Guide to The Philanthropy Outlook Model 2020 & 2021

P R E S E N T E D B Y

Marts & Lundy

R E S E A R C H E D A N D W R I T T E N B Y

Indiana University Lilly Family School of Philanthropy

F E B R U A R Y 2 0 2 0

Variable Definitions and Sources

Independent Variables1

C O N S U M E R S E N T I M E N T ( G C S E N T )

Consumer sentiment is an index computed based on monthly surveys covering personal finances, business conditions, and buying conditions. Data for consumer sentiment come from the Consumer Sentiment Index, Federal Reserve Bank of St. Louis (FRED), http://research.stlouisfed.org/fred2/series/UMCSENT

C O R P O R AT E P R O F I T S ( G C P R O F )

Corporate profits are corporate income after subtracting expenses. Data for corporate profits come from the Bureau of Economic Analysis, U.S. Department of Commerce, https://fred.stlouisfed.org/series/CPROFIT#0

C O R P O R AT E S AV I N G ( G C S AV E )

Corporate saving is corporate profits that are left over after taxes and dividend payments. Data for corporate saving come from the Bureau of Economic Analysis, U.S. Department of Commerce, https://fred.stlouisfed.org/series/B057RC1Q027SBEA#0

E M P L O Y M E N T ( G E M P )

Employment is a measure of the number of U.S. workers in the economy that excludes proprietors, private household employees, unpaid volunteers, farm employees, and the unincorporated self-employed. Data for employment from FRED, http://research.stlouisfed.org/fred2/series/PAYEMS

G R O S S D O M E S T I C P R O D U C T ( G G D P )

Gross Domestic Product (GDP) is “the value of the production of goods and services in the United States, adjusted for price changes,” according to the Bureau of Economic Analysis, U.S. Department of Commerce. Data for GDP come from Table 1.1.5, Bureau of Economic Analysis, U.S. Department of Commerce, http://www.bea.gov/iTable/index_nipa.cfm

H O U S E H O L D A N D N O N P R O F I T N E T W O R T H

( G N W O R T H )

Net worth for households and nonprofits is the net assets of households and nonprofits serving households after subtracting net liabilities. Data for the net worth of households and nonprofits come from FRED, http://research.stlouisfed.org/fred2/series/HNONWRA027N

TA X D U M M Y ( TA X D U M )

The Tax Dummy is zero except for 1986 when its value is one and 1987 when its value is negative one. The Tax Reform Act of 1986 implemented a two-step change in the highest individual tax rate from 50% in 1986, to 38.5% in 1987, and then to 28% in 1988. One would expect a spike in giving in 1986 as households shifted their planned giving forward to take advantage of the higher marginal tax rate in 1986. Likewise, one would expect a trough in giving in 1988 once the new lower tax rates were in effect. The 1987 response could be positive or negative. In fact, the data show a large spike in 1986, followed by a substantial decline in 1987, and a return to normalcy in 1988. The explanation outlined here does not account for this behavior. Nevertheless, the effects are so large that we elected to model that behavior directly to avoid the effect of the one-time tax reform exerting an undue influence on the remaining coefficients.

I N D I V I D U A L / H O U S E H O L D I T E M I Z E R S A N D

N O N - I T E M I Z E R S ( G N I T E M )

Itemizers refer to those taxpayers who can itemize certain expenses on their household taxes, as opposed to taking the standard deduction. Data for the number of itemizers come from the Internal Revenue Service (IRS), https://www.irs.gov/statistics.

I N T E R E S T R AT E F O R G O V E R N M E N TA L S E C U R I T I E S

( D R 1 T )

The interest rate for governmental securities is the rate of return on an asset after removing the effect of inflation. Data for the interest rates of governmental securities come from FRED, http://research.stlouisfed.org/fred2/series/GS1

P E R S O N A L C O N S U M P T I O N E X P E N D I T U R E S

( G R C O N S )

Personal consumption is a measure of personal consumption expenditures, or goods and services purchased by U.S. residents. Data for personal consumption come from FRED, from https://fred.stlouisfed.org/series/PCEC96#0

P E R S O N A L C O N S U M E R E X P E N D I T U R E I N D E X 2

“Personal consumer expenditures is the primary measure of consumer spending on goods and services in the U.S. economy. It accounts for about two-thirds of domestic final spending, and thus it is the primary engine that drives future economic growth. Personal consumer expenditures shows how much of the income earned by households is being spent on current consumption as opposed to how much is being saved for future consumption.”3 Data on consumer expenditures come from FRED, https://fred.stlouisfed.org/series/PCECTPI#0

