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Title: Reconsidering the philanthropic foundation minimum payout policy under a “new normal”

Corresponding author: Hagai Katz, Dept. of Business Administration, Guilford Glazer Faculty of Business and Management, Ben-Gurion University of the NegevAddress: Dept. of Business Administration, Ben-Gurion University of the Negev, POB 653, Ben-Gurion University of the Negev, Beersheba, Israel 84105E-mail: katzh@bgu.ac.il

Other authors: Zvika Afik, Dept. of Business Administration, Guilford Glazer Faculty of Business and Management, Ben-Gurion University of the Negev (first author)

Time schedule of submission:Received: July 19, 2018Revised: Accepted: Sept 17, 2018

Publication Type:Full Length Article (FLA)

Editorial Composition:

Number of Pages: 33Number of Figures: 4Number of Tables: 1

Editorial Office Note: Please make sure that Zvika Afik is listed as first author

Approved by: Date:

Dreve Lansrode, Rhode St. Genese, Belgium 1640E-mail: Editor@EconModels.com

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Reconsidering the philanthropic foundation minimum payout policy under a “new normal”  

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

Katz Hagai,Dept. of Business Administration, Guilford Glazer Faculty of Business and Management, Ben-Gurion University of the NegevPOB 653, Beersheba, Israel 8410500katzh@bgu.ac.il+972-54-4966851

AcknowledgementsIn memory of Prof. Simon Benninga who has sparked our interest in this research topic. Simon died 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 policy under a “new

normal”  

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outdated.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 reassessing foundation payout minimums. To examine

the impact of payout rates on grantmaking foundations lifespan and performance under

“new normal” economics, we simulate multiple foundations lifecycles using Monte Carlo

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 scenarios, foundations might face increasingly early mortality and

endowment depletion, limiting their potential impact. Furthermore, lower payout rates

allow for higher lifetime grantmaking, higher mean annual grantmaking, and lower

giving volatility. Accordingly, we suggest a tiered payout policy, in line with

foundations’ missions and proper financial planning.

Keywords: grantmaking foundations; payout; policy; Monte Carlo simulations;

investment

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Foundations have been supporting social causes for hundreds of years, and have

become an important factor in public policy making and human service funding and

development in developed nations since the beginning of the 20th century. Public debates

over their conduct in the US during those years resulted in the Tax Reform Act of 1969,

which set a 6% minimum payout, later lowered in 1981 to 5%. In recent decades, there

has been a substantial increase in the influence and salience of foundations as well as in

the accumulation of wealth in philanthropic endowments and foundations. Foundations

have been spreading in Europe and in other areas of the world due to processes of

privatization, inter-generational wealth transfer, and shrinking public social expenditures

(Barbetta, Colombo, & Turati, 2015). This has rekindled various debates on their

functioning and regulation, including their spending rate but also their lifespan, mission

related investments and more. Recent trends, and especially the dramatic impact of the

2008 economic crisis on foundations around the world (Dietz, McKeever, Steele, &

Steuerle, 2015; Foley, 2016; Havens & Schervish, 2013), along with changing

perceptions of foundation management, such as strategic philanthropy (Frumkin, 2006)

and time-limited foundations (Ostrower, 2011), require a renewed discussion of

foundation practices and policies. In fact, philanthropic foundations operate in a new era

when institutional norms are changing, and the financial environment in which they

operate has become turbulent and precarious. And yet, underlying the discussion and

research on philanthropic foundations to date, there has been an implicit assumption that

‘what has been will always be.’ Accordingly, financial research on foundations mostly

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relies on historical data. Renowned economist Mohamed El Erian (2010) warns against

this assumption, and argues that we 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 converging to 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

performance 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 for foundations with differing missions, using a range of

asset portfolio allocations, under diverse market conditions. The findings of the

simulations shed a new light on the payout debate and carry important implications for

foundation policies and regulations, as well as for foundation management.

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

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not rigid but rather versatile, and has demonstrated both stability and flexibility in the

face of social, economic, and political change.

