Archives: Articles

How should pre-retirees invest their savings? What’s the difference between the “money’s worth” of an annuity and its “insurance value”? Is economic inequality consistent with human nature? What is the link between economic inequality in the US in 2021 and the gradual decline in prevailing interest rates since 1982?

These are big, timely questions. They are asked and insightfully answered in the four research papers in this month’s edition of Research Roundup. As you might expect, a lot of retirement research enters RIJ’s airspace every day. We can’t keep up with all of the papers, but we try to read a dozen per month and then summarize four to six of them.

The writers are all people worth knowing about: John H. Cochrane, a Hoover Institution fellow who calls himself The Grumpy Economist; Emmanuel Saez of Berkeley, co-author with Gabriel Zucman of an 2019 book on tax-dodging by the wealthy; Alicia Munnell and her team at the Center of Retirement Research at Boston College, and MIT’s Daniel L. Greenwald (accompanied here by a team from Stanford and Columbia).

“Portfolios for Long-Term Investors,” by John H. Cochrane. NBER Working Paper No. 28513, February 2021.

A macroeconomist at the Hoover Institution who blogs as The Grumpy Economist, Cochrane used his keynote address at this spring’s National Bureau of Economic Research conference to ask (and formulate an answer to) the question, “How should long-term investors form portfolios in our time-varying, multifactor and friction-filled world?”

His immediate answer was: “Two conceptual frameworks may help: looking directly at the stream of payments that a portfolio and payout policy can produce, and including a general equilibrium view of the markets’ economic purpose, and the nature of investors’ differences.” That statement sounded relevant to anyone specializing in retirement income planning.

“We should focus on the stream of dividends, or more generally payoffs, that an investment can produce, rather than focus on one-period returns,” he adds. “Second, we should take a general equilibrium perspective. An investor should ask, what is the economic function of markets, and what is my role in it? If I want to buy, who is selling and why? Answering this question can cut through knots of algebra and statistics, and avoid many fallacies.”

Cochrane’s speech contains both mathematical formulas and pithy, informal asides. For instance here’s a comment on indexing. “If you’re an average investor—if you know that you’re no different from the average—or if you don’t really know you are different and how—you’re done, you know the answer,” he writes. “Off to the total market portfolio with you.”

“The need to properly hedge outside income or liability streams looms large, and I think it is something done poorly by our current portfolio theory and practice,” he concludes. “That involves thinking about payout policies as much as portfolio policies.  Risk management – describe what the bad states of the world are to you the investor, and make sure your portfolio isn’t bad at just that time—should be the core of investing, portfolio management and evaluation, not a small afterthought.” 

“What Is the Value of Annuities?” by Alicia Munnell, Gal Wettstein, Wenliang Hou and Nilufer Gok. Center for Retirement Research at Boston College Brief, March 2021, Number 21-5.

“No one has addressed this topic in two decades,” write a team of economics researchers at the Center for Retirement Research. Their recent brief estimates the money’s worth and the wealth equivalence of immediate, inflation-indexed immediate, and deferred annuities (bought at age 65 and start payments at age 85) “to capture both the expected value of such products and the value of the insurance they provide.”

The “money’s worth” of an income annuity is the ratio of the expected present value (EPV) of its payouts to its premium (generally quoted per $100,000). In a sense, it is the average investment value of the annuity. Despite changes in interest rates and mortality rates, “Money’s worth has remained stable over time, with an expected payout of about 80 cents per premium dollar for immediate and indexed annuities and about 50 cents per dollar for deferred annuities,” the researchers write.

“Wealth equivalence,” by contrast, means “the share of starting wealth an individual would require to be as well off with annuitization as without it. The smaller the necessary share of wealth, the better the product.” In other words, how much more money would you need at retirement if you wanted to self-insure against out-living your financial resources. This is the insurance value of an annuity: It frees up capital that you might have needed to hoard against the possibility of living to 100.

“The results confirm the intuition that groups with lower life expectancies have lower expected returns from lifetime income products. Blacks have lower returns than whites of similar relative education, and those with lower education have lower returns than those with higher education within racial groups,” the authors conclude. “However, this pattern does not hold when accounting for the insurance value of annuities.”

