, 358, worth 1% of the initial balance (adjusted for inflation) and zero withdrawals in all the other months.
  3. Annual withdrawals: Starting in month 1 and then on each retirement annivesary, withdraw 4% of the initial balance (adjusted for inflation). Zero withdrawals in all the other months.
  4. Lumpy Monthly withdrawals: Imagine we start with a $2m portfolio and target a 4% withdrawal rate, i.e., $80k per year. Instead of withdrawing exactly $6,666.67 per month, assume we withdraw $5,000 in most months (months 1, 3, 4, 6, 7, 9, 10, 12) but then you also have some lumpy expenses (summer travel, holiday expenses, estimated tax payments, etc.) amounting to $10k, $12k, $8k, $10k in months 2, 5, 8, and 11, respectively. See the bar chart below. So, we still withdraw monthly but in installments of between 0.75x to 1.80x the monthly average. Quite a bit of spending volatility: a standard deviation of about $2,600 (levels) or 67% (month/month percentage changes).
Monthly Withdrawals with a $5,000 base and some occasional spending shocks. ($80k per year = 4% out of a $2m portfolio)

In the spirit of the post last week, SWR Series Part 46, let’s see how the final distribution after 30 years differs using the three withdrawal frequency assumptions. See the chart below. Again, we note that there is significant uncertainty over your final net worth balance and there is nothing we can really do about that. But the good news is that the final distribution looks the same regardless of the withdrawal frequency. There are some slight changes in the extreme tail probabilities, i.e., the probability of running out of money slowly rises from 1.5% to 1.73% to 2.42% when we go from monthly to quarterly to annual withdrawals. But for the most part, the distributions look the same. The “Lumpy Monthly” scenario is even closer to the baseline and even has a slightly lower failure probability.

Histogram (distribution) of final CPI-adjusted net worth: 75/25 portfolio, 30 years of 4% annual withdrawal rate. The intra-year withdrawal pattern doesn’t have much of an impact!

In the table below, I also assemble some more stats. The top half of the table again has some of the same data as in the histogram charts. But in the lower half, I also like to condition on expensive equity valuations (as in today), i.e., what are the SWR stats conditional on a CAPE>20 and the S&P 500 at an all-time high. The model here again assumes a 30-year horizon and a 25% target for the final portfolio when calculating the failure probabilities and failsafe withdrawal rates. If you’re a regular reader, you may recall that this is a nice universally useful assumption to capture both traditional retirees who have a 30-year horizon with a 25% final value target for a bequest but are also applicable to an early retiree who has to bridge 30 years until Social Security and company pensions kick in. In that case, the 25% final value target is to supplement the pension and government program.

The failure probabilities of the 4% Rule under that scenario are at about 22.5% for all scenarios. The failsafe withdrawal rate goes from 3.584% to 3.581% (quarterly) to 3.504% (annual) and 3.585% (monthly with fluctuations). And I should mention it here again, the reason why I display the SWR/fail-safe with so many digits is that because I like to showcase how little of a difference the withdrawal frequency makes. I don’t advocate pinning down your personal SWR to the closest 0.001%. That would be down to a $20 precision in the annual budget for a $2m portfolio. Not a good idea! Probably it’s ideal if you round your budget to the nearest $1,000/year = 0.05% of a $2m portfolio.

SWR Stats: Even conditioning on expensive equity valuation, the withdrawal frequency doesn’t change the results much!

A quick side note: Why is it that by shifting to annual withdrawals, you get a slight deterioration in the failsafe rate? The earlier you withdraw the funds the less time they have to accumulate. Thus, on average, you should get better growth of your portfolio by withdrawing small sums more frequently. Hence, the deterioration in the average and median final net worth numbers when you reduce the frequency of withdrawals.

But how about the tail events? That can go either way. I looked at the more detailed results (not displayed here for brevity) and I noticed that during the Great Depression, you could have benefitted(!) from annual withdrawals because you withdrew a bigger chunk from the portfolio right before the 1929 crash. On the other hand, annual withdrawals would have hurt the 1965 retirement cohort because early on you still had moderate asset growth until the 1973 meltdown. The net effect of annual withdrawals mixing together the 1929 and 1965, 1966, and 1968 cohorts was slightly negative.

2: Fluctuations in annual withdrawals? Wing it!

Some critics have pointed out – correctly – that your spending will likely not be flat over the years. If you are a homeowner you will likely agree that home repairs and renovations are not spread out exactly uniformly over the years. Same with cars and other high-valued items; you may have to repair or even replace your car in one year and then have no major car expenses the next. Does that mean that my retirement withdrawal simulations are all moot? Well, let’s put that to the test.