P E R S O N A L I N C O M E ( G P I N C )

Personal income is the income received by persons from participation in production, government and business transfers, and government interest. Data for personal income come from Table 2.1, Bureau of Economic Analysis, U.S. Department of Commerce, http://www.bea.gov/iTable/index_nipa.cfm

T H E S & P 5 0 0 ( G S P )

The S&P 500 is the value of the Standard & Poor’s 500 Index on December 31 of a given year. Data for the S&P 500 come from FRED, https://research.stlouisfed.org/fred2/series/SP500

T H E P H I L A N T H R O P Y O U T L O O K 2 0 2 0 & 2 0 2 1 1

Dependent Variables

G R O W T H R AT E F O R I N D I V I D U A L / H O U S E H O L D

G I V I N G ( G I G I V )

The growth rate for individual/household giving includes cash and non-cash donations to U.S. charities contributed by all U.S. individuals and households (including those who itemize their charitable contributions on their income taxes and those who do not). Historical data for the growth rate in individual/household giving were derived from Giving USA 2019: The Annual Report on Philanthropy for the Year 2018, researched and written by the Indiana University Lilly Family School of Philanthropy and published by Giving USA Foundation, https://www.givingusa.org

G R O W T H R AT E F O R F O U N D AT I O N G I V I N G ( G F G I V )

The growth rate for foundation giving includes grants to U.S. charities contributed by all U.S foundations. Historical data for the growth rate in foundation giving were derived from Giving USA 2019: The Annual Report on Philanthropy for the Year 2018, researched and written by the Indiana University Lilly Family School of Philanthropy and published by Giving USA Foundation, https://www.givingusa.org. Foundation giving data in Giving USA are based on estimates produced by the Foundation Center (www.foundationcenter.org) and include grants from community, private (including family), and corporate foundations.

G R O W T H R AT E F O R E S TAT E G I V I N G ( G B G I V )

The growth rate for estate giving includes cash and non-cash donations (bequests) to U.S. charities contributed by all U.S. estates (including those who itemize their charitable contributions on their estate taxes and those who do not). Historical data for the growth rate in estate giving were derived from Giving USA 2019: The Annual Report on Philanthropy for the Year 2018, researched and written by the Indiana University Lilly Family School of Philanthropy and published by Giving USA Foundation, https://www.givingusa.org

G R O W T H R AT E F O R C O R P O R AT E G I V I N G ( G C G I V )

The growth rate for corporate giving includes cash and non-cash IRS itemized donations to U.S. charities contributed by all U.S. corporations and corporate foundations. Historical data for the growth rate in corporate giving were derived from Giving USA 2019: The Annual Report on Philanthropy for the Year 2018, researched and written by the Indiana University Lilly Family School of Philanthropy and published by Giving USA Foundation, https://www.givingusa.org

2

G R O W T H R AT E F O R E D U C AT I O N G I V I N G

( G E D U C G I V )

The growth rate for education giving includes cash and non-cash donations from itemizing and non-itemizing U.S. households, corporations, and foundations to U.S. educational charities, including institutions of higher education, private K-12 schools, vocational schools, libraries, educational research and policy, and other types of organizations serving educational purposes. Historical data for the growth rate in education giving were derived from Giving USA 2019: The Annual Report on Philanthropy for the Year 2018, researched and written by the Indiana University Lilly Family School of Philanthropy and published by Giving USA Foundation, https://www.givingusa.org

G R O W T H R AT E F O R H E A LT H G I V I N G ( G H E A LT H G I V )

The growth rate for health giving includes cash and non-cash donations from itemizing and non-itemizing U.S. households to U.S. health charities, including nonprofit community health centers, hospitals, and nursing homes; organizations focused on the treatment and/or cure of specific diseases; emergency medical services; wellness and health promotion; mental healthcare; health research; and other types of health organizations. Historical data for the growth rate in health giving were derived from Giving USA 2019: The Annual Report on Philanthropy for the Year 2018, researched and written by the Indiana University Lilly Family School of Philanthropy and published by Giving USA Foundation, https://www.givingusa.org

G R O W T H R AT E F O R P U B L I C - S O C I E T Y B E N E F I T

G I V I N G ( G P S B G I V )

The growth rate for public-society benefit giving includes cash and non-cash donations from itemizing and non-itemizing U.S. households to U.S. public-society benefit charities, including independent research facilities, community development organizations, human and civil rights organizations, philanthropy associations, national donor-advised funds, United Ways, federated charities, and other types of organizations. Historical data for the growth rate in public-society benefit giving were derived from Giving USA 2019: The Annual Report on Philanthropy for the Year 2018, researched and written by the Indiana University Lilly Family School of Philanthropy and published by Giving USA Foundation, https://www.givingusa.org

