5/2006
More than a Simple “Crowd-out”: The Case for
NEA Grants and US Dance Companies
Thomas M. Smith
Department of Economics
University of Illinois-Chicago
601 S. Morgan (m/c 144)
Chicago, IL 60607
(312) 355-3983
TomSmith@uic.edu
Abstract
This paper tests the crowding-out hypothesis for a balanced panel of nonprofit dance
companies in the United States between 1998 and 2003. This research uses a number of
model specifications to examine the impact of National Endowment for the Arts (NEA)
grants and Non-NEA grants on private donations. The results suggest that NEA grants
have a large ‘joint’ crowd-out effect on private donations, and a small positive ‘joint’
crowd-in to non-NEA grants. The end result is that NEA grants have a modest ‘simple’
crowd-out to nonprofit dance companies.
Keywords: Crowding-out, Nonprofit Dance Organizations
JEL L3
1. Introduction
Nonprofit organizations receive income from a number of sources: fees for
service, dues, rental of space and/or equipment, government grants, and private
donations. Researchers have focused on the relationship between the latter two
categories—government grants and private donations. The empirical and theoretical
models examining this relationship revolve around the idea that individuals will perceive
government dollars as substitutes, and give less (Abrams and Schmidt, 1984). This
‘crowding-out’ between government grants and private donations ultimately results in
nonprofit organizations receiving less total donated revenue.
This paper examines the relationship between government grants, specifically
National Endowment for the Arts grants, and private donated revenue for nonprofit dance
organizations in the United States. There has been some research addressing the
crowding-out issue for specific arts organizations--Kingma (1989) with public radio,
Brooks, (2000b) with symphony orchestra, and Smith (2003) with dance; and some
research that has specified types of arts organizations—museums and art galleries—
within larger samples (Weisbrod and Dominguez, 1986; Okten and Weisbrod, 2000;
Tinkleman, 2004). This paper uses a balanced panel of nonprofit dance companies
between 1998 and 2003 from the National Center for Charitable Statistics (NCCS)
Digitized Data and the National Endowment for the Arts-Dance Applicant dataset.
This paper is organized as follows. A brief discussion of the literature is
presented in the second Section. The dataset and empirical models are outlined in Section
3. The results are discussed in Section 4 and Section 5 provides a conclusion and policy
implications.
1
2. Crowding-In or Crowding-Out in the Arts
The relationship between public and private money to nonprofit organizations has
been examined by many researchers (see Steinberg, 1991, for a literature review). The
alternative hypotheses that emerge from the body of work are: 1) donors view
government funding as a replacement for their gifts, completely crowding-out private
donations (Roberts, 1984); 2) government funding is viewed as an imperfect substitute
for private donations resulting in incomplete crowding-out of private dollars (Andreoni,
1990; Steingberg, 1991) or; 3) that government funding signals a nonprofit’s worthiness
for support, creating a crowding-in effect (e.g. Payne 2001). There does not appear to be
a predictable direction of the crowding effect based on industry.
Several papers have examined the relationship between government and private
contributions specifically in the arts: Kingma (1989), Brooks (1999, 2000a, 2000b, 2003),
Okten and Weisbrod (2000), and Smith (2003). Kingma (1989) examines the
relationship between public and private funding with respect to nonprofit “public” radio.
The results indicate that public funding has a significant crowding-out effect on private
donations in the range of $0.14. Brooks (2000b) finds a positive crowding-in between
public funding and private funding to symphony orchestras in the amount of
approximately $2.50. However, the relationship between public and private funding is
non-linear and larger (or continued) grants crowd-out private contributions. Okten and
Weisbrod (2000) examine the relationship between public and private grants for a range
of nonprofit organizations, including arts exhibits (galleries), and find no significant
relationship between public and private donations. Smith (2003) looks specifically at the
impact of National Endowment for the Arts funding to nonprofit dance companies and
2
finds a modest crowding-in effect around $3. In another examination of public radio,
Brooks (2003) finds a positive and non-linear relationship between public and private
funding in public radio.
3. Data and Model
This research uses a balanced panel of nonprofit dance companies from the
National Center for Charitable Statistics Digitized data 1998-2003 ( NCCS Digitized 9803). The information in the NCCS was collected from the IRS Form 990—the tax form
used to report finances of 501(c)(3) organizations with more than $25,000 in annual gross
receipts.
The nonprofit dance companies in this analysis possess the National Taxonomy of
Exempt Entity codes issued by the IRS: A62 for dance. For this analysis, there are dance
and ballet companies for each year 1998 to 2003. Similar IRS Form 990 data used by
Payne (1998), Okten and Weisbrod (2000), Andreoni and Payne (2003), and Tinkleman
(2004) —the IRS’s Statistics of Income (SOI) tapes for 1982,1983, and 1985-1994—only
include companies in the upper registry of gross receipts, while the NCCS includes
companies that report gross receipts as low as $25,000 and as high as $500 million. The
descriptive statistics of these data are listed in table 1.
The model examining the effect of public grants on private donations is outlined