Echoing an observation by Moshe Milevsky, the pensions and annuity expert at in York University in Toronto, they write, “In particular, Blacks tend to get better value than whites despite their lower expected returns from such products, because Blacks have more uncertain longevity alongside lower expected lifespans.”

“Public Economics and Inequality: Uncovering Our Social Nature,” by Emmanuel Saez. NBER Working Paper No. 28387, January 2021.

With the Biden administration asking affluent Americans to sacrifice personal wealth (via higher taxes) to enhance public wealth (better infrastructure and social insurance), University of California-Berkeley economist Emmanuel Saez has published a timely paper exploring the boundary between our drive for individual gain and our instinct for cooperation.

The paper, which connects economics and anthropology,  focuses on the degree to which a nation’s citizens are willing (or not) to cooperate on the provision of universal education, health care, retirement benefits, and support for the poor.

“The standard economic model is based on rational and self-interested individuals who interact through markets, yet it is obvious that humans are also social beings who care about and act within groups such as families, workplaces, communities, or nations,” Saez begins.

“Our social nature, absent from the standard economic model, is crucial to understand our large modern social states and why concerns about inequality are so pervasive. Taking care of the young, the sick, and the elderly has always been done through families and communities and likely explains best why education, health care, and retirement benefits are carried out through the social state in today’s advanced economies.” But there are limits. “Humans are willing to pool resources with the social group they identify with but typically not others,” he writes.

With respect to retirement benefits, Saez points to two important reasons why, in advanced countries, “the problem of retirement is resolved at the social level, not at the individual level.” First, “individuals are not able to save on their own and invest wisely,” he writes. Second, a program like Social Security relieves adult children of the cost of supporting their elderly parents and uses risk-pooling to reduce the aggregate cost of supporting retirees.

Today, commentators have said that it’s useless to raise taxes on corporations or the wealthy because they will merely avoid the tax or pass the cost on to others. Saez seems to have that type of “behavior response” when he concludes:

“A social system functions best when individuals internalize the social objective. For example, means-tested support for those in need works best if recipients do not try to game the system; a tax system works best if taxpayers do not systematically try to avoid and evade their tax obligations. Behavioral responses are not only costly in terms of public funds, but they can also undermine trust in the social program which is perhaps an even greater harm. Therefore, it is better to design the social system to try and eliminate behavioral elasticities rather than take existing behavioral elasticities as a given as public economists generally do.”

Today, commentators have said that it’s useless to raise taxes on corporations or the wealthy because they will merely avoid the tax or pass the cost on to others. Saez seems to have that type of “behavior response” when he concludes:

“A social system functions best when individuals internalize the social objective. For example, means-tested support for those in need works best if recipients do not try to game the system; a tax system works best if taxpayers do not systematically try to avoid and evade their tax obligations. Behavioral responses are not only costly in terms of public funds, but they can also undermine trust in the social program which is perhaps an even greater harm. Therefore, it is better to design the social system to try and eliminate behavioral elasticities rather than take existing behavioral elasticities as a given as public economists generally do.”

“Financial and Total Wealth Inequality with Low Interest Rates,” Daniel Greenwald, Matteo Leombroni, Hanno Lustig, Stijn Van Nieuwerbergh. NBER Working Paper 28613, April 2021.

After World War II, the price of a 30-year inflation-indexed term annuity steadily declined and, after 1982, steadily increased. Over the same period, wealth inequality in the US steadily declined until 1982, then rose. According to this paper, that wasn’t coincidental.

The fall in interest rates after 1982 is the key ingredient. It fostered a rising stock market, and stock ownership is concentrated in the wealthiest fifth or tenth of households (even though more than half own some mutual funds). But the authors of this paper say that people holding any assets with “long durations”—houses, long-term bonds, buy-and-hold stocks—would have grown richer from falling rates.

As financially literate investors know, a bond’s or portfolio’s duration is its sensitivity to interest rate movements. For instance, the market price of a 30-year bond changes much more than a short-term bond when the prevailing interest rate changes. If you hold high duration investments, declining rates help you and rising rates hurt you.