T H E P H I L A N T H R O P Y O U T L O O K 2 0 2 0 & 2 0 2 1 3

Stability of the Variables Used in the Forecast

To estimate charitable giving in future years, we must generate estimates of the economic variables that affect giving. We can expect the accuracy of these estimates to be higher or lower based on each variable’s historical variance. Deviations in the variables would affect our outlook for giving. The next section, “Conditions That May Impact the Giving Predictions,” explains the changes in the variables that would have to take place in order to change the outlook for giving for each source and the three subsectors included in this Outlook.

C O N S U M E R S E N T I M E N T

Consumer sentiment affects giving by corporations. This variable is generally an unstable economic indicator, meaning the likelihood that the growth rate for this variable will be considerably different than predicted is high.

C O R P O R AT E S AV I N G A N D C O R P O R AT E P R O F I T S

While these variables have significant influence on corporate giving, they are unstable economic indicators. The likelihood that the growth rates for these variables will be considerably different than predicted is high.

E M P L O Y M E N T

The employment rate is a stable indicator of giving, meaning the projected growth rate is not likely to differ significantly from what is predicted in this Outlook. Therefore, its predicted impact on giving by corporations is deemed highly reliable.4

G D P

GDP is generally a stable indicator of giving, meaning the projected growth rate is not likely to differ significantly from what is predicted in this Outlook. Therefore, its predicted impact on giving by foundations and corporations is deemed highly reliable.5 However, GDP may fall if the U.S. economic environment experiences an exogenous shock as a result of recession, disaster, war, or other severe situations.

H O U S E H O L D A N D N O N P R O F I T N E T W O R T H

Household and nonprofit net worth is a stable indicator of giving, meaning the projected growth rate is not likely to differ significantly from what is predicted in this Outlook. Therefore,

its predicted impact on giving by individuals/households, foundations, and estates is deemed highly reliable.6

INDIVIDUAL/HOUSEHOLD ITEMIZERS AND NON-ITEMIZERS

While this variable has influence on individual giving, it is still considered an unstable indicator due to the recent changes in tax law. The likelihood that the growth rates for these variables will be different than predicted is high.

I N T E R E S T R AT E F O R G O V E R N M E N TA L S E C U R I T I E S

The interest rate for governmental securities has significant influence on estate giving, in particular. This variable is a stable economic indicator. Therefore, its predicted impact on giving by estates is deemed highly reliable. Otherwise, this variable plays a small role in our predictions overall.

P E R S O N A L C O N S U M P T I O N

Personal consumption affects giving to education. This variable is generally a stable economic indicator, meaning the projected growth rate is not likely to differ significantly from what is predicted in this Outlook.7

P E R S O N A L C O N S U M E R E X P E N D I T U R E S

Personal consumer expenditures affect giving to education, health, and public-society benefit. There are many different types of personal consumer expenditures, and the majority are stable economic indicators. This means that for most of these indicators, the projected growth rates are not likely to differ significantly from what is predicted in this Outlook.

P E R S O N A L I N C O M E

Personal income is a stable indicator of giving, meaning the projected growth rate is not likely to differ significantly from what is predicted in this Outlook. Therefore, its predicted impact on giving by individuals/households is deemed highly reliable.8

T H E S & P 5 0 0

While S&P 500 has significant influence on corporate, individual/household, and foundation giving, this variable is an unstable economic indicator. The likelihood that the growth rate for this variable will be considerably different than predicted is high.9

4

Predicting Giving for the Sources of Philanthropy

This Philanthropy Outlook was constructed using econometric methods. We began building the prediction models by testing economic variables with established links to charitable giving. Specifically, we tested those variables that measure giving capacity and the cost of giving as reflected in tax rates. For each source of giving—individual/household, foundation, estate, and corporate—we selected the macroeconomic variables that best accounted for the growth rates in giving.

There are a large number of variables that can potentially account for giving. It is not practical to include all the potential explanatory variables in each of the models. To identify those variables that best explain and predict giving behavior, we employed a two-step process. In the first step, we tested each possible combination of explanatory variables. From the results, those combinations of variables with the greatest explanatory power were identified. Then, the models with the highest explanatory power were re-estimated through 2006 and used to predict the remainder of the sample. The “best” model was the one that yielded predictions that most closely resembled actual giving behavior.