Let’s compare the following scenarios:

  1. Baseline as before: Monthly withdrawals, flat throughout the entire 30-year retirement. For example, if you have a $2m portfolio and a 4% withdrawal rate, you’d withdraw $80k every year. And that’s withdrawn monthly, in $6,666.67 installments.
  2. Withdrawals are still monthly and level in every year, but they are 25% higher in odd years (including year 1) and 25% lower than the baseline in even years. Thus, the withdrawals jump back and forth between $100k in odd years and $60k in even years. That’s a quite a bit of fluctuation in annual withdrawals, by the way, -40% and +67% annual changes!
  3. The same as before but we reverse the pattern: -25% in odd years (including the first year) and +25% in even years. So, to use the same example above again, this is someone who withdraws $80k per year on average, but will do so by withdrawing $60k in odd and $100k in even years.

Let’s see how the results shape up under the three scenarios. Below is the same table format. True, by frontloading your withdrawals (+25%/-25%) you get a slightly lower final net worth, and by starting with the lower of the two withdrawals (-25%/+25%) you get a slightly larger final net worth than under the baseline. But the differences are not really that large, considering the significant spending variability (+40%/-67%). Conditional on high equity valuations, the results are also almost identical. The fail-safe rates are all very close to each other, around 3.58%. The failsafe amounts would be about 0.7% lower or higher depending on whether we start with the high or low spending shock in the first year. Not really anything to sweat.

SWR Stats with fluctuations in annual spending. Not much of a difference!

Also notice that, if we don’t really know ex-ante whether we will get the high or low spending shock in the first year and we model this as flipping a coin determining the first-year spending shock, we’d average over the two scenarios +25%/-25% and -25%/+25% and that average will land smack back at the baseline outcome. See the table below:

If a coin flip determines the initial spending level, the aggregate stats are essentially back to the baseline. Note that the fail-safe remains at the +25%/-25% level but the failure probabilities are also almost identical to the baseline!

Thus, even sizable annual spending variability doesn’t appear to make much of a difference in withdrawal stats, as long as the fluctuations are centered around a baseline mean. Get your baseline budget right and beyond that, be my guest, just wing it!

Summary so far

So, from scenarios 1 and 2, what have I learned? Why don’t we see a much larger impact from deviating from the flat spending assumption? Very simple: Retirement success or failure is due to three major causes: First, Sequence of Return Risk. Second, Sequence of Return Risk. And third, Sequence of Return Risk. No, but seriously, if you recall from Part 14 and Part 15 of this series, Sequence Risk comes from an extended and deep drawdown in your portfolio which causes you to withdraw relatively more shares while the portfolio is down. You’d then draw down the portfolio so much that even an eventual recovery can not rescue your underwater nest egg. Specifically, during some of the historical bear markets, equities occasionally took more than a decade to recover in real terms (see my 2019 post “Who’s afraid of a Bear Market” for more fun facts). If the market is down for that many years, it doesn’t really matter so much how you distribute and spread out your withdrawals month after month. If the frequency of withdrawal fluctuations is much faster than the major market cycles, then spending fluctuations don’t have too much of an impact on Sequence Risk, if the average spending is roughly your annual budget.

But what if we didn’t get our baseline budget right? That brings me to the next case study…

3: Fluctuations with an upward bias? Worry!

Let’s keep the annual fluctuations from the previous case study with the following twist: Instead of fluctuating +/-25% assume that in odd years we hit our budget and withdraw the target, e.g., $80k if we started with a $2m portfolio. But in the even years, we overshoot and withdraw $100k. Think of this as a retiree who calibrated his or her budget to the best-case scenario where you have no surprise expenses. But then every second year, in the even years, reality catches up with you and “sh!t” happens, i.e., you have home repairs, you have much higher travel expenses because you attend your third cousin’s wedding, you have health expenditures, you replace your car, you upgrade your home, etc. The possibilities are endless.

That’s easy to simulate. I simply run the same sims as above but alternate between 0% and +25% spending shocks. Otherwise, the assumptions are exactly the same as in case study 2. The simulation results look a bit worrisome. You will shift the final net worth distribution significantly to the left. Not more “uncertainty” if measured as the standard deviation, but more risk of running out of money. The unconditional failure probability goes up from 1.5% to over 9%. The safe withdrawal amount conditional on expensive equities shrinks by 10%. Or, if keeping the withdrawal rate the same you would exacerbate your failure probabilities. This is something to worry about!

SWR Stats when fluctuations have an upward bias: Now, that’s something to worry about!

4: Retirement Spending Creep? Worry!

