Reconsidering the philanthropic foundation minimum payout … · 2019-03-24 · and inﬂuenced public policies tackling a host of social problems (Bulmer, 1995). This institution,

Please cite this article in press as: Afik, Z., & Katz, H. Reconsidering the philanthropic foun-dation minimum payout policy under a “new normal”. Journal of Policy Modeling (2018),https://doi.org/10.1016/j.jpolmod.2018.09.004

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Journal of Policy Modeling xxx (2018) xxx–xxx

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Reconsidering the philanthropic foundation minimumpayout policy under a “new normal”

Zvika Afik a, Hagai Katz b,∗a Dept. of Business Administration, Guilford Glazer Faculty of Business and Management, Ben-Gurion University of the

Negev, Israelb Dept. of Business Administration, Guilford Glazer Faculty of Business and Management, Ben-Gurion University of the

Negev, POB 653, Beersheba 8410500, Israel

Received 19 July 2018; accepted 17 September 2018

Abstract

With the increasing salience of foundations in many policy fields, and recent changes in market conditions,policies towards foundations designed decades ago seem outdated. In this article we suggest reassessingfoundation payout minimums. To examine the impact of payout rates on grantmaking foundations lifespanand performance under “new normal” economics, we simulate multiple foundations lifecycles using MonteCarlo methods in diverse capital market conditions, with varied investment and payout strategies.

We find that while under past market regime perpetuity seems to be a given, under more probable future sce-narios, foundations might face increasingly early mortality and endowment depletion, limiting their potentialimpact. Furthermore, lower payout rates allow for higher lifetime grantmaking, higher mean annual grant-making, and lower giving volatility. Accordingly, we suggest a tiered payout policy, in line with foundations’missions and proper financial planning.© 2018 The Society for Policy Modeling. Published by Elsevier Inc. All rights reserved.

JEL classification: C530; H210; L310; L380

Keywords: Grantmaking foundations; Payout; Policy; Monte Carlo simulations; Investment

Introduction

Foundations have been supporting social causes for hundreds of years, and have become animportant factor in public policy making and human service funding and development in devel-

∗ Corresponding author.E-mail address: [email protected] (H. Katz).

https://doi.org/10.1016/j.jpolmod.2018.09.0040161-8938/© 2018 The Society for Policy Modeling. Published by Elsevier Inc. All rights reserved.

https://doi.org/10.1016/j.jpolmod.2018.09.004


http://www.sciencedirect.com/science/journal/01618938

mailto:[email protected]




2 Z. Afik, H. Katz / Journal of Policy Modeling xxx (2018) xxx–xxx

oped nations since the beginning of the 20th century. Public debates over their conduct in the USduring those years resulted in the Tax Reform Act of 1969, which set a 6% minimum payout, laterlowered in 1981 to 5%. In recent decades, there has been a substantial increase in the influenceand salience of foundations as well as in the accumulation of wealth in philanthropic endowmentsand foundations. Foundations have been spreading in Europe and in other areas of the worlddue to processes of privatization, inter-generational wealth transfer, and shrinking public socialexpenditures (Barbetta, Colombo, & Turati, 2015). This has rekindled various debates on theirfunctioning and regulation, including their spending rate but also their lifespan, mission relatedinvestments and more. Recent trends, and especially the dramatic impact of the 2008 economiccrisis on foundations around the world (Dietz, McKeever, Steele, & Steuerle, 2015; Foley, 2016;Havens & Schervish, 2013), along with changing perceptions of foundation management, suchas strategic philanthropy (Frumkin, 2006) and time-limited foundations (Ostrower, 2011), requirea renewed discussion of foundation practices and policies. In fact, philanthropic foundationsoperate in a new era when institutional norms are changing, and the financial environment inwhich they operate has become turbulent and precarious. And yet, underlying the discussion andresearch on philanthropic foundations to date, there has been an implicit assumption that ‘whathas been will always be.’ Accordingly, financial research on foundations mostly relies on histor-ical data. Renowned economist El-Erian (2010) warns against this assumption, and argues thatwe have entered “a new normal” where the global economic landscape has dramatically changed,and interest rates and investment returns that we saw before the global crisis are convergingto lower long-term averages. This notion is supported also by others, such as Summers (2013,2014), who predicts long term stagnation and turbulence in financial markets. The new context,then, requires research under different assumptions and using different methods. When past per-formance becomes irrelevant, research using analytical tools, such as Monte Carlo simulations,that examine varying market conditions, are more appropriate (Cooley, Hubbard & Walz, 2003;Pfau & Kitces, 2014). Therefore, in this work we study Monte Carlo simulations of payouts forfoundations with differing missions, using a range of asset portfolio allocations, under diversemarket conditions. The findings of the simulations shed a new light on the payout debate andcarry important implications for foundation policies and regulations, as well as for foundationmanagement.