The number of estimations required to employ this strategy depends upon the number of potential explanatory variables. As this number rises, the number of estimations increases dramatically. For instance, with five potential variables, 32 estimations are required; but with 10 potential variables, 1,024 estimations are required. Our experience suggests that 17 variables (131,072 combinations) is a practical limit. If we consider only variables that occur in the same year as the giving variable, this restriction is not serious. But, experience also suggests that variables from the previous year (“lagged variables”) may also be important. For that reason, the best model was selected in three steps:

• (Model Process 1) Only same-year variables were considered. The best model produced in this step was called the “base model.”

• (Model Process 2) Previous-year (lagged) variables were added to the base model, and the best model was

chosen using the criteria described above. The result was referred to as the “revised model.”

• (Model Process 3) The revised model was tested to determine if the current-year variables contained in the model were still relevant once the lagged variables introduced in Model Process 2 were included. Additionally, if any variable had previously been included in the final model from the prior year but was not in the revised model, it was included here for testing. Very few variables from the base model were eliminated in this step. The result was called the “final model.”

Predicting giving requires predictions of the explanatory variables used in the prediction models. The Indiana University Lilly Family School of Philanthropy has partnered with the University of Pennsylvania Wharton School of Business to include predictions for select economic variables from its Penn Wharton Budget Model in the Philanthropy Outlook model to increase the strength and rigor of the results. These variables included GDP, number of itemizers, GDP deflator, employment, one-year treasury rate, and the Consumer Price Index. The Wharton School of Business specializes in predicting economic variables and the Penn Wharton Budget Model accounts for policy changes, including the TCJA.

We began with an aggregate model that included growth rates in real GDP, consumer sentiment, employment, the GDP price index, and change in the one-year real interest rate. The growth rate in the monetary base was also included to capture monetary policy. Each of these six variables was regressed on a one-year lag. This model was self-contained in that it yielded predictions for the previously unpredicted variables through 2021. Most of the remaining variables were grouped into blocks and regressed on the six macroeconomic variables along with the lags. Once we predicted growth rates for the four components of giving, we were able to extrapolate the corresponding levels. Summing the four levels together yielded our prediction for the level of total giving. Then, we calculated the implied prediction for the growth rate of total giving.

T H E P H I L A N T H R O P Y O U T L O O K 2 0 2 0 & 2 0 2 1 5

PREDICTING GIVING FOR THE RECIPIENT SUBSECTORS

For predicting giving to the recipient subsectors—education, health, and public-society benefit—we used a similar process to the one described above for predicting the sources of giving. We began this process by using the same independent variables as the earlier models. We then tested additional macroeconomic variables that have either established or theoretical links to giving. Those variables that demonstrated both high explanatory power for subsector giving and were highly correlative with actual historical giving trends were selected for the subsector models.

PREDICTING GIVING FOR THE STRESS TEST ANALYSIS

We also received predictions from the Wharton School of Business for variables under a severe recession similar in scale to the 2008-09 recession. As with our baseline results, these variables included GDP, number of itemizers, GDP deflator, employment, one-year treasury rate, and the Consumer Price Index. Using these values to predict the other variables allowed us to produce a stress test analysis that estimates how charitable giving would change under severely adverse conditions as compared with The Philanthropy Outlook’s current results.

Table 1 G I V I N G P R O J E C T I O N S F O R T H E S T R E S S T E S T A N A LY S I S

These predictions are not growth rates, but rather differences in level between our baseline results and charitable giving under stress test conditions for each source in both years (e.g., the model predicts that total giving will be 10.6% smaller in 2020 in the stress test conditions than in our baseline).

G I V I N G VA R I A B L E S F O R T H E P R E D I C T I O N M O D E L S

The giving variables predicted within The Philanthropy Outlook 2020 & 2021 are listed in Table 2a. Candidate variables used to model each source of giving are listed in Table 2b. Tables 4 and 5 provide the regression equations used to predict each giving type within The Philanthropy Outlook 2020 & 2021. Table 6 provides the ratio of the root-mean-squared error to the standard deviation for each giving type, and Table 7 displays summary statistics for the giving variables and explanatory variables used in the models.

Figure 1 shows actual versus predicted growth rates for total giving for the years 2007 to 2017.