Another retirement variation and idiosyncrasy: what if your withdrawals creep up slowly, i.e., spending grows at a rate faster than inflation? The reasons for this spending creep are endless. Here are a few:

  1. You’re “keeping up with the Joneses”: The average U.S. resident increases spending not just in line with inflation but also in line with average per capita GDP. That’s easily 1-2% annually. If you retire and commit to spending only your initial budget plus CPI inflation every year, keep in mind that your neighbors, friends and relatives around you are advancing their budget every year and you will fall behind. If you’re fine with that, more power to you. But not everyone wants to fall behind by 35% or 81% respectively when their peers are raising their standard of living by 1% and 2% per annum, respectively.
  2. Your personal inflation rate is higher: Recall that the U.S. CPI measures the price index of an average consumption basket for an average urban consumer. Your personal rate can differ wildly. Just taking a look at all the different subcomponents of the CPI and how much variation there is in the cross-section raises the issue that if your basket is overweight on the higher inflation items (health care, rent, etc.) and underweights the low-inflation items (electronics) then your personal CPI might increase much faster than the overall CPI. So forget about “Keeping up with the Joneses” – merely keeping up with your own basket might necessitate raising your withdrawals faster than the CPI!
  3. You no longer want an iPhone3: Sometimes people ask me whether the CPI makes adjustments for advances in product quality. The answer is, yes, definitely. And that can pose a problem. Imagine the average mobile phone becomes 30% cheaper every year. If you bought a phone for $600 two years ago, then it should cost around $294 today. But if your phone breaks you would likely not buy that same phone again. Instead you will likely buy a new state-of-the-art phone, costing maybe even more that the old one two years ago, say, $700. So, while the mobile phone category in the CPI might have a 30% annualized price drop, your out-of-pocket expenses for this category is up 8% per year. And again, the difference between the CPI and your personal spending changes comes from the fact the agency that constructs the CPI number (Bureau of Labor Statistics), does not compare the average mobile phone price in 2021 with the average phone price last year. It compares phones with identical charactestics across time! So, in other words, this is a form of “keeping up with the Tim Cookses”, i.e., raising your real spending because you buy better and better electronic devices.

So, let’s see what happens if we raise our real spending by 1% or 2% a year. That certainly has an impact on your withdrawal states. The unconditional failure probability of the 4% Rule goes up from 1.5% to 7.7% to almost 19%, when raising the withdrawals by 1% and 2% a year, respectively. Conditional on expensive equity valuations you go from 22.5% to 49% to 58%. Ouch! The failsafe initial withdrawal amount goes from about 3.6% to 3.2% and 2.85%, respectively. That’s a 10.6% and 20.4% drop, respectively, from the baseline with a flat spending profile. Spending creep is definitely something we cannot ignore! I certainly worry about that in my personal retirement planning!

5: Nursing Home Expenses? Wing it (mostly)!

Do you remember the Paula Pant podcast with Suze Orman? In a nutshell, Suze Orman proposes that you’d need at least $5 million or even better $10 million to hedge against all the uncertainties in retirement, including the possibility of nursing home expenses; up to $350k a year, her claim, not mine. My reply: When I retired at age 44, nursing home expenses were the last problem on my mind. It’s not like I said my Goodbye’s at the office on June 1, 2018, and then moved into a nursing home that same afternoon. I’d like to delay that move for at least another 40 years! Nursing home expenses most often hit retirees in their 80s or older. Irrelevant for at least another 30+ years for me and 40+ years for my wife.

But that doesn’t mean that we can completely ignore nursing home expenses. A large enough expense shock occurring decades into your retirement can still jeopardize your retirement. It all depends on how large the shock is and how many years into the future we’re talking about! Here are the scenarios I like to study:

  1. Baseline: a 50-year retirement horizon with flat expenses and no nursing home stay. I assume a 75/25 asset allocation as before but a zero final net worth. One can think of this as a 40-year old early retiree who wants to ensure the money lasts until age 90.
  2. A nursing home stay during the last 3 years of retirement (ages 87-90) with an additional annual cost worth 1/20 of the initial portfolio value. So, for example, if you start with a $2m initial portfolio we assume that the nursing home adds $100,000 in annual withdrawals on top of the baseline withdrawals. So, if you assume a 3.5% withdrawal rate from a $2m portfolio and thus $70,000 annual withdrawals, we would jack up the withdrawals to $170,000 during the last three years. That’s a pretty decent budget – not as much as the $350,000 annually for Suze Orman’s Ritz Carlton nursing home, but still quite generous!
  3. A nuring home stay as before, but we assume that the expenses span the last ten (!) years of the investment horizon.
  4. The same scenario as before, i.e., the nursing home stay begins in year 41, but only lasts yntil the end of year 43 and the retiree passes away after that. The idea here is that in most cases, a nursing home sty doesn’t last much longer than 3 years. Sure, an 80-year old may have an unconditional life expetancy of 10 years, but conditional on moving into a nursing home, that life expectancy usually melts down significantly. According to this source, most nursing home stays are 3 years or less. Thanks to Fritz at The Retirement Manifesto for the link!
Percentages of elderly requiring nursing home care of various durations. Source: Elder Law Answers, “How Likely Are You to Need Long-Term Care?”, February 24th, 2016