Using data from the Survey of Consumer Finances, the researchers found that “Low-wealth households have low financial durations, driven by their higher share of deposit-like assets, the presence of consumer debt, and lower shares of housing, private business, and stock market wealth. The reverse is true for high-wealth households. This heterogeneity in financial duration is a new empirical finding, and crucial for the response of financial inequality to interest rates.”

This process, they say, is natural. They also suggest that falling rates demands certain savings behavior. “A persistent decline in real interest rates, like the one experienced in much of the world between the 1980s and the 2010s, naturally leads to a rise in financial wealth inequality. Households whose wealth is predominantly made up of financial rather than human wealth, and particularly those with short-maturity assets, must increase savings to be able to afford the same consumption plan,” the paper said.

In this scenario, young people and low-income people are hurt the most. “While all households require more financial wealth to finance the old consumption allocation, young households require the largest compensation. Since they must save for retirement for many years, the loss in compound interest hits them particularly hard,” the paper said.

“While the wealthy see a large increase in financial wealth under the compensated distribution (as much of 38% of the increase in aggregate wealth goes to the top-1%), the top-1% and top-10% financial wealth shares and the gini nevertheless fall since the required increase in financial wealth for the young is greater still. In other words, the large human wealth of the young does not provide a large enough hedge against interest rate declines.”

© 2021 RIJ Publishing LLC. All rights reserved.

Life insurance companies are using predictive analysis, derived from substantial insurer historical data, as the basis for modeling variable annuity owner behavior (OB). An important metric for predicting OB is the degree to which a contract is “in the money” (ITM). 

The insurer is concerned that when the liability (present value of the future estimated owner income) is substantially greater than the account value, the owner will elect the guaranteed income and the insurer would then be on the hook for the estimated difference.  But how can contract owners or their advisers know when the contract is ITM, so that they could take advantage of this situation?

The CEO of an actuarial firm that predicts OB recently told Retirement Income Journal:

Some issuers argue that annuity owners are not actuaries or calculating machines. So, they’ll look to simpler proxies, like ‘nominal moneyness.’ Others believe that while annuity owners aren’t doing detailed calculations, they have some sense of actuarial/economic value. So, they use an actuarial moneyness measure. We spend more time talking with our clients about this issue than anything else, because it is critically important to the long-term profitability of these products.

Can owners “intuit” a contract’s ITM? As an actuary, I’m skeptical. Most VA contracts have many moving parts; Estimating the ITM would require both an excellent method and a substantial programming effort. But, as shown below, these kinds of apps are fast becoming a reality. Spurred by growing owner awareness, and recent Best Interest regulatory requirements, they’ll likely have a major impact on OB.

R. Michael Markham

However, the CEO’s comment above raises a useful and important distinction. This actuarial/economic value is an insurer’s liability, based on their finances; whereas, for the owner, the value would be an asset, based on their finances. These two values must be different, and so must their ITMs.

“Nominal ITM” would be a crude approximation, but it does establish the principle of a separate owner-centric ITM, one that the owner could use to optimize their income. The issuers would need to modify their OB models to account for the owners/advisers’ reactions to this or similar metrics.

So, what are the challenges in developing a more useful insurer’s OB model?

The need to jump the insurer/owner divide

To develop an OB model, insurers would first need to jump the insurer/owner divide.  That is, put themselves in the shoes of the owner/adviser, and ask how, at any point in time, they would perceive the available options.  Then they’d have to try to predict which current and future choices would maximize their ITM-ness.

The overarching principle must be that the calculations are based on the owners’ perspectives and on their attributes, not the issuers’. The owners see the world from their own financial perspectives. They’re probably not aware of how the insurer manages its finances. They care only that the policy fulfills the guarantees in the contract.

How the ITM is calculated

What are the actual (and optimal) mechanics for calculating ITM? If we assume that each future year-end has its own ITM, which one(s) would be utilized? Further, would the insurer’s actuarial ITM (I-ITM), or nominal ITM (N–ITM) determine OB, or some weighted combination of the two?

The general equation for ITM is:

ITM = (PV Future Income – Account Value)/Account Value

This equation looks simple. Far from it.

• There are many possible future scenarios, each with numerous possible income streams.
• There are several ways that the PV component could be calculated; from deterministic to stochastic.
• There are many approaches for determining the benefits and how they would be discounted.