in Steinberg (1991) and has been presented recently in Khanna, Posnett and Sandler
(1995), Payne (1998), Okten and Weisbrod (2000) and summarized in Andreoni (2004):
1) Dit = Priceit +  Govtit + Fundit+ PSRit + Xit+it
3
where Dit refers to private monetary donations to the ith company at time t, the Priceit
variable is discussed and defined below, Govtit is government grants to the ith company
in the current period1, Fundit is fund-raising expenditure for the ith company, PSRit is the
program service revenue for the ith company, X is a vector of other co-variants—
including dichotomous variables identifying the state where the nonprofit is located,
dichotomous variables identifying the year, and the per-capita income2 for the state and
period—and ε is a random disturbance.
Steinberg (1991) decomposes the government grants into two separate parts:
2) Dit = Priceit +  GovtFEDit +GovtSTATEit+ Fundit+ PSRit + Xitφ+it
such that government grants reflect both federal and state level payments. Because
federal government dollars have an impact on state government dollars and private
donations play a role in the decisions of state granting agencies, the correct reduced form
equations are
3a) Dit = GovtFEDit + ρ2it Ψ +v1it
3b) GovtSTATEit = GovtFEDit +ρ1it Ψ +v2it
The interpretation of and  are of ‘joint’ crowd-out (positive or negative). The
‘simple’ crowd-out can be determined by the ratio of the two coefficients
1
 2 1
. Much
of the research on establishing crowd-out uses a modified version of equation 1), of
which the coefficient is interpreted as the ‘simple’ crowd-out. If equation 1) is
performed with OLS, the coefficient β2 is bias. However, if the values of government
1
Current private donations have been modeled as a function of current government grants (Posnett and
Sandler, 1989 and Payne, 1998) and as a function of lagged government grants (Okten and Weisbrod,
2000).
2
Other measures of economic performance or the political-economic environment have been included
(Payne, 1998).
4
grants are approximated using an instrumental variable technique to account for
endogeneities, the coefficient is un-bias (Steinberg, 1991; Kingma, 1989). Un-bias
estimates of simple crowd-out can also be determined by estimating equations 3a) and
3b).
This research will test the relative impact of government grants, including both
NEA direct grants and state-level grants, on private donations to determine both ‘joint’
and ‘simple’ crowd-out.
Price
The Priceit variable is defined by Okten and Wesibrod (2000) 3 as
Priceit = (1-T)/ (1 – Fit-1)
Where T is the marginal income tax rate facing an individual donor and Fit-1 is the ratio of
fundraising expenditure to total donations for the ith firm in the previous period. The
numerator of this variable was changed to 1 for Tinkelman (2004) because taxes are
constant across all firms.
There are several other specifications that with respect to the Price variable. For
companies where Donit-1 = Fundit-1, Fit-1 = 1 and Price is undefined by calculation. In
these cases, where Donit-1 = Fundit-1 at some positive amount, Price is equal to 148.4,
log(Price) = 5, consistent with Weisbrod and Dominquez (1986). However, where Donit-1
= Fundit-1 = 0, then Fit-1 would be undefined. In these cases, Price is equal to 1. In the
cases where fundraising is larger than donations, Price is negative. Weisbrod and
Dominquez (1986) excluded cases with a negative Price from their analysis. In order to
3
Weisbrod and Dominguez (1986) identified Price = 1(1-F-A), where F is the ratio of fundraising
expenditures to donations and A is the ratio of administrative expenditures to donations. The form 990 data
used in this analysis does not provide a meaningful measure of A. The price variable without A should not
have an adverse impact on the measure of effective firm practice on individual contributions.
5
retain the balanced panel, these observations will not be dropped. Instead, where Fundit-1
> Donit-1 , Price = (Price)2 up to a maximum of 148.4.
A True Panel?
One of the critiques of using the SOI tapes is that the returns for a particular year
include organizational information from different fiscal years. That is, the 1999 SOI tapes
may include information for organization “A” from fiscal year 1998 and information for
organization “B” from fiscal year 1999. As such, using observations from sequential SOI
tapes may not represent a true panel. It can also be the case that information from two
sequential SOI tapes may not represent consecutive fiscal years for a specific
organization (Tinkleman, 2004). The NCCS however, is organized by fiscal year. As
such, the 1998 NCCS includes financial data for an organization’s 1998 fiscal year. This
analysis only includes nonprofit arts organizations that have complete information (nonmissing IRS Form 990) for each year 1998 through 2003. Likewise, the information in
the NEA-DA is organized by fiscal year of the company and grant year—the year the
NEA granted money (or didn’t grant money) to the organization. As such, the
information from the NEA-DA can be merged with the NCCS data for a consistent fiscal
year.
4.1 Estimates of ‘Simple’ Crowd-out Using Total Grants
OLS and TOBIT
The analysis of equation 1 was estimated using OLS. The results of the OLS
model suggest that current government grants (column 1) crowd-out $0.65 in private
contributions. This estimate is statistically significant. This indicates that the average
nonprofit dance company has a net gain of $35 for every for every $100 received from
6
government sources : a gain of $100 from government sources is accompanies by a loss
of $65 from private sources. Other results from the OLS estimates indicate that every