Let’s take a look at the results. A nursing home stay in the final three years of your 50-year retirement horizon has a pretty negligible impact on your final net worth distribution. No surprise here. The unconditional failure probability of the 4% rule goes up from 1% to 2%. The market-peak conditional SWR stats slightly deteriorate with a failsafe withdrawal amount lower by 2.57%. In contrast, spending the entire final decade in the nursing home is certainly very impactful. The failsafe withdrawal amount is almost 10% lower than in the baseline. But a stay in the facility for a full ten years is also quite unlikely. Conditional on entering the facility in year 41, you probably also face a much lower life expectancy. If you live for only another 3 years, you have pretty much the same stats as in the baseline. In that sense, we can certainly “wing it” with respect to the nursing home expense risk.

SWR Stats with different nursing home scenarios. The only noticeable difference is apparent in the 10-year nursing home scenario at the end of the retirement.

Side note: Though it’s the first time I write about the nursing home issue in the context of the SWR Series, I have briefly looked at some simulations in a 2018 post in response to the Suze appearance on the Afford Anything podcast. Similar results: Not much of a difference in safe withdrawal rates especially if you account for the fact that ending up in the nursing home also likely lowers your life expectancy.

Bonus: Boredom in Retirement? Wing it!

Before I wrap up today’s post, I like to reiterate something I’ve pointed out numerous times before. But since not every reader has read all posts and listened to all the podcasts I’ve done over the years, here it goes again: when I announced my plan for retirement, especially early retirement, people inevitably asked me if might get bored. Well, that is one retirement uncertainty where I was willing to “wing it”. And I have so far. We traveled very extensively in 2018 and 2019. Never came even close to feeling bored then. Even with the recent travel restrictions and being cooped up at home, I never exactly felt bored. But I noticed that I have some extra bandwidth. Even back in 2018 when I retired, I knew that this would happen. But I had no idea what exactly I would want to do about it and I never really planned for it. I just winged it. And sure enough, starting in 2021 I started pursuing some other projects I’m passionate about. Winging the boredom part is much easier than winging an underwater portfolio!

Conclusion

There you have it. The retirement planning lesson for today: Never Say Never Wing It!

Specifically, some of our idiosyncrasies can be safely ignored, such as normal spending fluctuations (case studies 1 & 2). But getting your baseline budget right might be something you want to spend some time on. Fudging the numbers here and basing your average budget on the best-case scenario, i.e., lowest possible spending and not factoring in unexpected budget busters like home repairs, might be a recipe for failure (case study 3).

Another major headache is the issue of spending creep as in case study 4. Hey, it’s just a percent of spending creep per year, how much of a difference in the failsafe can that make? Well, it would be wise to lower the initial retirement budget by over 10%. And a 20% cut if you plan spending increases by 2% over inflation. And this is simulated over a 30-year horizon. Longer horizons would demand an even more drastic cut.

Somewhere in between is the nursing home scenario, case study 4. What makes the nursing home expense much less of an issue is that it’s the antithesis of Sequence Risk; it’s too far in the future to really matter. For early retirees with several decades before that big-ticket item might hit you, you can safely wing it today. The average traditional retiree might want to worry a little bit more but we still use the power of compounding to deal with this issue.

Thanks for stopping by today! Please leave your comments and suggestions below! Also, make sure you check out the other parts of the series, see here for a guide to the different parts so far!

Title picture credit: Pixabay.com

Technical Appendix

For those interested in replicating the results here, how would I simulate quarterly withdrawals in the SWR Simulation Google Sheet? Or any of the other deviations from flat withdrawal patterns? Very simple, here’s the trick.

First, go to the tab “Cash Flow Assist” in the Google SWR Sheet. Then set the scaling, which is set to 1.00 in the baseline (hence, a flat spending path!) to the pattern you like to model. For quarterly withdrawals set the scaling to 3.00, 0.00, 0.00, 3.00, 0.00, 0.00, 3.00, …

Modeling quarterly withdrawals in the Google Sheet!

For annual: Set the scaling to 12.00 in months 1, 13, 25,… and zero in all other months.

For the -25%/+25% annual spending pattern, set the scaling to 0.75 in odd calendar years (= months 1-12, 25-36, 49-60,…) and to 1.25 in even calendar years (= months 13-24, 37-48, 61-72,…):

Modeling the low/high alternating spending patterns in the Google Sheet.