Table 2a G I V I N G VA R I A B L E S M O D E L E D B Y T H E R E G R E S S I O N S

2020 2021

T O T A L -10.6% -1 1 .7%

I N D I V I D U A L S -10.1% -10.7%

C O R P O R A T I O N S -9.7% -1 1 . 5%

F O U N D A T I O N S -1 3. 5% -1 5.9%

B E Q U E S T S -9.1% -10.8%

Dependent Variables Name

G R O W T H R A T E O F N A T I O N A L I N D I V I D U A L / H O U S E H O L D G I V I N G G I G I V

G R O W T H R A T E O F N A T I O N A L C O R P O R A T E G I V I N G G C G I V

G R O W T H R A T E O F N A T I O N A L F O U N D A T I O N G I V I N G G F G I V

G R O W T H R A T E O F N A T I O N A L B E Q U E S T G I V I N G G B G I V

G R O W T H R A T E O F N A T I O N A L E D U C A T I O N A L G I V I N G G E D U C G I V

G R O W T H R A T E O F N A T I O N A L H E A L T H G I V I N G G H E A L T H G I V

G R O W T H R A T E O F N A T I O N A L P U B L I C - S O C I E T Y B E N E F I T G I V I N G G P S B G I V

6

* The second column contains the names of the variables that appear in one or more of the models.

See Tables 3 and 4 for the final models.

| : Either the current or lagged value of this variable is included in the final model.

• : This variable was tested for inclusion in the final model but was rejected.

Empty cells reflect variables that were not tested within the specific giving model.

Table 2b C A N D I D AT E VA R I A B L E S U S E D T O M O D E L E A C H T Y P E O F G I V I N G (Variables are in year-to-year rates of growth)

Candidate Independent Variables Name* gigiv gcgiv gfgiv gbgiv geducgiv ghealthgiv gpsbgivC O N S U M E R S E N T I M E N T ( I N D E X ) G C S E N T I • I • • • •

C O R P O R A T E P R O F I T S G C P R O F •

C O R P O R A T E S A V I N G G C S A V E I

C O R P O R A T E T A X R A T E D C T A X I

D I S P O S A B L E P E R S O N A L I N C O M E G D P I N C I I • •

E M P L O Y M E N T G E M P • • • • •

G D P G G D P I I I • I • •

H O U S E H O L D A N D N O N P R O F I T N E T W O R T H G N W O R T H • I I • I •

D U M M Y V A R I A B L E F O R T H E 1 9 8 6 T A X R E F O R M

T A X D U M I I I • I

N U M B E R O F I N D I V I D U A L / H O U S E H O L D T A X I T E M I Z E R S

G N I T E M I I • •

I N D I V I D U A L / H O U S E H O L D T A X R A T E D P T A X • • • I

I N T E R E S T R A T E F O R G O V E R N M E N T A L S E C U R I T I E S

D R 1 T • I • I • • •

M O N E T A R Y B A S E G M B A S E I

P E R C E N T H E A L T H C A R E C O N T R I B U T E D T O G D P

D P C T C O N T T O G D P P C E H E A L T H C A R E •

P E R S O N A L C O N S U M P T I O N G C O N I • • •

P E R S O N A L C O N S U M E R E X P E N D I T U R E S :