dollar of fundraising increases private donations by more than $6.65. Companies that
receive more from ticket sales and other program service revenue also receive more in
private donations in the range of 60%. Higher state-level per-capita income has a
positive but insignificant impact on private donations. The price of donating has a
negative but insignificant impact on private donations.
The estimates using the Tobit technique are reported in column 2. The Tobit
results indicate that every dollar in current government grants crowds-out $0.47 in private
support for the average nonprofit performing arts organization. The Tobit estimates for
fundraising and program service revenue—$6.73 and $0.593—are also very similar to the
OLS estimates. The similarity between the Tobit and OLS estimates are consistent with
the fact that only 25 percent of the observations in this sample have a zero value for the
dependent variable (McDonald and Moffitt, 1980). With the Tobit estimates, the impact
of per-capita income is negative (although not significantly different than zero). The
price variable for both Tobit estimates is also negative but insignificant. Both the OLS
and Tobit estimates suggest t a crowding-out effect of government grants on private
donations.
Instrumental Variable: 2SLS
As indicated, some researchers have suggested that there may be an endogeneity
issue between government grants and private donations—e.g. omitted variables effecting
both government and private dollars (Payne, 1998; Okten and Weisbrod, 2001; Andreoni
and Payne, 2003). In order to correct for this, this paper employs an instrumental
7
variable technique. In the first stage, current government grants are estimated using
state-level legislative appropriations to state arts agencies and state arts councils in the
state where the nonprofit organization is located. Although some researchers have used
state level government transfer payments or total state level government expenditures to
approximate the “size of the pie (Andreoni and Payne, 2003, p. 805)” that nonprofit
organizations are fighting for, this research uses the appropriations to the agencies
responsible for determining grants. In effect, this research is approximating the size of
the serving fork used to distribute the pie. Because government grants can come from
both the state and federal level, using a state level proxy does not simply replace the
actual government grants. The F-statistic for the first stage (22.67) indicates that the
choice of state-level appropriations is a good instrument. The IV estimates for
government grants indicates that every $1 received by a company from the public sources
crowds-out $1.56 in private contributions. This estimate is twice the size of both the
OLS and Tobit estimates.
Fixed-Effects
One of the advantages of this dataset is that it includes information on the same
arts organizations across periods. These data represent a true panel of organizations by
fiscal year4. As such, equation 1 can be estimated under a firm fixed-effects model. The
results of the firm fixed-effects estimation are listed in column 4 of table 1. The null
hypothesis of no fixed effects is rejected with an F value of 8.62. The coefficient for the
government grants indicates that one dollar of government funding crowds-out $1.03 in
private funding. The magnitude of the government coefficient for the fixed-effects
4
For this research, companies were only used if there were returns for each of the four fiscal years, 19982003.
8
model is larger than that found in the non-fixed effect model (OLS and TOBIT), but is
smaller than the estimate using 2SLS. This result is consistent with other estimates using
fixed-effects find that government grants that find positive ‘reputation effects’ (Payne,
1998).
4.2 Estimates of ‘Joint’ and ‘Simple’ Crowd-out Using NEA Grants
The analysis of equation 1 using OLS estimates the ‘simple’ crowd-out—the
relationship between government grants and private donations. The estimate of the
relationship between government grants and private donations are bias if there are
endogeneities. Un-bias ‘simple’ crowd-out can be established by analyzing the reduced
form of the crowd-out equation, equations 3a) and 3b). These estimates reveal that NEA
grants crowd-out $2.10 in private donations and that NEA grants crowd-in $0.42 in statelevel government grants. The ‘simple’ crowd-out of government grants are $1.47. The
fixed-effects estimates of ‘joint’ crowd-out reveal that NEA grants crowd-out $4.07 in
private contributions and crowd-in $0.78 in state-level grants. The ‘simple’ crowd-out of
government grants are $2.28. The estimates of simple crowd-out using both OLS and
Fixed-Effects are very similar to the estimates of simple crowd-out using instrumental
techniques.
5. Conclusion
This research uses a panel of nonprofit dance organizations from 1998 to 2003 to
examine the impact of government grants on private donations. Theory suggests that
individuals will substitute public money—in whole or partially—for their own donations.
This substitution will lead to crowding-out (complete or partial) between government
9
grants and private funding. Using several estimating techniques, this research has
provided evidence that, on average, government grants crowd-out private donations to
nonprofit performing arts organizations in the range of $1.50-$2.00.
10
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Steinberg, R (1991), “Does Government Spending Crowd Out Donations? Interpreting
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13
Table 1. Descriptive Statistics
Variable
Independent
Privatet
Dependent
Govt
NEAgrantt
STATEgrantt
PSRt (ALL)
FundExpt
Pricet