1. The payout rule, its history, status, and practice

Although the origin of philanthropic foundations can be traced to ancient times (Kiger, 2000),foundations increasingly gained importance since the late nineteenth and early twentieth century,when large philanthropic foundations emerged with the aim of contributing to the public good,based on scientific principles and using extensive wealth. They became a major social institution,and influenced public policies tackling a host of social problems (Bulmer, 1995). This institution,argue Hammack and Anheier (2013), is not rigid but rather versatile, and has demonstrated bothstability and flexibility in the face of social, economic, and political change.

Following much criticism that foundations serve political interests, and that their lack of trans-parency allows them to abuse their status, the Congress passed the Tax Reform Act of 1969(Frumkin, 2006; Sansing, 2002). For the first time, private foundations were required to distributeat least 6% of their assets annually. That minimum could vary, affected by money rates andinvestment yields. Since under the economic conditions in the seventies this could mean de factorates closer to 7%, in The Economic Recovery Act of 1981, Congress changed the calculationand set minimum payout at a fixed 5% (Jagpal, 2009). According to Deep and Frumkin (2006),




Z. Afik, H. Katz / Journal of Policy Modeling xxx (2018) xxx–xxx 3

this legislation was mostly a result of political bargaining, rather than of systematic financialanalysis. Toepler (2004) too, argues that the 5% minimum payout rule was established withoutreally exploring any of the relevant fundamental issues. This minimum includes all charitablerelated expenses such as salaries and administrative costs, which typically represent about 10% ofwhat is considered “qualifying distributions” (Sansing, 2002). Despite differences in regulationsgoverning foundations between countries (Barbetta et al., 2015), a payout minimum rule is notsingularly a US policy. A 3.5% “disbursement quota” exists in Canada (Domingue, 1996). Areport comparing foundation laws in 40 European countries (EFC, 2015) finds that some requirethat foundations spend a certain amount or proportion of their income during a specified period. Adebate over a 5% “disbursement quota” has also been an issue in the British context (Leat, 2016;Pharoah & Harrow, 2010).

The logic behind the payout rule is quite straightforward. It is intended to prevent ‘endowmenthoarding’ behavior, and to guarantee that the donation is in fact distributed to support the causefor which it was given. Plus, the donation that constitutes the foundation is slowly disbursed overmany years, while the tax benefits for the donation are given today. Thus, there is a time gapbetween the time the charitable deduction is made and the time the future distributions for publicbenefit are dispensed (Sansing & Yetman, 2006). Therefore, current tax payers ‘pay’ now forcharity distributed throughout the next few generations, or even in perpetuity (Klausner, 2003).Foundations that fail to meet the minimum payout are penalized by an increased excise tax (2%instead of 1%).

Some commissioned research was done about the minimal payout rule and whether it shouldbe changed. The National Network of Grantmakers’ report presents clear arguments for increas-ing the payout policy by a “1% more for democracy” (Mehrling, 1999). The report contends thateven if private foundations had paid as much as 8% between 1974 and 1995, they would retaintheir endowments. The National Committee for Responsive Philanthropy published a documentsuggesting foundations should pay out at least 6% (Jagpal, 2009). On the other hand, a studycommissioned by the Council of Foundations (Harrison, 1999) concludes, after studying differ-ent levels of payout, that the 5% rule is optimal in order to maintain the value of the endowmentand maximize how much is actually distributed. A study sponsored by the Council of Michi-gan Foundations (Cambridge Associates, 2000) concludes that the 5% mark (when inflation isadjusted) is already too high and cannot guarantee the perpetuity of endowments. A FoundationCenter guide to understanding payout (Renz, 2012) shows that most large endowed foundationsgave more than the 5% minimum and that about 20% of those foundations gave more than 10%each year. It also finds a link between a higher foundation size and a lower payout rate.