And likewise for the spending creep: Simply start with a scaling of 1.00 and then aply geometric growth to the scaling factors by 1% and 2%, respectively.

73 thoughts on “When to Worry, When to Wing It: Withdrawal Rate Case Studies – SWR Series Part 47

  1. The proportions may not be exact, but I’m sure the 80:20 rule can provide a steer on how much time to spend dialling in your planning around a SWR. Spend no time planning and you’re adding uncertainty to your plans.

    As you note, spend your time coming up with a good middle of the road budget for year one is a good use of time. But spending hours trying to come up with accurate individual spending plans for each of the next 10 years would be overkill.

    1. Yes, that’s a good summary. On certain issues it’s closer to a maybe 99/5, i.e., you get 99% of the result for 5% of the effort.
      But it’s important to know where those shortcuts are and where to work a bit harder. Hence, this post. 🙂
      Thanks for stopping by!

  2. Hi Karsten, really interesting post and very helpful to understand how sensitive the safe withdrawal rate is to some of these uncertainties, particularly around varying spending patterns. I have really enjoyed reading your safe withdrawal rate series. One question I have is how much of a difference a variable withdrawal rate makes across retirement and the extent to which spending more earlier increases the sequence of returns risk? Many retirees will spend more to begin with and less later on as they become less active. If the same total amount is being spent over the entire retirement period (before adjusting for inflation) how does the front loading of spending effect the sequence of return risks?

    An example might help to explain. I have a stepped spending forecast reducing in 4 steps. My forecasted annual spend for the first 8 years to age 60 is 4.3% of the portfolio to allow for extra travel costs and assuming we keep our current house, the next 8 years reduces to 3.5% to reflect downsizing the house and removing the remaining mortgage and lower property costs. The next 8 years drops to 2.3% as state and db pensions kick in and then finally from 75 to 95 it drops to 1.9% once travel costs are removed. All rates are based on spending in today’s money terms. The weighted average of the withdrawal rate across the entire assumed retirement of 44 years is 2.7%. How would a stepped spending pattern like this one compare to a flat spending pattern of just the average withdrawal of 2.7% in each and every year (in today’s money terms). The total money being spent is the same in both scenarios, it just that it is front loaded in the stepped example. Apologies if you’ve covered this before in your series, but I had a look over the intro / summary and couldn’t see an example like this. Thanks for all your do. I have learned a lot from your work.

    1. Good point. You can model such a pattern of expenses in the Google Sheet through playing with the scaling parameter and the supplemental cash flows.
      You;d have to punch the numbers in yourself but just eyeballing it this seems like a ultra-safe withdrawal plan.
      Normally, people can easily go to 5%+, sometimes 6%+ early on when the withdrawals drop by that much later in life. See one such case study here:
      https://earlyretirementnow.com/2019/11/08/safe-withdrawal-rate-case-study-for-becky-and-stephen/

      1. Similar sentiment. The “smile curve” for retiree expenditures more closely resembles actual spend patterns. A Morningstar paper does a nice job of describing this (link below) and includes analysis that actually use “smile curve” expenditure assumptions (they even have an equation). I haven’t seen any sensitivity analysis on how different smile curves impact withdrawals nor am I aware of any calculators that estimate retirement expenses in this fashion. Any thoughts on how to go about framing up the sensistivity analysis beyond just plugging in numbers by eyeball?

        https://www.morningstar.com/content/dam/marketing/shared/research/foundational/677785-EstimatingTrueCostRetirement.pdf

        1. Yes, saw that paper before. I looked at the smile curves (Figure 7, left side) and it looks like the lines (Y/Y changes) are completely inconsistent with the lines on the right side (levels, normalized to 100 at age 65 ).
          The blue line on the left is >0% for the first 10 years, yet the level is stagnating and then falling. Not sure what to make of that.

          Either way, I agree with some of the findings. It sounds like people with a lot of assets and a generous initial budget indeed cut their spending over time. They will scale back voluntarily as they age.
          People with a lot of initial budget and not a lot of assets also cut expenses. They have to cut spending.

          But I doubt all of this research has much bearing on the average early retiree. If you retire at age 30 with a $25k annual Mr. MM budget, I seriously doubt that people will cut that much after age 65. Chances are you need more when you age.

    2. I would love to see how David’s scenario play out as I would like to be able to do something similar. Really enjoy this post and even look at it with my husband who really don’t like finance. Thank you for all your work and for your google sheet that was really helpful for me.

        1. FWIW – I really enjoyed the scenarios you did of peoples retirements. Wish you would do more of them.

  3. I feel like a pro but I learned something today! I’d like to have some flexibility to keep up w/ the Joneses occasionally. So I guess my portfolio doubling means I can afford a 1 or 2% boost to annual spending if I want it. So far so good – haven’t even given myself an inflation boost even after 8 years! But it’s coming, I can feel it!