Here’s a diagram showing some possible income streams, which could also include different combinations, such as a stream of free withdrawals, followed by the election of a guaranteed income stream, followed by, in some cases, when allowed, a death benefit or a surrender.

If owners are “not actuaries or calculating machines,” how could they determine any ITMs for these complicated variable products?

How to address this challenge

There are two kinds of ITMs: the actuarial one, observed by the insurer (I-ITM), and the one observed by the owner/adviser (O-ITM). This distinction is critical. Owners will decide based on the O-ITM, not the I-ITM. 

The owner will rely on an ITM that is based on the contract’s relevant details, of course, but equally on the owner’s age, gender, risk-appetite, estimated longevity, benefit preferences, tax brackets, targets, legacy, and other goals. The O-ITM is a function of all these things.

Ask the question: what lump sum would the owner need to invest today in a no-load mutual fund portfolio, based on their risk-appetite, to self-fund the contract’s projected owner net income stream? The answer can be computed by simulating their investing in a mutual fund whose portfolio matches their risk-appetite, and by deducting the contract’s periodic income payments, with the last deduction resulting in a zero balance.

The lump sum is what their O-ITM asset would be worth to them today. This asset would then be the PV component of the numerator used in the O-ITM calculation. Discount rates used for contract income would equal the net returns of this owner-centric portfolio. In fact, this is critical for what comes next: portfolio returns make sense only in a specific economic scenario.

‘Most Frequent Gambit’

Obviously the future is mostly unknown. There are many, many possible future economic scenarios. To address this challenge, the best approach is stochastic, i.e., projecting O-ITM distributions derived from numerous calibrated, randomly generated economic scenarios.

These economic scenarios would be combined with current user-entered policyholder attributes, to help match present and future decisions based on owner goals.

The metric for evaluation is the PV of net income, and we assume that the owner aims to maximize this figure. This is a common theme in mathematical approaches. But this “goal seeking” needs to be developed and applied in a way that the adviser and owner will understand and accept.

Broadly considered, we can begin to locate the maximum by considering every possible “start year/benefit type” gambit. In each scenario, each such “gambit” produces its own O-ITM figure. But we need to sift the resulting figures to estimate the owner’s most likely choice (OB). This determines the contract’s overall O-ITM.

So, what are the steps for calculating the OB, and the contract’s overall O-ITM?

1. Project numerous economic scenarios and determine benefits for the targeted range of years.
2. For each scenario/targeted year, produce an O-ITM frequency distribution for the maximum gambits for each scenario.
3. Using this distribution, identify the most frequent gambit (MFG).
4. For each targeted year, in each scenario, use the MFG to determine the O-ITM for each year and record each year’s gambit.
5. Starting at the evaluation year, check each scenario, and, if its MFG occurs in that year, then that gambit will be used to calculate the scenario’s unique and final O-ITM, as of the evaluation year.

Note that all income streams are discounted based on the scenario’s returns, for a shadow no-load mutual fund portfolio based on the owner’s risk-appetite.

Also note, each year’s frequency distribution is derived from the assumption that the owner is running the O-ITM app at that time, within each scenario. Due to the very large number of calculations required to do this, for each scenario and each projected target year, approximate methods will be needed.

B-I requirements spur online apps

The app described so far seems theoretical, but the demand for O-ITM apps is not. Advisers, broker-dealers, and producers will be stepping up their game for their clients when selling, replacing, or managing variable annuity contracts. They will need to comply with  new regulations that address new fiduciary responsibilities, such as the SEC’s Reg-BI, new standards from both the CFP Board and the Department of Labor and multiple states’ Best Interest requirements (e.g., New York’s Reg 187 or Arizona’s SB1557).

Several watchdogs will be looking over their shoulders; advisers will need to evaluate contracts before and after purchase, providing on-going due diligence and account-management advice. The Biden administration is likely to enforce Best Interest and strengthen the fiduciary requirements for anyone selling or managing these kinds of products. New online apps, such as SmartAsset, are help potential policyholders pick the most reliable advisers. Every adviser will need to bolster a fiduciary relationship with clients.

