My response to the titular question: Pretty much, yes. Worse, it’s dangerous to your financial health.

This is both a bold and self-interested accusation. It’s bold because virtually the entire financial industry is engaged in conventional financial planning. Indeed, the conventional financial planning software industry generates $1 billion in annual revenues selling financial planning tools. And the accusation is admittedly biased because I head a personal financial planning software company, Economic Security Planning. Our main product — MaxiFi Planner — does economics-based financial planning, which attempts to answer some of the same questions but is virtually unrelated to conventional financial planning.

Rules of Dumb

Standard financial planning begins by asking people what they want to spend in retirement. Consider the following exchange between financial planner Sandy, and her client, Joe.

Sandy: Tell me your spending target.

Joe: One trillion dollars a year.

Sandy: Come on. Give me a number you can afford.

Joe: A penny a year.

Sandy: Give me a realistic spending target.

Joe: I don’t know what’s realistic.

Sandy: What are you now spending? That’s a good reference point.

Joe: No idea. And if I did know, how would I know if it’s too high or too low? If I’m spending too much now and plan to do so in retirement, I’ll run out of money. If I’m spending too little now and plan to spend the same in retirement, I’ll leave money on the table.

Sandy: You’re right. That’s why I suggest we use the industry’s standard replacement-rate rule. It sets your target at 80 percent of your pre-retirement earnings.

Joe: But if the target is a lot higher than my current spending, shouldn’t I spend more now and set a lower target for my retirement spending — perhaps a 50 percent replacement rate? I’m not trying to deprive myself before retirement so I can spend more when I’m 80 and, possibly, dead.

If the target is a lot lower than my current spending, shouldn’t I raise it by using a higher replacement rate? Why should I plan to have my living standard drop after I’m retired? If I wanted it to drop, maybe I should be saving nothing for retirement. And whatever replacement rate we use, how will we know the plan works — that I won’t go broke or leave money on the table?

Sandy: Let’s go with the 80 percent rate for now. It’s tried and true, and I’ll check the probability your plan works using a standard Monte Carlo statistical simulation, which calculates the probability that you can afford your retirement-spending target given how you are investing, your current assets and how much you’re saving.

Joe: But everyone’s different and what’s tried and true for most people won’t necessarily be appropriate for me. Nor, frankly, will my spending be constant after retiring. So, shouldn’t we be setting a separate target for spending in each year?

Sandy: You ask a lot of questions. Let me run you through the analysis.

Joe: Fine. You’re the professional. 

Now Sandy has Joe just where she wants him. She’s gotten Joe to adopt a retirement spending target that’s far too high for most households. Let’s listen to what happens in their next meeting.

Sandy: Joe, before you came in, I ran a Monte Carlo simulation program using the 80 percent replacement rate. There’s bad news. Your current portfolio is very conservative and you aren’t saving much. Hence, the chance of meeting your target without running out of money is only 40 percent.

Joe: That’s terrible! What should I do?

Sandy: Well, I also ran the simulation assuming you invest in our aggressive-growth mutual funds. Yes, they come with fees. But your probability of meeting your target is now 87 percent. My advice is to invest in these funds.

Joe: If you’re sure that’s best, go ahead.

Sandy, perhaps unwittingly, has not only raised Joe’s probability of meeting “his” target; she’s also raised Joe’s probability of losing his shirt. Indeed, the dance between Sandy and Joe is largely a bait and switch sales job. Sandy baits Joe with the “standard” replacement rate, which for most households is miles too high, then presents him with a problem he doesn’t necessarily have, and then, to her financial benefit, switches Joe to a solution that comes with downside risk that he’s either not told about or is told is small.

Life-Cycle Consumption Smoothing

In contrast to conventional financial planning, economics-based financial planning is predicated on the “life cycle theory.” This theory was conceived (albeit without its 1950s-era name) in the 1920s by the premier economist of his day, Yale’s Irving Fisher. Fisher worked out the math of intertemporal (that is, across time) consumption choice. He showed that how much people should consume (and, thus, save) over time depends on something we all share: human physiology. Fisher focused on our guts — the fact that as we eat more and more, we get fuller and fuller. Consequently, every additional bite delivers less added pleasure. In the obverse, our hunger pangs get increasingly sharp as our meals are reduced in size.

Fisher modeled our enjoyment from food as well as consumption in general by postulating three principles. First, people derive happiness — “utility” — from their consumption. Second, utility increases with the amount consumed. Third, the increase in utility from added consumption is smaller if you are already consuming a lot. This third predicate is called “diminishing marginal utility.”

It’s easy to test diminishing marginal utility in humans. When my youngest son, David, was nine, I took him to an excellent bakery and bought 20 cupcakes. It was about 3 p.m. — eons from lunch. Back home, I put all 20 cupcakes on a plate and told David to dig in. He devoured the first in seconds. The second took a minute. The third took three minutes. In the middle of the fourth, I said, “David, eat as many as you’d like. Mom’s not home.” His response was, “Let’s save the rest for tomorrow.”