    I can see this in practice. I live in a 1970’s home. Today’s 2021 home has a lot more fancy stuff in it vs my old fashioned home. But today’s home is just considered normal. Also going back in time to 1970, we all had 1 car per family, no computers, no cell phones, 1 TV (okay maybe 1.3 TVs by 1970 😉 ). Nobody was eating sashimi weekly and getting lattes and boba teas. I wouldn’t want to reduce my standard of living to the 1970’s, so I can assume that future me won’t want to maintain my 2021 lifestyle forever. Eventually I’ll want a VR headset, a fancy new phone, a self-driving car, etc.

    One way I’ve addressed this is by intentionally underspending the max potential spend I could make. So my portfolio has more than doubled but I’m not spending the surplus today. Nice to have optionality in the future!

    1. Hi Justin! Yes, that’s what I noticed in your spending stats (by the way, the only long-term consistent spending stats I know of). Quite intriguing that it’s flat to even slightly down. I guess it’s a combination of spending more efficiently and spending in categories that don’t have very steep CPI.
      Not sure everyone on the FIRE community can do that! 🙂
      But you’re right, with the flat spending and a ballooning portfolio you can certainly splurge in the future!

    2. Less is more – the new trend (in some groups) is anti-consumption & environmental awareness so you’re just ahead of the times.

  4. Great post as always, thanks for the analysis.

    I’ll add that for item 3 and 4, this is a reason against super early FIRE IMO. I started looking at all this about 8 years ago. Since then we had two kids, which grew our spending. Some things I could plan for, some I couldn’t. At first it was frustrating to see the goal posts be moved out, my original spending assumptions grew by 50%! Fortunately my income also grew and in time we reached a new plateau, which has been steady for four years more or less.

    My previous experience has pushed me to be more conservative, and I’ve aimed at 3% as a starting point. It was good that I didn’t rush FIRE, otherwise I would likely have added a lot of stress (even through I would have been lucky with the market).

    1. Very good points. The earlier people retire the more uncertainty 30+ years down the road!
      I’m still going back and forth between the two major themes: much lower spending when old (the conventional wisdom) and much more spending when old (travel with more comfort, hire more help, higher health costs,etc.)
      All that is much easier to figure out for 60+ year olds than for 40 year olds!

  5. Prof ERN, yet another wonderful “what if” analysis. I hope you will never suffer from the paralysis of analysis. LOL. I know you are not particularly an advocate of cash for emergencies and to reduce the risk of SWR, however, I look at your Portfolio Parameter Cash portion of your outstanding SWR Toolbox as my counter to fluctuations in withdrawals. My scenario even 5% in cash looks great despite the opportunity cost of having some cash on hand for the unpredictable.

    1. Thanks!
      A caveat: The cash cushion is not something that’s easy to simulate because all the SWR toolbox does is to keep the portfolio % shares constant. There is no feature to simulate a cushon that you deplete if thigs go sour and then build up again as the market recovers. There are just too many loose ends and parameters to consider, i.e., when to tap the cushion and when to replenish.

  6. Hi ERN – Very helpful thank you.

    What about if you choose to delay a higher equity allocation until valuation gets to a more ‘normal’ level? For example, what withdrawal rates could you sustain if you for example went 5 years at 10% equities before switching to say 50-75% equities for the remainder?

        1. True. But just to be sure, not the concept itself but the low equity share. Kitces and Pfau get a very low equity share because they use Monte Carlo, which doesn’t account for the the mean reversion in equity valuations!

  7. Fantastic addition to your body of work with some very interesting results to boot!
    I have been one of those banging the “your spending will likely not be flat over the years” drum. For info, my spending is far more lumpy month-to-month than your model. Using a max to min ratio your model comes out at 2.4 (12000/5000), my spending lumpiness (using that ratio) in any one calendar year peaks at around 11 with an annual average close to 5. This, of course, is an artefact of our lifestyle and I have no idea how unusual that may be.

    1. Thanks for sharing! Well, the quarterly withdrawals will have an infinite ratio (3x divided by zero), and it will still not impact the SWR stats very much. I suspect all the different intra-year variations have a pretty negligible impact.
      I think the better metric is stdev(monthly)/average(monthly). If that’s still smaller than the quarterly you should be good.
      For example, that measure would be:
      3.46 for annual withdrawals
      1.48 for quarterly
      0.39 for that other lumpy monthly withdrawal scenario

      1. Thanks for the thoughtful reply.
        That metric (sample stdev/average) across the same calendar years spending gives: approximately 1 worst case, 0.2 best case, with an average of around 0.6.

