The Role of Revenue Type in the Growth of

Young & SmallNot-for-Profits

Elizabeth A.M. Searing, CNPDoctoral Candidate

Andrew Young School of Policy StudiesGeorgia State University

WIMPS Workshop, Indiana University (IUPUI)December 9, 2014

Agenda• Motivation and research questions• Literature: nonprofit growth, small enterprises, and

revenue types• Hypotheses on the impact of revenues on spending• Data • Methods and findings– Dynamic estimation– Fixed effects with lags

• Conclusions and discussion– Model feedback?

2

Motivation

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• Empirical: Entrepreneurs & the missing nonprofit SME specialty

• Theoretical: Organizational ecology and the liabilities of newness and smallness

Theoretical Motivation:Organizational Ecology and the “Liabilities”

Stinchcombe (1965)

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InternalLack of in-house

learning Lack of routine

External  Lack of allies

Lack of 

established 

clients 

• Newness– Stinchcombe (1965)– Not-for-profits:

• Singh et al. (1996)• *Fichman and Levinthal

(1991)• *Chambré and Fatt (2002)

• Smallness– Theory underdeveloped

(Hager 1999)– Not-for-profits:

• Frumkin (2002)• B Hager et al. (2004)

Research Question

• Motivating Research Question:– What is the role of revenue type in not-for-profit

growth?

• Operationalized Research Question:– How does not-for-profit spending respond to

increases in revenue types?or

– What are the total expense elasticities with respect to different income types?

5

Literature:Why are small and young enterprises unique?

• Obstacles to growth (Frumkin, 2002)– Human capital– Financial capital

• The curses of good luck • The curse of optimism• Trzcinski and Sobeck (2012)– 383 small- and medium-sized not-for-profits– Two cross-sections of survey responses – DV: Staff size, budget growth, clients served, and

number of programs– Findings: Program development and positive attitude!

6

Literature:What is the role of revenue type?

• Strong literature in revenue interactions– Crowding out– Diversification

• Relationship between revenues and operations– Benefits theory (Wilsker and Young 2007)– Pratt’s typology (2005)

• Relationship between revenue type and growth– Large nonprofits (Foster and Fine, 2007)– Donors and social contacts (Galaskiewicz, Bielefeld, and

Dowell, 2006)– Impact of total expenses on revenue volatility (Carroll

and Stater, 2007) 7

Dependent Variable

• Total annual expenses– Assumes service provision maximization as the

Executive Director (“the nonprofit’s”) objective function• Supporting literature (Weinberg, 1978; Ansari, Sidderth et. al,

1996; Brooks, 2005; Galaskiewicz, Bielefeld, and Dowell, 2006)

• Other objectives can be feasibly modelled by program expenditure growth (Steinberg, 1986)

– Division of functional expenses is unreliable

– Employment is a particularly bad fit8

Independent Variables: Revenue Types• Private Giving

– The role of donative support– Previous studies involving fundraising elasticities (such as

Okten and Weisbrod, 2000)– Critiques: instability

• Indirect Support– The role of other nonprofits: federated campaigns and

“organizational family ties” – Critique: size and loss of autonomy

• Program Service Fees– Growing part of the sector– Reliability and autonomy– Critiques: Mission drift and potential interaction effects with

other revenues 9

Independent Variables: Revenue Types

• Government Grants– Measuring government contributions– Varieties– Critiques: Isomorphism and resource dependency (Frumkin

and Kim, 2002)

• Income from Non-Mission Activities– Interests, dividends, investments income (including rent)– Sales of securities, other assets, and inventories– Critique: Mission drift, plus possible tax implications

• Hypotheses H1-H5:Total expenses with respect to each revenue type will be unit elastic, ceteris paribus. 10

Independent Variables: Non-Revenues

• Unrestricted Net Assets (UNA)– Accumulation protects against vulnerability– Congressional action against hoarding– Calabrese (2012) shows that targeted accumulation occurs

and suggest motivations.

• Hypotheses H6:Total expenses with respect to UNA will be elastic.

• Fixed Cost Ratio– Fixed costs signal a stream of future outlays

• Hypotheses H7:Total expenses with respect to increasing levels of fixed cost will be inelastic.

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Data- Source:

NCCS “Digitized Data”

- Compiled Form 990s

- Highly detailed and checked for accuracy

- 1998-2003

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Beginning sample size- less not-for-profits observations with missing rule dates- less not-for-profits observations with incorrectly formatted rule dates- less not-for-profits observations with ages less than -1- less observations from private foundations- less observations from the 990 EZ form- less observations that do not use accrual accounting

1,388,480-33,827-16,399

-4,979-13,422

-267,846-415,451

Potential sample size after cleaning 636,556

- less observations from not-for-profits older than 10 years- less observations from not-for-profits who never made less than $150K during the study time frame

-410111-163,760

Potential final sample 62,685

- less observations that have insufficient reporting years for the dynamic modelling

-35,216

Final sample of observations of young and small not-for-profitsFinal sample of young and small not-for-profits

27,469

11,089

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Sample Composition: Young and Small (N=62,685)