Mean
Std Dev
Private dollars donated to dance organizations
in current period
1,362
229,959
1,019,263
Total Government support to all dance
organizations
Government Support to all dance organizations
in current period
Non-NEA grant dollars to dance organizations
Program Service Revenue
Fundraising Expenditure
1,362
46,068
113,197
1,362
13,204
26,125
1,362
1,362
1,362
32,864
50,124
24,692
8,1324
1
Where Ft is the ratio of fundraising
1  Ft
1,362
1.39
4.92
Description
expenditure to total contributions
14
Table 3. Impact of Government Funding on Private Donations
Dependent variable: Dollars of private donations
Estimating Technique
One-Way
Variable
Intercept
Pricet
Governmentt
FundRaiset
PSREVt
Year 1999b
Year 2000
Year 2001
Year 2002
Year 2003
PerCapYjt
State Effects
N
R2
LogL
Fa
1. OLS

S.E.)
-51,108
34,556
-708.62
1,638.71
-0.652
0.1492
6.645
0.2911
0.5954
0.028
23,49
47,866
-4,079
47,847
-19,662
47,933
45,525
47,907
16331
47918
10.205
11.015
YES
1362
0.7499
2.Tobit

S.E.)
-124,600
40,177
-710.94
1802
-0.4688
0.1648
6.7385
0.3194
0.5938
0.0315
-10,094
55,644
-15,930
55,600
-30,947
55,724
36,629
55,452
8,179
55,382
15.26
11.201
YES
1362
3 I.V.
7. Fixed Effects


S.E.)
S.E.)
-45241
23,395
37852
35754
-654.21
-623.30
1780
1,681
-1.58
-1.031
0.215
0.1804
6.542
7.142
0.3554
0.3655
0.6841
0.7280
0.0312
0.0362
-9,514
-49,885
-10,014
-49,080
-21,015
-49,125
28,014
-49,225
5,414
-49,001
11.02
-8.05
YES
NO
1362
1362
0.2147
-15322
8.62
BOLD: Significant at 0.10 or greater
a: H0 is no fixed effects
b: Benchmark Year is 1998
15
Table 4. Impact of NEA Funding
Estimating Technique: 1. OLS
Dependent variable:
Variable
Intercept
Pricet-1
NEAt
FundRaiset
PSREVt
Year 1999
Year 2000
Year 2001
Year 2002
Year 2003
PerCapYjt
STATE
N
R2
OLS
One-Way
Fixed Effects
One-Way
Fixed Effects
DON
STATE
DON
STATE

S.E.)
-68,593
34,579
-521.29
1,649.46
-2.102
3.118
6.087
0.2639
0.6013
0.0287
5073
48206
-7821
48171
-23625
48255
46782
48242
18582
48292
10.28
9.025
YES
1362
0.7485

S.E.)
26,396
6,244
-238.82
297.87
0.4277
0.5630
0.8517
0.0476
-0.0083
0.0052
-3526
8705
5489
8699
5982
8714
-1516
8712
-2257
8721
5.021
3.212

S.E.)
27404
36255
-554.00
1704.5
-4.07
2.883
6.603
0.3570
0.714
0.0370
--

S.E.)
-5080
5871
-65.70
276.05
0.7816
0.4669
0.5420
0.0578
0.0117
0.0059
--
--
--
--
--
--
--
--
--
6.245
4.451
4.215
3.211
1362
0.3255
1362
0.8019
1362
0.2772
BOLD: Significant at 0.10 or greater
a: H0 is no fixed effects
b: Benchmark Year is 1998
.
16