That’s exactly what’s happening. A method called BI-SEM, from the BI-SEM group, has been proposed. It addresses the need for a standard stochastic approach to the evaluation of variable deferred annuities and deferred annuities in general. Second, apps are now emerging to implement the method.

BI-SEM (Best Interest – Stochastic Evaluation Method)

BI-SEM is a new stochastic evaluation method that bases its O-ITM on a policyholder’s key attributes, and discounts future estimated net income based on the policyholder’s risk-appetite (see BI-SEM whitepaper). I believe the BI-SEM method, when implemented correctly, provides an excellent O-ITM model for owners/advisers.

As a stochastic method, BI-SEM employs important features from the NAIC’s Commissioners Annuity Reserve Valuation Method (CARVM) and Principles Based Reserves (PBR). In their whitepaper, the group emphasizes piggybacking on existing, well-vetted statutory valuation procedures. Any app that properly employs this method should significantly impact OB.

In addition to Age and Sex, the Method makes use of the following valuable attributes in its projections:

• Risk Appetite
• Health Status/Estimated Longevity
• Goals and Strategies
• Tax Brackets
• Benefit Preferences
• Retirement Age Targets
• Legacy Wishes

In most cases these are attributes not known to the Insurer. BI-SEM emphasizes that, unlike the Insurer, an adviser can ask these personal questions. In fact, for Best Interest, they must.

When implemented properly, the method is a sound one for the owner’s interests, because it:

• Takes advantage of important personal attributes known only to the policyholder.
• Is generally based on current well-vetted US Statutory Valuation Methods.
• Is stochastic – a better simulation of real life from more viewpoints.
• Calculates a current dollar evaluation of a contract.
• Calculates a Score for the contract
• Provides dispersion statistics.
• Uses standardized metrics for contract comparisons.

A BI-SEM App from InjAnnuity, Inc.

This discussion is not purely theoretical.  O-ITM apps already exist, on the internet.  The DARMA online app authored by James Kavanagh, a founder at InjAnnuity Inc., is an excellent implementation of BI-SEM. The screen shots below highlight its versatility. 

The app produced meaningful reports and charts and did an excellent job of proving its numbers with a comprehensive set of on-demand audit reports (see below).

The existence of these kinds of apps, and their easy accessibility on the internet, greatly strengthens the case for the O-ITM-centric OB model.

Getting it right

Usage of O-ITM-based apps is not widespread—yet. The approach now needed by insurers is to assume an OB Model where the owner/adviser is much more informed about O-ITM. They need to assume that they are using a sophisticated app to best the insurer.

The overarching need in the model is to base owner/adviser decision-making from their now better-informed owner-POV. For the best results, the model should be stochastic. The big challenge will be in estimating owner attributes that are not known to the insurer. They can be based on the information contained in the contract. For example, an owner’s risk-appetite might be derived from their subaccount portfolio.

Other attributes might be derived from elected riders. Targeted retirement ages and other owner goals could be derived from historical experience for policyholders in the same cohorts. This would be combined with attribute probabilities derived from the historical studies performed by suppliers like Ruark, LLC.

OB assumptions should now be updated to consider that a certain percentage of policyholders are O-ITM aware, N-ITM aware and recognize that this is a moving target as O-ITM-aware apps proliferate. Getting this right is critically important to the long-term profitability of these products.

© 2021 R. Michael Markham.

With ever-rising technology costs and a squeeze on their margins, US retirement plan recordkeepers have had only one refuge of profitability: Becoming big enough to achieve the necessary economies of scale. The industry has dramatically consolidated, and Prudential may be on the verge of exiting the game. 

A report on Tuesday by Bloomberg said that Prudential, the eighth largest recordkeeper by assets under management in the US, is thinking about selling its 401(k)/403(b)/457 retirement plan recordkeeping business. Sources weren’t identified and Prudential didn’t confirm the report.

“The Newark, NJ-based life insurer is working with a financial adviser to find buyers for its so-called full-service solutions business,” said Bloomberg, attributing the information to unidentified sources. “No final decision has been made and Prudential could opt to keep the business.” 