This spreading of consumption over time — “consumption smoothing” ­­— is precisely what Fisher’s math (lifetime utility maximization) predicts. It’s not just a nine-year-old’s behavior; It’s all of our behaviors. This is why we all (try to) save for retirement. We don’t want to splurge when young and starve when old. Nor do we want to do the opposite.

Consumption smoothing, when applied to financial planning, means coming up with annual spending recommendations for households that obey three requirements.

First, spend within your lifetime financial means. Second, make sure the recommended annual spending path doesn’t violate cash-flow constraints. Cash-flow constraints are particularly important for younger households who have young mouths to feed, mortgages to pay off, and future college tuition and other special expenditures to cover. Such households won’t be able to have as high a living standard in the short run as they’ll enjoy in the long run because they can’t borrow at a reasonable cost against their future incomes. For such households, consumption smoothing entails having as stable a living standard over time as possible.

Third requirement: Set the annual spending path such that, apart from cash-flow considerations, your living standard is either stable (smooth) through time or gradually changes based on the pattern you desire. For example, you may want your living standard to decline by, say, one-half of 1 percent per year starting at age 80.

Consumption Smoothing in Practice

Smoothing is a solvable problem. After all, if you can approximate your lifetime resources (regular assets, retirement accounts, labor earnings, etc.), the desired time pattern of your living standard, the tax-benefit system and your borrowing limitations, what you can spend each year can be calculated.

Unfortunately, making this calculation is far beyond the capacity of anyone’s brain, including whiz-bang economists like me. I once posed a simple consumption-smoothing problem to a roomful of economists specializing in personal finance. I asked them how much a single middle-aged person with no children and no complicated finances should spend each year if the person wanted to be able to spend the same amount (adjusted for inflation) annually through the end of his or her life. The answer was close to $75,000. None of the “experts” came within $15,000 of this figure. The lowest figure was $30,000. The highest was $135,000.

Why were the economists so far off? The question is akin to asking people to think 30 moves ahead in chess. And the average Joe is no better at this than the average economist. 

How do I know this problem is so hard? My company’s financial planning software takes into account a long list of factors including future labor earnings, inflation, regular and retirement account asset balances, returns on assets, retirement account contributions and withdrawals, life insurance decisions (which entail paying premiums), federal and state income taxes, payroll taxes, mortgage payments and other housing expenses, future housing changes, vacation homes, Medicare’s standard and high-income Part B premiums, real estate holdings, special expenses and receipts, each of Social Security’s 11 benefits (whose receipts are governed by literally hundreds of thousands of rules) … you get the point.

Were one to print out the software’s very efficient code in legible type, you would need 3,500 sheets of paper. It’s an intense program not simply because of the need to sweat the details. The computation problem, itself, is intense.

Without diving too deep (though I can’t resist a quick dip), the solution requires running three dynamic programs that trundle results back and forth until they jointly converge on an internally consistent solution. Dynamic programming is a computation technique developed in the 1950s by the mathematician Richard Bellman; it’s used by economists and engineers to solve problems involving choices over time.

The method, in the context of consumption smoothing, is to figure out what the household should do in the last period of its life and then use that solution to figure what should be done in the second to last period of life, continuing backward to the current period. This explains why dynamic programming is also called “backward induction.” 

The iteration between the three programs is necessary to handle chicken and egg problems. An example here is spending and taxes. The sums you can spend smoothly over time depend on what you’ll have to pay in taxes over time. But what you’ll owe in taxes over time depends on what you’ll be spending over time. Hence, calculating spending (getting the chicken) requires knowing the taxes (having an egg), but calculating taxes (having an egg) requires knowing spending (getting a chicken).

To summarize, economists have spent nearly a century pushing an elegant theory that has only recently become operationally useful because it was so difficult in terms of computation. Conventional financial planning flourished in this economics-based planning vacuum.

Life Cycle Insuring and Investing

Fisher’s model laid the basis not just for the economics of spending and saving. It is also central to the economics of insurance and investing. Indeed, 12 Nobel prizes have been awarded to economists for work on financial behavior and financial markets, all of which traces back, directly or indirectly, to Fisher’s seminal contribution.

When applied to these other areas, the prescription is to smooth consumption not just over time, but over times — good times and bad times. Good times are when your house doesn’t burn down. Bad times are when it does. Homeowners insurance smooths (stabilizes) our living standard across these two scenarios. The same is true of other forms of insurance, including life insurance, longevity insurance (insurance against outliving your savings), auto insurance and health insurance.

As for investing, good times are when you are invested in risky assets and they perform well. Bad times are when you are invested in risky assets and they perform badly. The way to smooth your living standard between these two states is to reduce your holdings of risky assets and increase your holdings of safe assets. The higher the proportion of safe assets you hold in your portfolio, the smaller the living-standard difference will be when stocks do well and when they don’t.

Conventional Versus Economics-Based Advice

Guiding households on how much insurance they need requires having a “living standard machine” — i.e., the appropriate software — that can tell how much their living standards will drop under worst-case scenarios. Take life insurance. The proper amount ensures that survivors suffer no living standard decline relative to what otherwise would have been the case. Thus, economics-based financial planning has a clear criterion for its life insurance recommendations. Conventional financial planning has no equivalent for the simple reason that it doesn’t calculate the annual living standard path that needs to be insured.