Following much criticism that foundations serve political interests, and that their

lack of transparency 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 distribute at least 6% of their assets annually. That

minimum could vary, affected by money rates and investment yields. Since under the

economic conditions in the seventies this could mean de facto rates closer to 7%, in The

Economic Recovery Act of 1981, Congress changed the calculation and set minimum

payout at a fixed 5% (Jagpal, 2009). According to Deep and Frumkin (2006), this

legislation was mostly a result of political bargaining, rather than of systematic financial

analysis. Toepler (2004) too, argues that the 5% minimum payout rule was established

without really exploring any of the relevant fundamental issues. This minimum includes

all charitable related expenses such as salaries and administrative costs, which typically

represent about 10% of what is considered “qualifying distributions” (Sansing, 2002).

Despite differences in regulations governing foundations between countries (Barbetta, et

al, 2015), a payout minimum rule is not singularly a US policy. A 3.5% “disbursement

quota” exists in Canada (Domingue, 1996). A report comparing foundation laws in 40

European countries (EFC, 2015) finds that some require that foundations spend a certain

amount or proportion of their income during a specified period. A debate over a 5%

“disbursement quota” has also been an issue in the British context (Leat, 2016; Pharoah

& Harrow, 2010).

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The logic behind the payout rule is quite straightforward. It is intended to prevent

‘endowment hoarding’ behavior, and to guarantee that the donation is in fact distributed

to support the cause for which it was given. Plus, the donation that constitutes the

foundation is slowly disbursed over many years, while the tax benefits for the donation

are given today. Thus, there is a time gap between the time the charitable deduction is

made and the time the future distributions for public benefit are dispensed (Sansing &

Yetman, 2006). Therefore, current tax payers ‘pay’ now for charity 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 should be changed. The National Network of Grantmakers’ report presents clear

arguments for increasing the payout policy by a “1% more for democracy" (Mehrling,

1999). The report contends that even if private foundations had paid as much as 8%

between 1974 and 1995, they would retain their endowments. The National Committee

for Responsive Philanthropy published a document suggesting foundations should pay

out at least 6% (Jagpal, 2009). On the other hand, a study commissioned by the Council

of Foundations (Harrison, 1999) concludes, after studying different levels of payout, that

the 5% rule is optimal in order to maintain the value of the endowment and maximize

how much is actually distributed. A study sponsored by the Council of Michigan

Foundations (Cambridge Associates, 2000) concludes that the 5% mark (when inflation is

adjusted) is already too high and cannot guarantee the perpetuity of endowments. A

Foundation Center guide to understanding payout (Renz, 2012) shows that most large

endowed foundations gave more than the 5% minimum and that about 20% of those

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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 and conclude 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 large foundations, an average 1.29$ is distributed for

every dollar required legally, and that foundations with 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 most foundations have a flat

payout rate of approximately 5%, regardless of other considerations such as market

conditions, changing costs or their mission and time horizon. Recent data show than not

much has changed. Afik, Levy and Katz (2018) analyze 2006-2010 National Center of

Charitable Statistics data of 500 grantmaking foundation (faithfully representing a larger

sample of 12,190 foundations), 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, & Weisbenner (2010) analyze university endowments and find “endowment

hoarding” behavior, where endowments pay out less following hard years but don’t pay

out more after good years. In Finland, historical financial performance was a weak

predictor of payout rates (Aalto, 2016).

According to Hamilton (2011), foundation officers are not sufficiently aware of the

payout dilemma, and if they are, they fail to understand that increased payout can shorten

the endowments lifespan and consequently reduce the total giving of the foundation

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throughout its life. More recent data 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 is opposed to a problem-driven

approach that suggests adjusting payout to the distinct and sometimes dynamic trajectory

of different social problems. It is also incompatible with changes in social needs resulting

from economic booms or downturns. AllianceBernstein (2010) suggest aligning payout

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 the foundation’s cumulative distributions over a period of 30 or 50 years and its

remaining assets at the 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. The AllianceBernstein 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 smoothing rule is optimal

in most cases. This allows more regular levels of distribution without materially affecting

the TPV. Without smoothing, giving behaves pro-cyclically, thus reducing giving during

downturns when need is greater, and increasing giving when the economy recuperates

(Dietz et al., 2015). Deep and Frumkin (2006) suggest cancelling mandated payout rates

altogether to allow foundations to choose payout rates that fit their missions best. Toepler

(2004), suggests requiring minimum payout only until the foundation gave enough to the

public cause that it supports, and eliminating payout requirements after the foundations

grantmaking reaches the tax benefits embodied in its assets.