C L O T H I N G G C L O T H I N G I •

C O M M U N I T Y S C H O O L S E R V I C E S G C O M M U N I T Y S C H O O L S I •

E D U C A T I O N G E D U C A T I O N •

E D U C A T I O N ( H I G H E R ) G S E R V I C E S H I G H E R E D • • •

E D U C A T I O N ( P R E K – 1 2 ) G N U R S E R Y T O H S I •

E D U C A T I O N S E R V I C E S G E D U C S E R V I C E S I • I

F O R E I G N T R A V E L G F O R E I G N T R A V E L • • I

F U R N I S H I N G S G F U R N I S H I N G S •

G O O D S : J E W E L R Y A N D W A T C H E S G G O O D S J E W E L R Y A N D W A T C H E S • • I

G O O D S : M O T O R V E H I C L E S G G D O O D S M O T O R V E H I C L E S •

G O O D S : N E W M O T O R V E H I C L E S G G O O D S N E W M O T O R V E H I C L E S I • •

G O O D S : T E X T B O O K S G G O O D S E D U C B O O K S •

H E A L T H G H E A L T H I • •

H E A L T H C A R E S E R V I C E S G H E A L T H C A R E S E R V I C E S I I •

N O N P R O F I T S A L E S G N P O R E C E I P T S A L E S • I

N O N P R O F I T S E R V I C E S G N E T N P O S E R V I C E S I I

N O N P R O F I T F I N E X P S E R V I C E S G S E R V I C E S F I N E X P N P O • •

P H A R M A C E U T I C A L S G P H A R M A • •

R E C R E A T I O N G R E C I • •

R E C R E A T I O N S E R V I C E S G R E C S E R V I C E S • I

S O C I A L A N D R E L I G I O U S S E R V I C E S G S O C I A L S E R V I C E S A N D R E L I G I • •

P E R S O N A L G I V I N G G P G I V I

P E R S O N A L I N C O M E G P I N C I I • •

P E R S O N A L S A V I N G G P S A V E I • • I

P E R S O N A L S A V I N G R A T E D P S R A T E I I • I

P R E V I O U S Y E A R ’ S V A L U E O F T H E G I V I N G V A R I A B L E

• • • I • • I

P R O P O R T I O N O F M O N T H S I N W H I C H T H E E C O N O M Y W A S I N A R E C E S S I O N

D R E C M • • • I

S & P 5 0 0 ( I N D E X ) G S P I I I I I • I

T O T A L G I V I N G G T G I V • • I

Table 3 M O D E L S F O R P R E D I C T I N G G I V I N G B Y D O N O R T Y P E A N D S T E P - A H E A D A C C U R A C Y C H E C K

G I V I N G B Y I N D I V I D U A L S / H O U S E H O L D S

gigiv = -1.811 + 0.950gpinc + 0.091gsp + 0.147gcsent + 2.823dpsrate – 0.228 grpsave + 9.388taxdum + 0.082gnitem + 1.332gpinc–1 + 0.081 gsp–1 – 9.408gdpinc–1 – 0.075gcsent–1 + 12.078dpsrate–1 – 1.129ggdp–1 – 0.137gpsave–1 + 9.907gcons–1 +0.073gnitem–1Adjusted R2=0.7020, Sample: 1955-2018 (n=64)

G I V I N G B Y C O R P O R AT I O N S

gcgiv = -1.940 + 0.122gcsave + 0.085gsp + 1.097dr1yr + 12.998taxdum + 1.218ggdp –0.092gcsave–1 – 47.135dctax–1 + 1.782dr1yr–1 + 0.251gmbase–1Adjusted R2=0.4228, Sample: 1955-2018 (n=63)

G I V I N G B Y F O U N D AT I O N S

gfgiv = 0.391 + 0.249gsp – 0.601gnworth + 0.372gsp–1 – 0.146gcsent–1 + 1.542ggdp–1Adjusted R2=0.4058, Sample: 1955-2018 (n=64)

G I V I N G B Y E S TAT E S

gbgiv = 1.906 + 0.423gsp – 1.371dr1t – 1.696gnworth + 1.896gnworth–1 – 0.471e–1Adjusted R2=0.2885, Sample: 1955-2018 (n=64)

Notes: e–1 and e–2 are one-period and two-period lagged residuals from the respective models. These models use 2007 as the first prediction.

Table 4 M O D E L S FO R P R E D I C T I N G G I V I N G TO T H E R EC I P I E N T S U B S EC TO RS A N D S T E P-A H E A D AC C U R ACY C H EC K

E D U C AT I O N G I V I N G

geducgiv = -3.678 + 0.213gsp – 1.635grdpinc + 3.273dpsrate + 2.745ggdp + 7.053taxdum + 0.047grnitem + 0.302gpgiv – 3.135NetNPOServices + 1.890gHealth – 0.830gpinc–1 + 0.088gsp–1 + 4.340gcons–1 +0.562gEducServices–1 – 0.541NetNPOServices–1 – 1.947gRec–1 + 0.321CommunitySchools–1 + 0.484gHealthServices–1 – 0.191gGoodsNewVehicles–1Adjusted R2=0.8105, Sample: 1961-2018 (n=58)

H E A LT H G I V I N G

ghealthgiv = -1.929 + 1.168gnworth + 5.060gHealthcareServ + 2.154gNurseryToHS – 1.097gClothing - 7.291gNPOReceiptSales + 0.830gNPOReceiptSales–1Adjusted R2=0.3428, Sample: 1961-2018 (n=58)

P U B L I C - S O C I E T Y B E N E F I T G I V I N G

gPSBgiv = 1.066 + 0.048gsp + 45.236taxdum + 1.444gTotGiv – 4.300drecm–1 – 0.381gsp–1 - 39.668dptx–1 + 15.837dpsrate–1 – 0.695grpsave–1 + 1.947taxdum – 1.727gEducServices–1 – 0.301gForeignTravel–1 + 0.211gJewelry+ 2.693gRecServices–1 – 1.000et-1Adjusted R2=0.781, Sample: 1956-2017 (n=63)

Notes: e–1 is a one-period lagged residual from the respective models. These models use 2005 as the first prediction.