      2. Did you consider enumerating the potential impact of steadily declining spending on the WR?
        I would imagine your 1%PA and 2%PA would be reasonable models, albeit this time modelled as decreases vs inflation – see e.g. Banerjee, March 2021, and his – and others – previous work. I assume these and similar reports are what you refer to in a comment above as ‘conventional wisdom’. I ask as there are still quite a few folks peddling – IMO incorrectly – the U-shaped model of spending for retirees. Thanks again.

        1. It would work in the other direction, obviously.
          Personally, I don’t think that applies to the average 40-yo retiree. Maybe from age 70 on you scale back, but not during your mid-life-crisis years. So, one could simulate a saw-tooth pattern: rising until age 70, then decline and then rising again age 80+ to account for nursing home.

          1. Most of the US studies (Banerjee, Blanchet, etc) tend to focus on retirement consumption, so from age 60 to 65 onwards. Figure 3 in this Study (https://ilcuk.org.uk/wp-content/uploads/2018/10/Understanding-Retirement-Journeys.pdf) from the UK shows much more of the lifecycle. On average, in the UK, household consumption peaks around your forties and declines thereafter. Every household, of course, will have its own unique trajectory through life’s major milestones (at whatever age they happen to occur for you & yours) and there are key structural differences between the UK and the US. Personally, I suspect in most cases assuming level (inflation adjusted) consumption will turn out to be a pretty safe approach provided folks spend enough time “getting your baseline budget right”.

            1. Yeah, but that won’t work for me personally. I already know that our spending will likely rise with inflation+x. We will have to hire more help as we age and we like to keep traveling, and that will become more expensive becuase we’d likely travel in more comfort as we age.
              And the big gorilla in the room: healthcare.
              And let’s not forget taxes either.

  8. Hey ERN, great analysis—as usual! I actually had most of these questions in my head, too, and figured one day I’d break down the numbers and (attempt to) run a similar analysis. Thanks for doing all the work! 🙂

    One conclusion that stood out to me was under the “Fluctuations with an upward bias? Worry!” section. While, indeed, we should worry given this circumstance, it’s not actually as bad as I’d have imagined (in terms of failure rate). Needing to overspend your budget by 25% every other year seems pretty severe—a real miscalculation took place or life really got turned upside down. Still, you had a pretty good shot at making it through fully intact. Goes to show the resilience built into a lot of these calculations/assumptions.

    Congrats on avoiding the big B word in retirement. I know that, if anything, I’m increasingly less bored! The world is filled with so much to do, and that needs doing! Cheers!

    1. Thanks!
      The +0%/+25% scenario boils down to a roughly 12.5% higher withdrawal, i.e., going from 4% to 4.5%. Pretty unpleasant failure stats, especially when considering the conditional on high equity valuations: 50% failure conditional on CAPE>20.
      Unconditionally, yes, not much of a difference, but in today’s environment that would be way too scary for my taste! 🙂

    1. Qualitatively, I agree. But I think that drawing down the equity portion all the way to 30%, with 70% bonds seems nuts. It’s based on Monte Carlo simulation without taking into account mean reversion in equity valuations.

  9. The odd thing about nursing homes is if you have any money going in, they’ll drain it, and if you don’t have any money, it’s covered by medicaid.

    1. Sadly true Wallies. Depending on the state, Medicaid nursing homes vary in quality and availability. My parents are both suffering from dementia, my mom in particular with advanced symptoms. My two siblings and I are looking for a suitable ALF with dementia/Alzheimer’s care for them. My father, a disabled Korean War era marine vet, is stubborn and steadfast in his defense not to leave his home. But that’s another story line. In Florida, they would need to drain their savings first before Medicaid kicks in. Most ALFs do not take Medicaid, therefore, we would have to move them which is not ideal for their ailments. The ones that do take Medicaid are not as good. Despite only a 10th grade education for my dad and my mom’s 5th grade, they came to the US from an impoverished Caribbean island and raised their children so well that my siblings and I have been successful enough to retire early. I consulted several elder attorneys on such different legal routes like guardianship. One suggested I set up a trust to protect their assets and get on Medicaid now. However, Florida has a 5 year look back period, therefore, my parents would not qualify. I have a problem with this anyway. Medicaid is truly for the needy. There was one retirement blogger who I had a friendly argument over this. Most of us on the Big ERN’s blog are fortunate to have done the right things to retire early and be financially secure. No need to shelter money this way to takeaway from others who truly need the financial support from Medicaid especially in a world where both federal and state governments are financially strapped. My parents may have enough money to live in a private ALF with memory care for 4-6 years. Actuarily, one of them, if not both, will not make it that far. Nonetheless, if we are blessed to have them longer, their children have agreed to pick up the tab and consider this just another fluctuation and unanticipated consequence to our own retirement plans which we may need to adjust as the Big ERN has so exquisitely conveyed, because we feel this is the right thing to do.