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In light of this, we argue that it is essential to reexamine foundation payout in the

context of the ‘new normal.’ We perform Monte Carlo simulations of payouts for

foundations with differing missions, using a range of asset portfolio mixes, under diverse

market conditions. The following sections of this article describe the methodology chosen

for the analysis, present the findings of the simulations, and conclude with an analysis of

the implications of the findings on foundation payout policies and regulations.

Methods

Obviously, not all foundations are the same. Their payout, their investment mix,

and their lifespan 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, other choices 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 for the speculative nature of its investments. Founded primarily as a public image

instrument to improve the firm’s reputation, the endowment aims to support education

and research to fight drug and alcohol abuse, with an initial one-time donation of $100M.

The expressed policy of the foundation, 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 nonprofit children’s hospital chain. With seed endowment of $1 Billion, the

foundation is dedicated to financing the hospitals and to developing new child healthcare

services and solutions, and is managed by a state-appointed public committee. Seeing that 10

its mandate is both to sustain an existing system of hospitals and to develop new services

and solutions to meet new challenges and utilizing emergent technologies, foundation B’s

giving policy is explicitly perpetual, while annual 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 convenience. Details of the two foundation simulations are presented below.

We use inflation-adjusted parameters throughout, hence all our results reflect real values.

Since taxation varies between jurisdictions, we do not include taxes in our calculations.

In this work, to maintain consistency with recent literature, we use a prior

established asset model and three market scenarios, similar to Pfau & Kitces (2014). Our

simulation building block is the geometrical Brownian motion (GBM) which is a random

process that is widely used in finance including the famous Black and Scholes (1973)

formula and contemporary literature. It can 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:

(1)

where:

is the asset identifier.

and is the price of asset i at time 0 and T respectively.

is the deterministic drift rate of process i.

is the deterministic volatility of process i.

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is a standard Wiener process i: , where εi~N(0,1) iid, and

= corr(ε1, ε2) is the correlation between the two normal random variables εi. (Note: we use the common terminology and designation: εi~N(0,1) means that εi is normally distributed

with zero mean and variance equals one; iid means that εi draws are independent and identically

distributed)

The two assets represent two large diversified portfolios, an equity index (S&P500)

and a fixed income index (intermediate-term U.S. government bonds). Table 1 presents

their model parameters for the three market scenarios, which are identical to those used

by Pfau & Kitces (2014) for comparability and tractability with prior literature. Scenario

1 is simply based on historical parameters for the years 1926-2011. Pfau & Kitces (2014)

use historical real return averages found in Ibbotson Stocks, Bonds, Bills, and Inflation

yearbook. Scenario 2 uses the real returns assumptions prepared by Harold Evensky for

the MoneyGuidePro software of July 2013 (MoneyGuidePro is a popular financial

planning software package, see http://www.moneyguidepro.com/ifa for example. Harold

Evensky is regarded one of the leading financial planners in the US). Scenario 3

represents “a new normal” (à la El-Erian, 2010) of low interest rates, similar to the

present economic environment 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 the recent financial crisis, starting 1929 and

2007 respectively.

[INSERT TABLE 1]

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Since we analyze long-term fund “life” scenarios we use periods of T=1 year for

the time increment. A fund pays its gross annual donation at the beginning of the year

according to its specific payout scenario and then balances its asset portfolio according to

its fixed asset mix. The gross 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 a higher resolution analysis would not

change the results and conclusions materially. The scenario path then steps one-year

forward with adequate financial gains and losses to the beginning of the next year. To

study “perpetual” foundations we use a simulation horizon of 100 years, and examine the

residual asset value at the start of year 101. We conjecture that a longer horizon is

irrelevant since the market, the underlying assumptions, and the donor’s goal would not

hold for a period longer than a few dozen years. As well, a 100-year horizon provides a

comprehensive view, insight, and understanding of a fund performance, risks, and merits.