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56%

26%

10%

8%Neither

New Only (Age ≤ 10)

Both

Small Only (Total Revenues ≤ $150K)

Sample Composition: Subsectors(N=27,469)

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Subsectors

0 5 10 15 20 25 30 35 40 45

ArtsHealthHuman Svcs.PublicEdu. (w/o Higher)Higher Edu.HospitalsOther

Operationalization

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Name Formula NCCS Digitized Data Fields

SIZE Total expensesln (p1totexp + p1speve + p1rente + p1securc

+ p1othc + p1invntc +1)

PRIVATE GIVINGDirect Support + Special Events +

Duesln (p1dirsup + p1spevg + p1dues + 1)

INDIRECT Indirect Support ln (p1indsup + 1)PROG SVC  Program Service Revenues ln (p1pSrev + 1)

GOVT GRANT Government Grants ln (p1govgt + 1)

NON-MISSIONINC

Interest + Dividends + Other Investment Income + Securities

Sales +Non-Security Asset Sales + Rent + Inventory Sales

ln (p1int + p1div + p1oinv + p1securg + p1othg + p1rentg + p1invntg + 1)

UNA Unrestricted Net Assets ln (p4e_unre + 1)

FIXED COST(Occupancy + Interest+

Depreciation) / Total Expenses

(p2tOccup + p2tint + p2tDepr) /(p1totexp + p1speve + p1rente + p1securc +

p1othc + p1invntc )

AGEYears since receipt of tax-exempt

statusfisyr - ruledate

Summary Statistics (Dynamic)

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Name Mean Median SD Min/Max

SIZE 406,467.1 144,297.5 6,404,217 0 / 988,287,395.7

PRIVATE GIVING 101,499.7 31,600 373,319.9 0 / 19,743,116

INDIRECT 11,507.6 0 206,328.6 0 / 20,527,736

PROG SVC  164,797.8 6,500 4,618,705 0 / 2,749,559

GOVT GRANT 50156.4 0 487,848.3 0 / 48,181,248

NON-MISSIONINC

50,293.5 1,125,8 1,084,612 0 / 153,964,382

UNA 313,379.4 34,481.6 3,722,804 0 / 546,679,869

FIXED COST .145 .055 .203 0 / 1

AGE 5.9 6 2.5 1 / 10

*Adjusted, but unlogged. N=27,469

Modelling Sequence

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Fixed Effects with Lagged Dependent VariableNickell Bias Endogeneity

Fixed Effects with Lagged Independent VariablesAutocorrelation

Interpretability

Fixed Effects Endogeneity Simultaneity

Dynamic Model with an Blundell-Bond Estimator

Dynamic Model

• Controls: Age, year dummies; separate sectors• System GMM using Blundell-Bond (1994) (IV)

estimator– Use of a level as an instrument allows dummies and time-

invariant variables– Lags are limited to 2– All revenues save grants are treated as endogenous; grants

are treated as predetermined. Age, the current year’s fixed cost ratio, and the previous year’s UNA are all considered exogenous with the current year’s spending. 19

Results (Blundell-Bond): Revenues

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VARIABLES Arts Health Human Public Edu wo High

High Edu Hospitals Other

Govt. Grants 0.020** 0.045*** 0.026*** 0.038*** 0.031*** 0.034***(0.009) (0.011) (0.007) (0.008) (0.010) (0.009)

Private Giving 0.044*** 0.053** 0.060*** 0.088***

(0.014) (0.025) (0.022) (0.033)

Indirect Support 0.049** 0.028*** 0.036*

(0.023) (0.010) (0.019)

Program Svc. Rev. 0.074*** 0.048*** 0.048*** 0.042*** 0.049***

(0.021) (0.010) (0.013) (0.015) (0.014)

Non-Mission Inc. 0.047* 0.054**

(0.025) (0.025)

Results (Blundell-Bond): Independent

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VARIABLES Arts Health Human Public Edu wo High High Edu Hospitals Other

Expenses (lag 1) 0.410*** 0.444*** 0.461*** 0.358*** 0.259** 0.392***

(0.137) (0.128) (0.080) (0.073) (0.101) (0.090)

Expenses (lag 2) 0.090** 0.061**

(0.035) (0.029)

Fixed Cost Ratio -1.686*** -2.076*** -1.228*** -1.247*** -2.692***

(0.285) (0.389) (0.139) (0.423) (0.390)

UNA (lag) 0.015** 0.018** 0.006** 0.023***

(0.008) (0.007) (0.003) (0.008)

Age -0.108** -0.104** -0.126*** -0.094** -0.167***

(0.053) (0.046) (0.034) (0.046) (0.048)

HHI 0.533* 0.538*** 0.667**(0.290) (0.185) (0.312)

Results (Blundell-Bond): Diagnostics

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VARIABLES Arts Health Human Public Edu wo High High Edu Hospitals Other