Besides these commissioned studies, little academic research was published on the matter.Deep and Frumkin (2006) show that most foundations’ actual payout converges around the 5%mark. They also analyze arguments whether to increase or maintain the current level of payout andconclude that the status quo is becoming less viable (see also Irvin, 2007). Other studies (Sansing,2002; Sansing and Yetman, 2002, 2006) of broader samples of foundations find that for largefoundations, an average 1.29$ is distributed for every dollar required legally, and that foundationswith less income and higher expenses will usually be the ones that will try to minimize payout.In a study of 290 foundations between 1972–2006, Deep and Frumkin (2006) find that mostfoundations have a flat payout rate of approximately 5%, regardless of other considerations suchas market conditions, changing costs or their mission and time horizon. Recent data show than notmuch has changed. Afik, Levy, and Katz (2018) analyze 2006–2010 National Center of CharitableStatistics data of 500 grantmaking foundation (faithfully representing a larger sample of 12,190foundations), and find an average 7.2% and median 5.1% payout rates. Research into causes





of payout rates leaves much to be answered. Brown, Dimmock, Kang, and Weisbenner (2010)analyze university endowments and find “endowment hoarding” behavior, where endowmentspay out less following hard years but do not pay out more after good years. In Finland, historicalfinancial performance was a weak predictor of payout rates (Aalto, 2016).

According to Hamilton (2011), foundation officers are not sufficiently aware of the payoutdilemma, and if they are, they fail to understand that increased payout can shorten the endowmentslifespan and consequently reduce the total giving of the foundation throughout its life. More recentdata show that the 5% payout minimum has become the default, practiced by most foundations(Afik, Levy, & Katz, 2018; Dietz et al., 2015; Hamilton, 2011; Renz, 2012). This default practice isopposed to a problem-driven approach that suggests adjusting payout to the distinct and sometimesdynamic trajectory of different social problems. It is also incompatible with changes in socialneeds resulting from economic booms or downturns. AllianceBernstein (2010) suggest aligningpayout and investment strategy to the foundation’s mission. To assess a foundation performance,their report introduces the concept of Total Philanthropic Value (TPV) which adds together thefoundation’s cumulative distributions over a period of 30 or 50 years and its remaining assets atthe end of the period. Though AllianceBernstein’s model remains opaque, it seems rather rich,based on methodical simulations using a variety of historical financial data over a long time. TheAllianceBernstein report focuses mainly on foundations that pay-out no less than 5% on average,exploring smoothing formulas (i.e. basing the payout on asset average over N trailing recent years,N = 0, 1, 2, . . .) with and without floors and ceilings, generally showing that a 3–5 year smoothingrule is optimal in most cases. This allows more regular levels of distribution without materiallyaffecting the TPV. Without smoothing, giving behaves pro-cyclically, thus reducing giving duringdownturns when need is greater, and increasing giving when the economy recuperates (Dietzet al., 2015). Deep and Frumkin (2006) suggest cancelling mandated payout rates altogether toallow foundations to choose payout rates that fit their missions best. Toepler (2004), suggestsrequiring minimum payout only until the foundation gave enough to the public cause that itsupports, and eliminating payout requirements after the foundations grantmaking reaches the taxbenefits embodied in its assets.

In light of this, we argue that it is essential to reexamine foundation payout in the context ofthe ‘new normal.’ We perform Monte Carlo simulations of payouts for foundations with differingmissions, using a range of asset portfolio mixes, under diverse market conditions. The followingsections of this article describe the methodology chosen for the analysis, present the findings ofthe simulations, and conclude with an analysis of the implications of the findings on foundationpayout policies and regulations.

2. Methods

Obviously, not all foundations are the same. Their payout, their investment mix, and theirlifespan vary. To allude to the differences and at the same time restrict this work to a single paper,we analyze imaginary cases of two foundations, each represents an architype. Naturally, otherchoices are valid and could provide further insights. We shall leave these for future research.

Foundation A is a corporate foundation established by a money management firm known forthe speculative nature of its investments. Founded primarily as a public image instrument toimprove the firm’s reputation, the endowment aims to support education and research to fightdrug and alcohol abuse, with an initial one-time donation of $100M. The expressed policy of thefoundation, managed by a board committee of the firm, is to retain the endowment perpetually,by limiting its grantmaking to retain the original endowment in real terms.





Foundation B was established following conversion into for-profit of a major state’s nonprofitchildren’s hospital chain. With seed endowment of $1Billion, the foundation is dedicated tofinancing the hospitals and to developing new child healthcare services and solutions, and ismanaged by a state-appointed public committee. Seeing that its mandate is both to sustain anexisting system of hospitals and to develop new services and solutions to meet new challengesand utilizing emergent technologies, foundation B’s giving policy is explicitly perpetual, whileannual disbursement must be no less than a minimum of 80% of the first year’s contributions.

The foundation money amounts are chosen to reflect typical sizes and for exposition conve-nience. Details of the two foundation simulations are presented below. We use inflation-adjustedparameters throughout, hence all our results reflect real values. Since taxation varies betweenjurisdictions, we do not include taxes in our calculations.