8

Table 3 includes the dependent variables for giving by individual/households, corporations, foundations, and estates. Table 4 includes the dependent variables for giving to education, health, and public-society benefit. These variables are on the left side of the equation. On the right side of the equations within Tables 3 and 4 are the independent variables that comprise each model, along with their coefficients and a constant variable. Most of these variables are in the form of growth rates and are therefore percentages. The -1 subscript after a variable name refers to the prior year’s value. For instance, gsp–1 in the individual/household equation for the year 2020 is the growth rate of the S&P 500 in 2019.

We can also use the equation for individual/household giving as an example for how the results are interpreted. This equation says that a 1% increase in the growth rate for the S&P 500 (gsp) is associated with approximately a 0.091% increase in the growth rate for personal giving. These effects are summed for each variable, as well as for the constant, which gives us our predicted growth rate for that year. The abbreviations for each of the variables are listed in Tables 2a and 2b.

The R2 value below each equation is a measure of how well the model explains the results upon which it is based. R2 values can range from 0 to 1, with higher values indicating greater explanatory power. For instance, the adjusted R2 of 0.702 for giving by individuals/households means that the model accounts for 70.2% of the variance in the growth rate for this series. In general, the R2s reported above are satisfactory, and in some cases superior, given the typical difficulty in explaining growth rates. The ability to explain historical behavior need not translate into high-quality predictions of future growth rates.

While R2 values are reported here, they were not the criterion used for model selection. Instead, models were selected based on a combination of root-mean-squared error (RMSE), Bayesian or Schwarz information criterion, as well as the Akaike information criterion. R2 values are reported for their ease of interpretation and ubiquity.

The sample identifies the years included in the data series used to estimate the models.

T H E P H I L A N T H R O P Y O U T L O O K 2 0 2 0 & 2 0 2 1 9

Prediction Quality

A common check of prediction quality is to re-estimate the model, setting aside the most recent observations. The revised model is then used to produce predictions over the set-aside observations. These predictions can be compared directly to their corresponding actual values. Here, we re-estimate the models using data through 2006. These models are then used to construct step-ahead values year by year through the end of the sample. Step-ahead analyses assume that the prior year’s values are known for generating the current year’s value.

In the table below, RMSE is the root-mean-squared error defined as:

Where “Actual” is the actual growth rate, “Prediction” is the predicted growth rate, and “T” is the number of years in the prediction period, standard deviation (Std.Dev) is defined as:

“Average” is the average of the actual growth rates.

Table 5 R AT I O O F T H E R O O T- M E A N - S Q U A R E D E R R O R T O T H E S TA N D A R D D E V I AT I O N F O R E A C H G I V I N G T Y P E

The third column in the table contains the ratio of the RMSE to the standard deviation. If the predicted values are no better than the simple average of the actual values, the ratio is one. Smaller ratios indicate better performance, and a ratio of zero implies that the predictions equal the

actual values. With the exception of foundation giving, the ratio is less than one for all sources of giving. Among the recipient subsectors, only public-society benefit giving has a ratio above one.

RMSE (1) Standard Deviation (2) Ratio (1)÷(2)