As already indicated, conventional investment planning calculates the probability that you’ll run out of money if you 1) save the wrong amount (namely, what you’re now saving, which is surely incompatible with consumption smoothing) year after year during your working years, 2) spend the wrong amount (the targeted amount, which is just a reckless guess or profit-motivated suggestion by the industry) year after year when retired, and 3) never adjust your spending during retirement.

The calculations are, as Sandy said, generated via Monte Carlo simulations, where each simulation is predicated on a randomly chosen path of returns on the assets you hold. Since, as mentioned, the goal is to make your plan’s failure rate (that is, failing to generate desired income) as low as possible, conventional planning militates toward holding assets that have, on average, high returns. The stock market is such an asset. It’s no wonder, then, that conventional planning typically recommends that we primarily hold stocks, particularly when young.

If we think of three outcomes — doing really great, doing okay and doing miserably — conventional planning will recommend holding assets that increase the odds of doing really great, but that will also increase the odds of doing miserably. The odds of doing just okay will, necessarily, be reduced. The obvious problem with this “advice” is that it focuses on the chances of particular outcomes without weighing the quality of the outcomes. Due to diminishing marginal utility, doing really great (David’s getting to eat a fifth cupcake in a row) will provide much less benefit than it sounds on first hearing. On the other hand, doing miserably means experiencing a very low living standard. This means no supper, let alone cupcakes for dessert.

The focus on probabilities — the product of the probability of an outcome times the household’s happiness from the outcome — is problematic. Let me illustrate the problem in a different context. Suppose you are considering whether or not to have an operation to remove a tumor. The surgeon tells you that the chances the tumor is malignant are less than 10 percent and suggests, therefore, that you not bother with the surgery. She assures you that the surgery is perfectly safe, but the recovery will be painful. Furthermore, the doctor says that she only recommends the surgery when the chances of malignancy are above 30 percent.

What should you do? If you focus just on the probabilities, you’ll make a huge mistake. Yes, the surgery may lay you up and hurt like heck. But if the low-probability event — the tumor is malignant — is associated with a terrible outcome (e.g., you’ll die), you’ll surely have the surgery if you give it any independent thought.

Economics-based portfolio advice focuses on the expected utility of the outcome. Again, this is the product of the probability of the outcome times the utility of the outcome. In the case of investing predominantly in stocks, the probability of the market doing terribly is very low. But being financially wiped out would be dreadful. Hence, putting all, or even most, of your eggs in the stock market basket will be far from optimal.

So what is the economically optimal portfolio? The one that maximizes the household’s expected remaining lifetime utility, where this measure is the probability of each outcome times the utility from the outcome, summed over all outcomes.

My company’s software calculates expected utility from alternative lifetime investment strategies. Hence, it can be used to find the expected-utility maximizing strategy. For households who are neither risk lovers nor risk haters, the right portfolio is generally to hold about half of your assets in stocks and the rest in bonds and other low-yielding, but safe, assets. This is different from the default portfolio allocation of many, if not most employer-provided retirement accounts. Such plans typically default to allocating a substantially higher share (generally 75 percent) of assets to stocks, particularly for younger workers.

Will Economics-Based Financial Planning Triumph?

I sure hope so. There’s no end to the financial magic a living standard machine can produce.

To begin, it can answer critical questions, including “How much will my sustainable living standard decline if I retire early?” or “How much will my living standard rise if I downsize my home?” or “Will getting a graduate degree in education raise or lower my living standard given the costs of tuition and lost time?” or “Can we afford to have more children?” or “How much will our living standard decline if we get divorced?”

Second, a living standard machine can often find safe ways to raise a household’s living standards. Take the decision of when to start retirement account withdrawals and Social Security benefits. It turns out that taking retirement withdrawals first and Social Security second, preferably at age 70, will, generally speaking, very dramatically raise a household’s living standard by lowering its lifetime taxes and raising its lifetime Social Security benefits. Yet many, if not most, households incorrectly think they should tap into Social Security first. And they lose, what for them, is often a minor fortune.

Third, Monte Carlo living standard simulations will eventually let people understand the potential upside and downside living standard risk they face, not just from investing more or less aggressively but also from unexpected changes in inflation, unexpected out-of-pocket health care expenses, unexpected changes to their future labor earnings, etc.

This entirely realistic vision of economics-based financial planning advice comes with a call to economists to start thinking of their field as being prescriptive as well as descriptive. We now have the ability to genuinely help households to make proper financial moves. This, I suggest, is of far more social value than studying household financial mistakes and then blaming them for behaving badly — being myopic, undisciplined or financially illiterate.

This, by the way, is precisely the program of behavioral finance — blame households for not making proper financial decisions, which, by the way, behavioral economists have, themselves, not the remotest ability to get straight. In short, economists should become doctors, who don’t just study our physical problems but prescribe real medicine.