Longer horizons would increase the computation time and data volume linearly while

adding only marginally, if at all, to the foundation’s performance evaluation.

For a rather accurate expected (mean) value of a performance measure, 10,000

simulation paths are adequate. However, for risk analysis purposes extreme outcomes of

tail (rare) events are often of interest, therefore we simulate 100,000 paths for each

market regime and each investment mix and payout rule combination. To enhance

comparability between alternative choices of the endowments and to enable reproduction

of the analysis we generate and save time series of εi‘s that are repeatedly used in this

work. For each market regime we have 100,000 vector pairs of randomly drawn εi‘s,

standard normal iid, each 100 elements long, with the appropriate correlation. Using the

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parameters of Table 1 and the model of equation (1) we compute and save asset return

pairs for the three market regimes, a total of 2 assets × 100 years long × 100,000 paths ×

3 markets. These asset-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 university endowments. Typically, such an endowment pays 5% of its recent

three-year asset value average and often invests 60% of its money in equity. We consider

5% payout and 60% equity as a demonstrative example for a lawful and prudent

foundation following current IRS payout minimum 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 by historical

market returns. We do not ignore averages in this work, yet we complement the mean

with additional statistics, as averages have many flaws and could be dangerously

misleading. We examine 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 includes bonds). For each combination we calculate a set of

measures which includes: (1) residual portfolio value at the end of year 100; (2) total life

donation amount, summing the outlays of the entire 100-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 measure we calculate the

following descriptive statistics: minimum, maximum, median, average, standard

deviation, skewness, excess kurtosis, and 1, 5, 10, 90, 95, and 99th percentiles.

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Foundation B is quite similar to foundation A with one seemingly minor technical

difference: it funds 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 changes according to the specified payout rate. For example, for a

payout rate of 5%, the minimum annual donation is $40M ($1B x 5% x 80%). Unlike

foundation A which could theoretically live forever, adjusting its outlay proportionally to

its asset value, foundation B is committed to a minimal annual donation regardless of its

asset value. This might cause the depletion of its assets even within the first 10 years of

its operation! Hence, for foundation B we collect an additional performance measure,

years to depletion, the number of years since the start of the foundation until its minimal

annual payment exceeds its residual asset value.

Findings

The simulations described in the methodology section and their quantitative

analysis result in many tables and charts, much beyond the scope of a journal article.

Hence, we include in the paper only select representative examples (our fuller set of

results is available on request).

The simulations for the classic, ‘mainstream’ example of foundation A (Figure 1),

tell two cautionary tales. Firstly, by looking at the left column of charts in Figure 1, it is

clear that the changing financial climate significantly affects the foundations’ assets.

These three charts show the average residual value of the foundation’s endowment after

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100 years of operation at different combinations 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 average foundation will survive the 100-year

life-term in our analysis, and will even end up with a larger endowment (on average)

under most payout and portfolio mix combinations (Figure 1a). For example, the average

foundation with 5% payout and a 60% equity portfolio (and 40% bonds), will have

$325.3M in its coffers in the beginning of year 101. Under such assumptions, perpetuity

seems to be a given (if one trusts averages). However, under the more realistic future

scenarios of markets 2 and 3, perpetuity is hardly the norm (Figures 1c and 1e). Only low

payouts and high-risk portfolios 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 $13.7M under market 3. That is much below the

starting point of $100M.