In this work, to maintain consistency with recent literature, we use a prior established assetmodel and three market scenarios, similar to Pfau and Kitces (2014). Our simulation buildingblock is the geometrical Brownian motion (GBM) which is a random process that is widely usedin finance including the famous Black and Scholes (1973) formula and contemporary literature. Itcan easily be extended to portfolios of more than two assets (see for example Glasserman, 2004).Yet, for consistency with the spirit and theme of the relevant literature, without loss of generality,we use two assets to form portfolios. We express the two assets’ prices by the following equation:

Si,T = Si,0e

(μi−0.5σ2

i

)T +σiWi(T )

, (1)

where:i ∈ {1, 2} is the asset identifier.Si,0 and Si,T is the price of asset i at time 0 and T respectively.μi is the deterministic drift rate of process i.σi is the deterministic volatility of process i.Wi (T ) is a standard Wiener process i: Wi (T ) = εi

√T , where �i ∼ N(0,1) iid, and ρ = corr(ε1,

ε2) is the correlation between the two normal random variables εi.(Note: we use the common terminology and designation: �i ∼ N(0,1) means that εi is normally

distributed with zero mean and variance equals one; iid means that εi draws are independent andidentically distributed).

The two assets represent two large diversified portfolios, an equity index (S&P500) and a fixedincome index (intermediate-term U.S. government bonds). Table 1 presents their model parame-ters for the three market scenarios, which are identical to those used by Pfau and Kitces (2014) forcomparability and tractability with prior literature. Scenario 1 is simply based on historical param-eters for the years 1926–2011. Pfau and Kitces (2014) use historical real return averages found inIbbotson Stocks, Bonds, Bills, and Inflation yearbook. Scenario 2 uses the real returns assumptionsprepared by Harold Evensky for the MoneyGuidePro software of July 2013 (MoneyGuidePro is apopular financial planning software package, see http://www.moneyguidepro.com/ifa for exam-ple. Harold Evensky is regarded one of the leading financial planners in the US). Scenario 3represents “a new normal” (El-Erian, 2010) of low interest rates, similar to the present economicenvironment which might prevail for long periods. The three scenarios are long-term in nature,corresponding to the relatively long-term nature of foundations, and even the most “optimistic”scenario of market 1 includes periods of economic turmoil, especially the big depression and therecent financial crisis, starting 1929 and 2007 respectively.

Since we analyze long-term fund “life” scenarios we use periods of T = 1 year for the timeincrement. A fund pays its gross annual donation at the beginning of the year according to its


http://www.moneyguidepro.com/ifa




Table 1Model parameters for three market regimes, for equity and bonds in real values (inflation adjusted).

Market no. 1, historical market data during the years 1926–2011

Average returns Returns volatility Correlation

Stocks 8.59% 20.70% 0.1Bonds 2.56% 6.50%

Market no. 2, MoneyGuidePro scenario (Evensky assumptions)


Stocks 5.50% 20.70% 0.3Bonds 1.75% 6.50%

Market no. 3, lower future returns (Pfau & Kitces, 2014)


Stocks 5.10% 20.00% 0.1Bonds 0.30% 7.00%

specific payout scenario and then balances its asset portfolio according to its fixed asset mix. Thegross annual donation includes all the fund outlays for the year, including its operating expenses,salaries, etc. The model could be easily extended to shorter periods, such as quarterly or monthly,yet, similar to prior literature, for our paper the use of annual periods seems adequate, and ahigher resolution analysis would not change the results and conclusions materially. The scenariopath then steps one-year forward with adequate financial gains and losses to the beginning ofthe next year. To study “perpetual” foundations we use a simulation horizon of 100 years, andexamine the residual asset value at the start of year 101. We conjecture that a longer horizon isirrelevant since the market, the underlying assumptions, and the donor’s goal would not hold fora period longer than a few dozen years. As well, a 100-year horizon provides a comprehensiveview, insight, and understanding of a fund performance, risks, and merits. Longer horizons wouldincrease the computation time and data volume linearly while adding only marginally, if at all, tothe foundation’s performance evaluation.

For a rather accurate expected (mean) value of a performance measure, 10,000 simulationpaths are adequate. However, for risk analysis purposes extreme outcomes of tail (rare) events areoften of interest, therefore we simulate 100,000 paths for each market regime and each investmentmix and payout rule combination. To enhance comparability between alternative choices of theendowments and to enable reproduction of the analysis we generate and save time series of εi’s thatare repeatedly used in this work. For each market regime we have 100,000 vector pairs of randomlydrawn εi’s, standard normal iid, each 100 elements long, with the appropriate correlation. Usingthe parameters of Table 1 and the model of Eq. (1) we compute and save asset return pairs forthe three market regimes, a total of 2 assets × 100 years long × 100,000 paths × 3 markets. Theseasset-return simulated paths are then used for the analysis of the two foundations.