One possible reason for a change: the need for a big information technology upgrade. One plan adviser who has worked with Prudential for years, Barbara Delaney of HUB International, said that Prudential has multiple legacy systems, while competitors Empower and Vanguard have gone to faster, more agile cloud-based computing.

“This is no surprise. 401(k) margins are so small. Transactional businesses are tough to run. Prudential probably said, ‘Let’s focus on what we’re good at: life insurance and annuities,” Delaney told RIJ today. “And even Prudential employees seemed frustrated with their own systems.”

To illustrate the need for up-to-date technology, Delaney pointed to the passage of the CARES Act in 2020, which suddenly allowed retirement plan participants to take more money out of their accounts. Recordkeepers had to adapt within the space of hours, and all recordkeeping  systems were overtaxed. 

Prudential may also be underwater on the stable value funds offered in its retirement plans. As interest rates have gone down, stable value fund providers in some cases aren’t earning enough on their general account investments to cover the minimum yields contractually promised to plan sponsors. 

The plan recordkeeping business in the US has been consolidating for years. In the past year alone, MassMutual and Wells Fargo sold their retirement plan businesses to Great-West Lifeco, the second largest recordkeeper by AUM, and Principal, the fifth largest, respectively.

“This was not unpredictable. Prudential recently pulled out of the retail GLWB business, and the trend is toward of recordkeeper consolidation,” said Michelle Richter of Fiduciary Advisory Services. Another life/annuity industry insider told RIJ yesterday,  “My understanding is that [a Prudential deal] would be similar to the MassMutual sale. Retirement business very much a scale play.”

Scale is measured in fees on assets under management, or AUM. Fidelity leads in recordkeeping AUM with more than $2 trillion. Empower is a distant second, with almost $500 billion, followed by Vanguard, Alight, Voya, Principal and T. Rowe Price. (Alight is the new name for Aon Hewitt’s benefits business, now owned by the asset manager Blackstone. Empower is a unit of Great-West Lifeco.) Prudential was eighth in 2020 with about $180 billion (all AUM data come from Plan Sponsor magazine).

Stable value funds, with their modest but reliable yields, used to be the primary default investment for 401(k) plan participants who didn’t choose their own investments. The Pension Protection Act of 2006 allowed target-date funds and managed accounts to be default alternatives. These defaults included equities, and so offered much more higher upside than stable value funds. The Great Financial Crisis also ushered in the Fed’s ultra-accommodative interest policy, whose low rates reduced the returns on stable value funds. 

While the rates on many stable value funds were re-set every year, some retirement plans included stable value funds with long-term minimum guaranteed rates as high as 3% or more. Those promised rates, which helped providers win business, are now the source of losses, in some cases. Delaney said she does not believe that underwater stable value guarantees would cause Prudential to leave recordkeeping, however.

Prudential’s 10-K filing with the SEC on February 19, 2021, page 63, shows that $28.1 billion of the company’s $62 billion in contracts promising guaranteed minimum returns have a floor return of between 3% and 4%. An addition $900 million worth have contracts with guarantees of greater than 4%. In total, 47% of those contracts have minimum guarantees greater than 3% at a time when the ten-year Treasury is earning 1.56%. “The funds need to earn 3% to cover contractual minimums and the insurer’s cost of capital,” a person familiar with stable value funds told RIJ.

Prudential had considered growing its recordkeeping business through acquisition, but now has apparently decided to go the other way. A person familiar with Prudential Retirement estimated that the recordkeeping business accounts for about 10% of Prudential Financial’s earnings and 7% to 8% of its employees. 

Prudential has been an innovator in the in-plan annuity market. It began offering IncomeFlex, a guaranteed lifetime withdrawal benefit that could be wrapped around a plan participant’s Prudential target-date fund for an additional fee. That product—introduced a decade before the SECURE Act reduced barriers to annuities in plans—never reached critical mass, despite a big push from Prudential. 

In the retail retirement space, Prudential had $171.4 billion in variable annuity assets under management at the end of 2020, according to Morningstar. That was more than any other firm expect Jackson National ($229.1 billion) and TIAA ($527.4 billion). TIAA’s VAs are primarily group annuities in 403(b) plans. 

© 2021 RIJ Publishing LLC. All rights reserved.