[INSERT FIGURE 1]

Secondly, averages are often misleading. Many of us tend to associate random

outcomes with the normal distribution. However, many phenomena in financial markets

are not distributed normally. In a skewed distribution averages and medians are not

identical and could be materially different from each other. This is what we actually

observe in the right column of charts of Figure 1, depicting the medians that pair with the

left column charts of the figure. Considering that 50% of the simulated foundations

perform worse than the median, clearly the relatively rosy picture depicted by the

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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 (Figures 1b,

1d and 1f), the assets deplete dramatically as market conditions deteriorate. While under

market 1 the median foundation with 5% payout and a 60% equity portfolio is worth

$112.4M after 100 years, under market 2 it is worth $10.6M and under market 3 it has

only $5.1M left in its endowment. What this means is that while past market returns

allowed most foundations to remain viable and capable of promoting their missions after

100 years of operation, under recent and more realistic market conditions even prudent

foundations would likely be near closing their operations. Under market 2 only half of the

endowments of foundations paying 3% annually and invested in 70% equity would not be

depleted, and under market 3 all the median foundations in our simulations do not retain

their original $100M asset value. Furthermore, prudent planning cannot rely on averages

and even 50% chance of medians shows an optimistic view of the possible outcomes, as

the odds are 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 with a 3% payout rate. The 10th percentile remaining asset value

declines steeply for higher payout rates, and it is almost zero for 6% and 7% payout rates

(see Figure 2a). Furthermore, total lifetime giving, under even the most favorable

policies, is most likely to be lower than the foundation’s starting endowment value

(Figure 2b), i.e. in these circumstances the financial investment destroys value (in real

terms).

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[INSERT FIGURE 2]

The top two charts of Figure 3 show the average total grantmaking amount that

foundation A will give over 100 years under specified payout rates and portfolio mixes.

On the top left (Figure 3a) we can see that this amount increases the higher the risk the

foundation managers take in their investments. But more importantly, substantially higher

lifetime grantmaking is evident when payout rates are lower. This is not surprising if one

considers that high risk is usually accompanied by higher returns, and lower payout rates

allow for the endowment to accumulate more assets, as is also seen in Figure 1a. We

chose to show this under the less optimistic and more cautious assumptions of market 3.

Figure 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 shows how

contemporary assumptions generate much lower performance in terms of foundation size

and outlays, 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 dramatic under markets 2 and 3, when compared to market 1, a 3% payout

rate results in $50M more in total lifetime 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 a declining mean annual grantmaking of the

foundation when payout rate increases (Figure 3c).

[INSERT FIGURE 3]

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Another indicator of endowment performance – grantmaking volatility – is shown

in Figure 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 provide services 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 lower payout 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

foundation B, whose mission introduces an important restriction. Foundation B’s mission

includes the funding of a chain of hospitals, hence it is required to provide an annual

giving of no less than 80% of its first year’s outlays.

As seen in Figure 4, the cautious assumptions of market 3 (“a new normal”) do not

bode well for foundation B. Firstly, longevity is seriously compromised. Figure 4a on the

top left shows that on average foundation B would not reach 100 years of operation under

any payout and portfolio combination. While changes in the portfolio do not make a big

difference to foundation B’s longevity, payout rates certainly do. Even when paying out a

fixed 3%, the foundation will run out of money after sixty to seventy years on average.

However, when following the currently required 5% minimum payout, longevity will

decrease to thirty to forty years only. Focusing on the surviving endowments at the end of

100 years of operation, Figure 4e shows their average residual asset value in billion

19

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

important to note, that these are the results of the surviving foundations only, as under

this scenario most foundation do not make it to be 100 years old. In fact, the number of

surviving foundation at year 101 can be as little as two or three foundations out of

100,000. The highest survival rate is of foundations paying out 3% with a 70% equity

portfolio, at a 40% survival rate (Figure 4c). This rate dropes dramatically for higher

payouts and it is merely 5.1% for foundations following the current payout norm of 5%.

[INSERT FIGURE 4]

Not only do low payout rates mean greater longevity, they also mean greater giving

capacity, and consequently, greater impact potential. Figure 4b shows that total lifetime

grantmaking increases on average with higher equity-to-bonds ratios in the portfolio and

lower payout rate. Lower payout rates do increase the year-to-year volatility of the

average foundation’s giving (Figure 4f), yet in the case of foundation B this means that in

some years the disbursement would be (much) higher than its minimal required level,

which could allow for certain capital expenditures above the routine operation level. In

sum, in this scenario, endowment depletion rates are alarming and rise with payout rates.