Foundation A is a classic example and resembles many actual foundations, including uni-versity endowments. Typically, such an endowment pays 5% of its recent three-year asset valueaverage and often invests 60% of its money in equity. We consider 5% payout and 60% equity asa demonstrative example for a lawful and prudent foundation following current IRS payout min-imum and common asset management and investment practices, like those found by Arnsberger(1998), Breeze (2008), and Salamon, (1992). We aim to reexamine these “golden” rules as they





might be outdated and affected by survival bias, by “the flaw of averages” (Savage, 2009), and byhistorical market returns. We do not ignore averages in this work, yet we complement the meanwith additional statistics, as averages have many flaws and could be dangerously misleading. Weexamine 25 combinations of five payout rates and five investment mixes, specifically 3, 4, . . ., 7%payout rates and 30, 40, . . ., 70% equity shares (in each, the remaining portfolio share includesbonds). For each combination we calculate a set of measures which includes: (1) residual portfoliovalue at the end of year 100; (2) total life donation amount, summing the outlays of the entire100-year path; (3) average donation per path, averaging yearly outlays of the entire 100-year path;and (4) donation standard deviation per path as a measure of giving volatility. For each measurewe calculate the following descriptive statistics: minimum, maximum, median, average, standarddeviation, skewness, excess kurtosis, and 1, 5, 10, 90, 95, and 99th percentiles.

Foundation B is quite similar to foundation A with one seemingly minor technical difference: itfunds the operation of a chain of hospitals and thus commits to a minimal annual funding amount.For our analysis, we set this minimum level at 80% of the first-year outlay, which changesaccording to the specified payout rate. For example, for a payout rate of 5%, the minimum annualdonation is $40M ($1B × 5% × 80%). Unlike foundation A which could theoretically live forever,adjusting its outlay proportionally to its asset value, foundation B is committed to a minimal annualdonation regardless of its asset value. This might cause the depletion of its assets even within thefirst 10 years of its operation! Hence, for foundation B we collect an additional performancemeasure, years to depletion, the number of years since the start of the foundation until its minimalannual payment exceeds its residual asset value.

3. Findings

The simulations described in the methodology section and their quantitative analysis resultin many tables and charts, much beyond the scope of a journal article. Hence, we include in thepaper only select representative examples (our fuller set of results is available on request).

The simulations for the classic, ‘mainstream’ example of foundation A (Fig. 1), tell two cau-tionary tales. Firstly, by looking at the left column of charts in Fig. 1, it is clear that the changingfinancial climate significantly affects the foundations’ assets. These three charts show the averageresidual value of the foundation’s endowment after 100 years of operation at different combi-nations of payout and portfolio mixes. Consider that foundation A’s starting endowment was$100Millions. When simulated under the assumptions of past returns (market 1), the averagefoundation will survive the 100-year life-term in our analysis, and will even end up with a largerendowment (on average) under most payout and portfolio mix combinations (Fig. 1a). For exam-ple, the average foundation with 5% payout and a 60% equity portfolio (and 40% bonds), willhave $325.3M in its coffers in the beginning of year 101. Under such assumptions, perpetuityseems to be a given (if one trusts averages). However, under the more realistic future scenariosof markets 2 and 3, perpetuity is hardly the norm (Fig. 1c and e). Only low payouts and high-riskportfolios will result in such favorable results. The average foundation with 5% payout and a 60%equity portfolio, will end up with only $33.3M in its endowment under market 2 and just $13.7Munder market 3. That is much below the starting point of $100M.

Secondly, averages are often misleading. Many of us tend to associate random outcomeswith the normal distribution. However, many phenomena in financial markets are not distributednormally. In a skewed distribution averages and medians are not identical and could be materiallydifferent from each other. This is what we actually observe in the right column of charts of Fig. 1,depicting the medians that pair with the left column charts of the figure. Considering that 50%





Fig. 1. Foundation A, paying a fixed rate of its assets’ 3-year average, its starting value is $100M: average remainingvalue (left column) and median remaining value (right column) after 100 years, under three different markets. Each pointrepresents 100,000 simulation paths. Numbers are in million dollars (adjusted for inflation).

of the simulated foundations perform worse than the median, clearly the relatively rosy picturedepicted by the averages is strongly affected by a subset of very “lucky” simulated foundations.However, the results of these foundations do not represent the majority of the possible outcomes,which are equally likely.