Observations 2,319 2,941 11,829 4,471 3,071 88 229 2,434

Number of ein

957 1,209 4,704 1,745 1,302 36 87 1,023

Wald chi2123.2***

332.1***

523.6***

174.8***

189.9***

33.69***

124.6***

138.1***

Sargan 43.57 48.81 60.49 45.20 55.57 59.80 58.75 59.18

AR(2) 0.725 0.0124 0.173 2.061** -0.527 -0.609 0.191 -0.035

Concerns on the Dynamic Model

• Interpretation of coefficients• Negligible economic impact

• Misspecification concerns– Role of revenue proportion– OVB

23

Fixed Effects Model

• Controls: Age, year dummies; separate sectors• Panel regression using fixed effects

– Exclusion of a revenue type to avoid dummy variable problem

– Hausman tests verify that FE is preferred over RE

24

25

15%

40%

3%

30%

12%

Percentage of Total Revenues, by Revenue Type

Gov GrantsPrivate GivingIndirectProg Svc RevNon-Mission

N=42,254

Results (Fixed Effects): Revenues

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VARIABLES Arts Health Human Public Edu wo High High Edu Hospitals Other

Govt. Grants  -0.003* -0.003*** -0.0116**

(0.002) (0.001) (0.005)

Private Giving  -0.005*** -0.008*** -0.006*** -0.005*** -0.004** -0.024** -0.005***

(0.002) (0.002) (0.001) (0.001) (0.002) (0.010) (0.002)

Indirect Support -0.006** -0.008*** -0.005** -0.071*

(0.003) (0.002) (0.002) (0.042)

Non-Mission Inc. -0.008* 0.004* 0.010*** -0.026***

(0.004) (0.002) (0.003) (0.009)

Revenue Opportunity Cost (Arts)

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VARIABLES w.r.t.Grants

w.r.t.Private Giving

w.r.t.Indirect

w.r.t.ProgramSvc  Rev

w.r.t.Non-

MissionGovt. Grants  . 0.002 0.003 -0.003* -0.006**

(0.001) (0.003) (0.002) (0.003)

Private Giving  -0.002 . 0.001 -0.005*** -0.008***

(0.001) (0.003) (0.002) (0.003)

Indirect Support  -0.003 -0.001 . -0.006** -0.009***

(0.003) (0.003) (0.003) (0.004)

Program Svc. Rev.  0.003* 0.005*** 0.006** . -0.003

(0.002) (0.002) (0.003) (0.003)

Non-Mission Inc.  0.006** 0.008*** 0.009** 0.003 .

(0.003) (0.003) (0.004) (0.003)

Results (Fixed Effects): Independent

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VARIABLES Arts Health Human Public Edu wo High High Edu Hospitals Other

Expenses (lag) 0.124** 0.211*** 0.128*** 0.154**

(0.055) (0.049) (0.023) (0.064)

Fixed Cost Ratio  -0.991*** -1.627*** -1.175*** -0.990*

(0.296) (0.351) (0.391) (0.560)

Age 0.096*** 0.061* 0.533*

(0.035) (0.036) (0.293)

UNA (lag) 0.021*** 0.012*** 0.035*** 0.027*** 0.095** 0.065** 0.011**

(0.005) (0.003) (0.008) (0.007) (0.043) (0.031) (0.005)

Results (Fixed Effects): Diagnostics

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VARIABLES Arts Health Human Public Edu wo High High Edu Hospitals Other

Observations 3,682 4,580 18,010 6,787 4,897 132 339 3,827

R-squared 0.067 0.132 0.084 0.058 0.096 0.312 0.322 0.104

Number of ein

1,318 1,580 6,010 2,230 1,771 44 106 1,352

log likelihood -2445 -4281 -12520 -7113 -4026 -108.8 -478.2 -2169

Adj R-squared

0.0647 0.130 0.0839 0.0568 0.0935 0.249 0.299 0.102

F-Stat 7.124 9.185 21.67 10.79 13.82 2.625 3.303 10.97

Corr 0.0670 0.374 0.183 -0.145 -0.230 -0.913 -0.211 0.109

Concerns on the Fixed Effects Model

• Portfolio analysis techniques– How to find the economic impact rather than

relative impact• Standardize, then regress?• Simply multiply coef and SD -> what do we gain?

– The arbitrary base category: should I run the normal regression with dummies so I can suppress the constant (which isn’t allowable with FE)?

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Limitations (General)• Data– Well-known reliability issues

• Cleaning– Absolute value of expenses– Zeroing of negative revenues and UNA

• Sample– Much smaller N than is available in the general data– Had to drop EZ filers and non-accrual– Only included those that filled out the 990

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Conclusions

• Dynamic– Changes in total expenses are positively related to

changes in revenue type in a way that is statistically significant, but empirically negligible

– Increased fixed costs proportionally has a dampening effect on spending, indicating long-term fiscal prudence

– The rate of adjustment between the status quo and planned expenses varies between 55%-75%, depending on sector

• Fixed Effects– Earned revenues spur greater spending than donations

or government grants.– Impacts of lagged variables are muted compared to

expectations 32

Thank you!

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Elizabeth A.M. Searing, CNPDoctoral Candidate

Andrew Young School of Policy StudiesGeorgia State University

esearing1@gsu.edu