Cautious low risk investments mean, on average, an early death (Figure 4a) and a lower

life-time total grantmaking (Figure 4b). While this is reversed for the 10th percentile

20

foundation (Figure 4d), a low equity portfolio mix improves donation only slightly. The

lowest decile results are alarming, showing total lifespan grantmaking lower than the

beginning endowment of $1billion for all payout-investment combinations.

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 the last decade (e.g. El Erian, 2010), it would be wise to reconsider

policies that govern grantmaking philanthropic foundations. We address here one much

contested policy, which requires foundations to give at least 5% of their endowment each

year. The minimum payout regulation was set under very different financial

circumstances than today’s economic climate. The realization that the current 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

grantmaking foundations under changing financial assumptions, we generate a large set

of simulated foundations life cycles using Monte Carlo methods under three different

capital market conditions, with varied portfolio mixes and payout rates. We apply this

methodology on two hypothetical foundations. Since these two cases probably represent

many real foundations (particularly foundation A), their results provide important insights

21

into the influence of payout decisions on future performance in a changing economic

environment. We find that when we assume continuation of past market 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 retain

their endowment value for 100 years only when their managers would choose low

payouts, which do not commensurate with the accepted norms of foundation management

today. Under more current market conditions, foundations would likely face relatively

early endowment depletion and 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 missions for 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 all the foundations in our

simulations for the typical foundation A are depleted, on average, below their 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 lifetime grantmaking and higher mean annual grantmaking, and grantmaking is

also generally less volatile under 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 foundation would 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.

22

In light of these findings, we argue that perpetual foundations, retaining their initial

value forever, seem unrealistic and that minimum payout requirements need to be

reviewed. Despite the increased debate over limited-life foundations and the argument

that they are becoming the “new traditional” approach (Hamilton, 2011), perpetuity is

still the majority and the default choice in most cases (Renz & Wolcheck, 2009), even

among family foundations, where spend downs are becoming more frequent (Boris, De

Vita & Gaddy, 2015). Policies too, treat perpetual foundations as 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 findings

show, they seriously jeopardize perpetuity and hamper longevity.

Barbetta et al (2015) note the large heterogeneity between foundations, and assert

that new regulations should consider need for a more refined model of regulation of these

organizations. In line with their assertion, if canceling minimum payout requirements

altogether is not politically feasible, we argue that at the very least policymakers should

contemplate requiring divergent minimums for different types of foundations, based on

their missions. We suggest a tiered payout minimum policy, where different levels of

payout would be required for different foundations based on expressed mission and

relevant financial planning. Thus, for a foundation whose mission is clearly long-term

and the trajectory of the social issue it addresses calls for increasing investment over

decades, according to our analysis a low payout rate, possibly 3-4% annually, is most

suitable. Consider for example a foundation dealing with reduction of ocean pollution or

mitigating droughts in arid regions. The repercussions of these challenges will be felt

over decades and even centuries, and are likely to increase with over-population and

global warming. A somewhat similar, though less dramatic example, would be a

23

scholarship fund, aimed to provide financial aid to low-income students. Regretfully, it is

unlikely that low-income students will disappear in the near future, 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

give and make an impact throughout her lifetime.

We do not argue that judging the results of the payout minimum by crunching

numbers should be the singular criterion when reconsidering foundation payout minimum

policies. Many different concerns drove the decision to set a payout minimum.

Intergenerational tax fairness is one such concern (Klausner, 2003; Sansing & Yetman,

2006). Obviously, lowering the minimum payout rate does not address this matter. On the

other hand, policy makers may consider a different look on this issue, based on the idea

of sustainability. We believe that our generation should be 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 is one 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 to replace those that are disappearing?1 Our study is focused on the micro

level, and examines how financial climate, payout rates and asset management affect an

1 Thanks to K for this insight.24

individual foundation. While it has implications for the entire population of foundations,

it is not an overall analysis of the foundation sector. While our findings clearly suggest

revisiting payout rate minimum policies, policy should consider not just changing

requirements for existing foundations to ensure longevity and increase lifetime giving

amounts, but also providing better incentives for the establishment of 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 required minimum payout. Therefore, unless policies and regulations

will change, all foundations need to consider 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 with the foundation’s mission and the

nature of the social issues each foundation deals with.