When we examine the median remaining endowment for foundation A (Fig. 1b, d and f), theassets deplete dramatically as market conditions deteriorate. While under market 1 the medianfoundation with 5% payout and a 60% equity portfolio is worth $112.4M after 100 years, under





Fig. 2. Foundation A, paying a fixed rate of its assets’ 3-year average, its starting value is $100M: total donation during100 years (on the left), total donation during 100 years (on the right), both under market 3. Each point represents 100,000simulation paths. Numbers are in million dollars (adjusted for inflation).

market 2 it is worth $10.6M and under market 3 it has only $5.1M left in its endowment. What thismeans is that while past market returns allowed most foundations to remain viable and capableof promoting their missions after 100 years of operation, under recent and more realistic marketconditions even prudent foundations would likely be near closing their operations. Under market2 only half of the endowments of foundations paying 3% annually and invested in 70% equitywould not be depleted, and under market 3 all the median foundations in our simulations do notretain their original $100M asset value. Furthermore, prudent planning cannot rely on averagesand even 50% chance of medians shows an optimistic view of the possible outcomes, as the oddsare that 50% of the endowments would perform worse than the median. Indeed, under market 3,the 10th percentile remaining endowments, for all investment mixes, barely pass $8M even witha 3% payout rate. The 10th percentile remaining asset value declines steeply for higher payoutrates, and it is almost zero for 6% and 7% payout rates (see Fig. 2a). Furthermore, total lifetimegiving, under even the most favorable policies, is most likely to be lower than the foundation’sstarting endowment value (Fig. 2b), i.e. in these circumstances the financial investment destroysvalue (in real terms).

The top two charts of Fig. 3 show the average total grantmaking amount that foundation Awill give over 100 years under specified payout rates and portfolio mixes. On the top left (Fig. 3a)we can see that this amount increases the higher the risk the foundation managers take in theirinvestments. But more importantly, substantially higher lifetime grantmaking is evident whenpayout rates are lower. This is not surprising if one considers that high risk is usually accompaniedby higher returns, and lower payout rates allow for the endowment to accumulate more assets,as is also seen in Fig. 1a. We chose to show this under the less optimistic and more cautiousassumptions of market 3. Fig. 3b on the top right presents average total donation over 100 years,comparing the three markets for a foundation with a 60% equity in its portfolio. It again showshow contemporary assumptions generate much lower performance in terms of foundation size andoutlays, relative to historical performance, and that higher payout rates result in less giving. Yet,despite the fact that the change in total grantmaking with changing payout rates is less dramaticunder markets 2 and 3, when compared to market 1, a 3% payout rate results in $50M more in totallifetime grantmaking than does a 5% payout rate in market 3 ($228M vs. $178M, respectively),which are 28% more dollars to support drug and alcohol abuse programs. This also means adeclining mean annual grantmaking of the foundation when payout rate increases (Fig. 3c).





Fig. 3. Foundation A, paying a fixed rate of its assets’ 3-year average, its starting value is $100M: average total donationduring 100 years under market 3 (top left), average total donation during 100 years with 60% equity portfolio underthree different markets (top right), average mean and standard deviation (volatility) of annual donation per life path of100 years (bottom left and bottom right, respectively). Each point represents 100,000 simulation paths. Numbers are inmillion dollars (adjusted for inflation).

Another indicator of endowment performance − grantmaking volatility − is shown in Fig. 3d.Increased grantmaking volatility means more financial uncertainty for the foundation’s grantees,and consequently can limit the capacity of the organizations that depend on these grants to provideservices to their clients. Volatility is also negatively affected by increased payout. In general,grantmaking volatility (expressed here in average annual giving standard deviation in $Millions)increases with payout rate (foundations invested in 70% equity show a different pattern at lowerpayout rates). Thus, lower payout rates generally mean better predictability of foundation support,and less uncertainty for nonprofit organizations enjoying such support.

The picture that emerges from the study becomes more challenging when we study foundationB, whose mission introduces an important restriction. Foundation B’s mission includes the fundingof a chain of hospitals, hence it is required to provide an annual giving of no less than 80% of itsfirst year’s outlays.