T O T A L 4.503 5. 21 2 0.86 4

G I V I N G B Y I N D I V I D U A L S / H O U S E H O L D S 3. 273 5.866 0.55 8

G I V I N G B Y C O R P O R A T I O N S 5.736 8.976 0.6 39

G I V I N G B Y F O U N D A T I O N S 4. 3 4 6 4. 241 1 .025

G I V I N G B Y E S T A T E S 13.479 19.924 0.67 7

E D U C A T I O N G I V I N G 3.798 9.427 0.4 03

H E A L T H G I V I N G 5.8 4 4 7. 380 0.792

P U B L I C - S O C I E T Y B E N E F I T G I V I N G 14.1 15 9.03 3 1 .56 3

1 0

Variable AverageStandard Deviation Min Max Variable Average

Standard Deviation Min Max

G C S E N T 0. 242 9.6 30 -29.452 25.116 G H E A L T H 5.510 2.150 1 .0 49 10.661

G C P R O F 2.926 10. 205 -19.526 22.341G H E A L T H C A R E S E R V I C E S

5.5 87 2.550 0.671 1 2.6 4 4

G C S A V E 1 .8 52 26.942 -63.054 81 .093G S E R V I C E S H I G H E R E D

5. 218 3. 387 -2.710 15.730

D C T A X -0.0 02 0.027 -0.14 0.08 8G G O O D S J E W E L R Y A N D W A T C H E S

2.747 4.979 -8.5 3 3 17.603

G D P I N C 3. 275 1 .787 -1 . 305 8.8 39G G D O O D S M O T O R V E H I C L E S

2.97 7 1 1 .103 -21.997 32.186

G E M P 1 .713 1 .969 -4.421 5.674G G O O D S N E W M O T O R V E H I C L E S

2.698 14. 274 -29.909 4 0.95 8

G G D P 3.106 2. 260 -2.569 8. 3 30 G N U R S E R Y T O H S 4. 25 4 3. 274 -3.180 1 1 . 229

G N W O R T H 3.55 3 3.997 -15.632 10. 294G S E R V I C E S F I N E X P N P O

4.51 2 3.55 4 -5.8 59 15.1 25

T A X D U M 0.0 0 0 0.169 -1 .0 0 0 1 .0 0 0G N P O R E C E I P T S A L E S

5.0 03 2. 208 0.4 8 5 9.151

G N I T E M * 1 .47 7 10.194 -65.220 1 2.66 4 G N E T N P O S E R V I C E S 4.824 1 .869 1 .4 0 0 8.475

D P T A X -0.0 08 0.039 -0.191 0.086 G P H A R M A 5.523 3. 309 -1 .74 6 14.137

D R 1 T -0.0 03 1 .1 14 -2.5 86 2.8 41 G R E C R E A T I O N 3.802 3.0 08 -4.052 1 1 .90 0

G M B A S E 3.6 47 8.1 24 -6.94 8 56.822 G R E C S E R V I C E S 4. 219 2.527 -2.727 9.0 05

G C O N 3. 259 1 .74 4 -1 . 262 7.1 18G S O C I A L A N D R E L I G S E R V I C E S

5.101 3.18 3 -2.917 14.152

D P C T H E A LT H T O G D P 0.0 02 0.08 4 -0. 21 0. 308G G O O D S E D U C B O O K S

3.191 4.8 8 8 -5.06 3 18.872

G C L O T H I N G 1 .094 2.615 -5. 259 6.56 8 G P I N C 3. 317 2.0 41 -3.036 9.027

G C O M M U N I T Y S C H O O L S

4.715 5. 309 -8.795 18.8 35 G P S A V E 3.076 13.975 -47.472 37. 20 0

G E D U C A T I O N 4.790 2.5 80 -1 .08 3 10.98 4 D P S R A T E -0.017 1 .016 -2.425 2. 3

G E D U C S E R V I C E S 4.908 2.66 3 -0.91 2 1 1 .8 55 D R E C M -0.0 01 0. 375 -0.917 1

G F O R E I G N T R A V E L 4.926 6.6 30 -13.480 31 . 318 G S P 4.159 16.594 -50.527 37.810

G F U R N I S H I N G S 1 .966 4.613 -10.293 14.752

Table 6 SU M M A RY STATISTI C S FO R T H E G IVIN G VA RIA B LES A N D E X PL A N ATO RY VA RIA B LES USED IN T H E M O D ELS

* The GNITEM values represent the percentage growth rate. For all other variables, the values represent the logged difference.

T H E P H I L A N T H R O P Y O U T L O O K 2 0 2 0 & 2 0 2 1 1 1

Figure 1 A C T U A L V S . P R E D I C T E D G R O W T H R AT E S F O R T O TA L G I V I N G , 2 0 0 7 - 2 0 1 7

Figure 1 provides a comparison of forecasted growth rates against actual growth rates for total giving. The average and standard deviation of the forecasts match the actual average and standard deviation quite closely. The forecast average is 1.61%, and the actual average is 1.50%. The standard deviation of the forecast is 5.09%, versus 5.38%

for the actual. However, as Figure 1 shows, the forecast model missed a large spike in the giving growth rate in 2012. The predicted spike came in the following year when the actual growth rate had fallen below zero. The model did capture the downturn in the giving growth rate in 2008 at the onset of the Great Recession.

1 2

1 Also referred to as “explanatory variables.”

2 There are several terms used for the various personal consumer expenditures in The Philanthropy Outlook 2020 & 2021 models. Please refer to Table 2b to view these terms.

3 “Chapter 5: Personal Consumption Expenditures,” Concepts and Methods of the U.S. National Income and Product Accounts, Bureau of Economic Analysis, U.S. Department of Commerce, November 2017, https://www.bea.gov/sites/default/files/methodologies/nipa-handbook-all-chapters.pdf#page=90

4 Predictions are based on annual data available in October 2019.

5 Predictions are based on annual data available in October 2019.

6 Predictions are based on annual data available in October 2019.

7 Predictions are based on annual data available in October 2019.

8 Predictions are based on annual data available in October 2019.

9 Predictions are based on annual data available in October 2019.

T H E P H I L A N T H R O P Y O U T L O O K 2 0 2 0 & 2 0 2 1 1 3