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

30% 40% 50% 60% 70%0

500100015002000250030003500400045005000

1a. Average residual assets, Market 1

3%4%5%

Investment mix [% equity]

$Mill

ions

30% 40% 50% 60% 70%0

200

400

600

800

1000

1200

14001b. Median residual assets, Market 1

3%4%5%

Investment mix [% equity]

$Mill

ions

30% 40% 50% 60% 70%0

50100150200250300350400450

1c. Average residual assets, Market 2

3%4%5%

Investment mix [% equity]

$Mill

ions

30% 40% 50% 60% 70%0

20

40

60

80

100

1201d. Median residual assets, Market 2

3%4%5%

Investment mix [% equity]

$Mill

ions

30% 40% 50% 60% 70%0

50

100

150

200

2501e. Average residual assets, Market 3

3%4%5%

Investment mix [% equity]

$Mill

ions

30% 40% 50% 60% 70%0

10

20

30

40

50

60

701f. Median residual assets, Market 3

3%4%5%

Investment mix [% equity]

$Mill

ions

31

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

30% 40% 50% 60% 70%0123456789

2a. Endowment at end of year 100 by payout rate, 10th percentile

3%4%5%6%7%

Investment mix [% equity]

$Mill

ions

30% 40% 50% 60% 70%80

85

90

95

100

105

110

2b. Total life donation amount, by payout rate, 10th percentile

3%4%5%6%7%

Investment mix [% equity]

$Mill

ions

32

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

3% 4% 5% 6% 7%0

50100150200250300350400450

3a. Total life donation amount, average, Market 3

30%40%50%60%70%

Payout rate

$Mill

ions

Market 1 Market 2 Market 30

500

1000

1500

2000

2500 2,009

493 228

1,365

383 201

910

300 178

620

244 160

443 207

147

3b. Total life donation amount, average, with 60% equity, by payout rate

3%4%5%6%7%

$Mill

ions

3% 4% 5% 6% 7%0

0.51

1.52

2.53

3.54

4.5

3c. Mean annual donation, average, Market 3

30%40%50%60%70%

Payout rate

$Mill

ions

3% 4% 5% 6% 7%0

0.5

1

1.5

2

2.5

33d. Donation volatility, average, Market 3

30%40%50%60%70%

Payout rate

$Mill

ions

33

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

30% 40% 50% 60% 70%0

10

20

30

40

50

60

70

804a. Years to depletion, average

3%4%5%6%7%

Investment mix [% equity]

Year

s

30% 40% 50% 60% 70%0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.04b. Total life donation amount, average

3%4%5%6%7%

Investment mix [% equity]

$Bill

ions

30% 40% 50% 60% 70%0%5%

10%15%20%25%30%35%40%45%

4c. Surviving foundations percentage after 100 years

3%4%5%6%7%

Investment mix [% equity]

Foun

datio

ns

30% 40% 50% 60% 70%0.7

0.720.740.760.78

0.80.820.840.860.88

0.9

4d. Total life donation amount, 10th percentile

3%4%5%6%7%

Investment mix [% equity]

$Bill

ions

30% 40% 50% 60% 70%0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4e. Average surviving endowment assets af -ter 100 years

3%4%5%6%7%

Investment mix [% equity]

$Bill

ions

30% 40% 50% 60% 70%0.000

0.005

0.010

0.015

0.020

0.0254f. Donation volatility, average

3%4%5%6%7%

Investment mix [% equity]

$Bill

ions

34

Table 1. Model 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% 0.1

Bonds 2.56% 6.50%

Market No. 2, MoneyGuidePro scenario (Evensky assumptions)

Average returns Returns volatility Correlation

Stocks 5.50% 20.70% 0.3

Bonds 1.75% 6.50%

Market No. 3, Lower future returns (Pfau & Kitces, 2014)

Average returns Returns volatility Correlation

Stocks 5.10% 20.00% 0.1

Bonds 0.30% 7.00%

35