As seen in Fig. 4, the cautious assumptions of market 3 (“a new normal”) do not bode wellfor foundation B. Firstly, longevity is seriously compromised. Fig. 4a on the top left shows thaton average foundation B would not reach 100 years of operation under any payout and portfoliocombination. While changes in the portfolio do not make a big difference to foundation B’slongevity, payout rates certainly do. Even when paying out a fixed 3%, the foundation will run





Fig. 4. Foundation B, paying a fixed rate of its assets’ 3-year average, with a minimum of 80% of first year giving, itsstarting value is $1B, all under market 3: average longevity (years to endowment depletion, (a)), total lifetime givingduring 100 years (average (b), 10th percentile (d)), surviving endowment (percentage) at end of year 100 (c), averageassets of surviving endowment after 100 years (e), and standard deviation (volatility) of annual donation per life pathof 100 years (f). Each point represents 100,000 simulation paths. Numbers are in billion dollars (adjusted for inflation),except for (c) (percents).

out of money after sixty to seventy years on average. However, when following the currentlyrequired 5% minimum payout, longevity will decrease to thirty to forty years only. Focusingon the surviving endowments at the end of 100 years of operation, Fig. 4e shows their averageresidual asset value in billion dollars. Notably, there is no data for foundations with low equity





portfolios and high payout rate, simply because none survive to be 100 years old. Furthermore,we can see that unless foundation B managers choose higher-risk portfolios and low payout rates,the average foundation will erode its endowment below its original $1Billion value. It I importantto note, that these are the results of the surviving foundations only, as under this scenario mostfoundation do not make it to be 100 years old. In fact, the number of surviving foundation at year101 can be as little as two or three foundations out of 100,000. The highest survival rate is offoundations paying out 3% with a 70% equity portfolio, at a 40% survival rate (Fig. 4c). Thisrate dropes dramatically for higher payouts and it is merely 5.1% for foundations following thecurrent payout norm of 5%.

Not only do low payout rates mean greater longevity, they also mean greater giving capacity,and consequently, greater impact potential. Fig. 4b shows that total lifetime grantmaking increaseson average with higher equity-to-bonds ratios in the portfolio and lower payout rate. Lower payoutrates do increase the year-to-year volatility of the average foundation’s giving (Fig. 4f), yet in thecase of foundation B this means that in some years the disbursement would be (much) higher thanits minimal required level, which could allow for certain capital expenditures above the routineoperation level. In sum, in this scenario, endowment depletion rates are alarming and rise withpayout rates. Cautious low risk investments mean, on average, an early death (Fig. 4a) and a lowerlife-time total grantmaking (Fig. 4b). While this is reversed for the 10th percentile foundation(Fig. 4d), a low equity portfolio mix improves donation only slightly. The lowest decile results arealarming, showing total lifespan grantmaking lower than the beginning endowment of $1billionfor all payout-investment combinations.

4. Discussion

Seeing their roles and growing salience in many policy fields (Bulmer, 1995; Fleishman, 2007;Hammack & Anheier, 2013; Kiger, 2000), and the dramatic changes in market conditions in thelast decade (e.g. El-Erian, 2010), it would be wise to reconsider policies that govern grantmakingphilanthropic foundations. We address here one much contested policy, which requires foundationsto give at least 5% of their endowment each year. The minimum payout regulation was set undervery different financial circumstances than today’s economic climate. The realization that thecurrent conditions are not transient, and low interest rates and market fluctuations are here to stay(e.g. Summers, 2013, 2014), should influence us to reassess this policy.

To examine the impact of different payout rates on the lifespan and performance of grantmakingfoundations under changing financial assumptions, we generate a large set of simulated founda-tions life cycles using Monte Carlo methods under three different capital market conditions, withvaried portfolio mixes and payout rates. We apply this methodology on two hypothetical foun-dations. Since these two cases probably represent many real foundations (particularly foundationA), their results provide important insights into the influence of payout decisions on future perfor-mance in a changing economic environment. We find that when we assume continuation of pastmarket regime, perpetuity seems to be a given. However, under more realistic future scenarios,perpetuity is hardly the norm. Foundations similar to our foundation A, on average, would retaintheir endowment value for 100 years only when their managers would choose low payouts, whichdo not commensurate with the accepted norms of foundation management today. Under morecurrent market conditions, foundations would likely face relatively early endowment depletionand even mortality (when assets are too low to support a viable donation and its own operations).Past market returns allowed most foundations to remain viable and able to promote their missionsfor at least 100 years. However, under recent and more realistic market scenarios, even the most





prudent and lawful foundations will be closing their operations prematurely. Under market 3 allthe foundations in our simulations for the typical foundation A are depleted, on average, belowtheir original $100M, and their 10% lowest percentile reach 100 years of operations with only$8M asset value using 3% payout and a miniscule residual value, below $1M, using “the required”payouts of 5% (or higher). Furthermore, lower payout rates are associated with higher lifetimegrantmaking and higher mean annual grantmaking, and grantmaking is also generally less volatileunder low payouts.