      1. Eduardo, I commend you and your siblings doing right by your parents in their time of need. It certainly seems they’ve raised you well and you’re repaying a debt of gratitude and love by paying for their care. I won’t name names but I’m familiar with a retirement blogger that has no compunction for taking government handouts/freebies while having $2.4M. I’m glad you aren’t like him.

        I can say my grandmother lived and died quite well on Medicare and it didn’t seem to make a bit of difference in her care. My former neighbor has dementia and she was recently sent to an ALF costing $10K/month. She has nothing; all will be paid by Medicare. Although I certainly don’t want to see the elderly sick and dying on the streets, I don’t understand how some are drained of their last assets and some get a free ride but both go to the same hospitals in the end. I don’t see a big difference in care here in PA. Seems like a disincentive to plan for end of life care.

        1. Yes, that’s a concern. Too much of our “needs based” benefits system is based on income, not wealth. But that can cahnge! If I were a FIRE retiree I wouldn’t plan on government subsidies forever…

      2. Thanks for sharing. In a way it’s a sad story but it also conveys a lot pride for how well your parents prepared and how well they reaised you and your siblings.

        We had a similar issue with my mom. She needed 24h home care. It exhausted almost all her assets before she passed in 2018.
        But so what? I didn’t need an inheritance, I’m well-taken-care-of. My mom lived her last few years in dignity and with the assurance to not be a burden to anyone, not even the government.

  10. Before I need to go into a nursing home, I would have died. Being in a nursing home is not living.

  11. Suppose for example that the stock market zoomed up 30% in twelve months, suddenly putting an early retiree saver at their FIRE number years ahead of time. The retiree is considering working a few more months to “make sure the gains stick” so that they don’t quit their job right before a bubble bursts and they end up with a higher initial WR than expected. Would this strategy actually mitigate any risk, or is working a couple more months no more meaningful than reducing one’s WR?

    Jan-August 2021 is looking a lot like 1987.

    1. You could use ERN’s cape based SWR to mitigate the risk that you’re describing. Essentially, be more conservative when valuations are elevated and be more comfortable having a higher WR when they’re low.

  12. I think this goes back to the dangers of using debt in retirement for lumpy expenses even if its low interest debt. If the market is up a lot, its probably fine to use a little extra to pay cash for lumpy expenses like cars or home improvement but taking out a low interest loan that elevates your monthly expenses that you might have to pay back during a market crash can be dangerous.

    1. ERN’s cape based SWR rules might still work fine 2.1% + 0.4/100 = 2.5%SWR. If you had something like 75/25 portfolio you might have been alright given that their bonds have gone to the moon during the past 30 years.

  13. My wife and I both recently retired at 33 – we’ve always saved 50%+ of our income but had some good/outstanding luck with investments so didn’t expect to be at this point so soon. This covered off a few of the things I’ve been wondering/overthinking so thanks!

  14. Hi ERN, new convert to your blog and love the analysis. What if you have a slug of expenses early in retirement (school fees for young kids being the classic example)? How much of an impact to the sequence of returns would that have assuming 20-30% higher expenses for say 5 years?

    1. You’d have to plug in the numbers yourself, see the Google Sheet, Part 28.
      Another option is to simply set aside that big chunk and put it in a MM account or CD in preparation for the large expenses.
      In that case you’d run the SWR analysis independent of that. i.e., lower your net worth by that chunk but also ignore the extra expenses. Might be the lower-risk (i.e, lower Sequence Risk) option!

    2. You can try this out by adding the expenses to the cash flow sheet. I’ve actually made my own heavily modified version of ERN’s sheet that does thing like the onset of substantial medical expenses, big purchases like cars, housing remodels, (randomly assigned) emergency spend, an early substantial crash, and occasional “things get fucked up when you get old” costs. That cash flow sheet makes a ton of this possible to model. Be aware that the CAPE10 sheet ignores these expenses though…

  15. Hi ERN,

    Just curiours what license(s) are you missing to offer the for pay financial advice?
    From your contact page: “For-pay financial advice: I’m not currently licensed to give for-pay financial advice.”?

    Thanks

  16. Excellent article, ERN. I am really enjoying how you are analyzing more of the mechanics of the withdrawal phase. To be honest, accumulation was easy and withdrawal logistics – beyond simply the amount – seems so fraught with annoying complexity that it’s frustrating.

    Also, it would be great if you could do an update to the CAPE fear post to talk about the current CAPE levels as well as the recent discussion on Twitter regarding “fixing” the CAPE.

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