When the foundation’s mission imposes some restrictions, as does foundation B’s mission,results are even less favorable. Longevity is seriously compromised, and on average the foundationwould not survive more than 40 years under any payout and portfolio combination. In this scenario,asset depletion rates are alarming and rise with payout rates.

In light of these findings, we argue that perpetual foundations, retaining their initial valueforever, seem unrealistic and that minimum payout requirements need to be reviewed. Despite theincreased debate over limited-life foundations and the argument that they are becoming the “newtraditional” approach (Hamilton, 2011), perpetuity is still the majority and the default choice inmost cases (Renz & Wolcheck, 2009), even among family foundations, where spend downs arebecoming more frequent (Boris, De Vita, & Gaddy, 2015). Policies too, treat perpetual foundationsas the norm, and in some European nations sunsetting is even not legal (EFC, 2011). In light of that,current minimum payout requirements policies seem highly inappropriate, since as our findingsshow, they seriously jeopardize perpetuity and hamper longevity.

Barbetta et al. (2015) note the large heterogeneity between foundations, and assert that newregulations should consider need for a more refined model of regulation of these organizations. Inline with their assertion, if canceling minimum payout requirements altogether is not politicallyfeasible, we argue that at the very least policymakers should contemplate requiring divergentminimums for different types of foundations, based on their missions. We suggest a tiered payoutminimum policy, where different levels of payout would be required for different foundationsbased on expressed mission and relevant financial planning. Thus, for a foundation whose missionis clearly long-term and the trajectory of the social issue it addresses calls for increasing investmentover decades, according to our analysis a low payout rate, possibly 3–4% annually, is mostsuitable. Consider for example a foundation dealing with reduction of ocean pollution or mitigatingdroughts in arid regions. The repercussions of these challenges will be felt over decades and evencenturies, and are likely to increase with over-population and global warming. A somewhat similar,though less dramatic example, would be a scholarship fund, aimed to provide financial aid to low-income students. Regretfully, it is unlikely that low-income students will disappear in the nearfuture, and thus, most probably, the foundation’s scholarships would be needed for many years.Conversely, a foundation dealing with a current crisis, which is expected to explode imminently,would be more suitable for a 5% or higher payout, or potentially a planned sunsetting process.Other reasons for adopting a higher payout rate also exist, such as a donor’s preference to giveand make an impact throughout her lifetime.

We do not argue that judging the results of the payout minimum by crunching numbers shouldbe the singular criterion when reconsidering foundation payout minimum policies. Many differentconcerns drove the decision to set a payout minimum. Intergenerational tax fairness is one suchconcern (Klausner, 2003; Sansing & Yetman, 2006). Obviously, lowering the minimum payoutrate does not address this matter. On the other hand, policy makers may consider a differentlook on this issue, based on the idea of sustainability. We believe that our generation shouldbe held accountable for the social problems that its decisions generate for our children and our





grandchildren. Paying today’s tax benefits for endowments that will benefit future generations isone way to act on this responsibility. It is a lot like saving money for our child’s college education.

In this paper we ignore replacement ratio: how many new foundations are established toreplace those that are disappearing?1 Our study is focused on the micro level, and examineshow financial climate, payout rates and asset management affect an individual foundation. Whileit has implications for the entire population of foundations, it is not an overall analysis of thefoundation sector. While our findings clearly suggest revisiting payout rate minimum policies,policy should consider not just changing requirements for existing foundations to ensure longevityand increase lifetime giving amounts, but also providing better incentives for the establishmentof new foundations to replace those that run dry.

Finally, these findings should draw the attention of foundation managers and trustees as well.Perpetuity is obviously not feasible in the current financial climate and under the current requiredminimum payout. Therefore, unless policies and regulations will change, all foundations need toconsider adopting the best practices of limited-term foundations. That means planning for exit,and thinking strategically about how best to implement spending to maximize impact, in line withthe foundation’s mission and the nature of the social issues each foundation deals with.

Acknowledgements

In memory of Prof. Simon Benninga who has sparked our interest in this research topic. Simondied from a severe illness on 29 August 2015.

The study was supported by a small grant from the Israeli Center for Third-sector Research(ICTR), Ben-Gurion University of the Negev.

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Reconsidering the philanthropic foundation minimum payout … · 2019-03-24 · and inﬂuenced public policies tackling a host of social problems (Bulmer, 1995). This institution,