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2003 31: 166Public Finance ReviewArthur C. Brooks

Do Government Subsidies To Nonprofits Crowd Out Donations or Donors?  

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10.1177/1091142102250328ARTICLEPUBLIC FINANCE REVIEWBrooks / GOVERNMENT SUBSIDIES

DO GOVERNMENT SUBSIDIES TO NONPROFITS

CROWD OUT DONATIONS OR DONORS?

ARTHUR C. BROOKSSyracuse University

The traditional test of the public-goods crowding-out effect may mask important impactsof government subsidies on private giving because it confounds the effects of subsidieson average donation size versus their effects on the number of donors. Using panel dataon the U.S. nonprofit sector, the author shows that, although increased public fundinghas a neutral effect on total donations, it is associated with decreased average donationsbut a larger pool of donors.

Keywords: nonprofit; public subsidies; philanthropy

The literature on the economics of philanthropy and nonprofit orga-nizations has examined the public-goods crowding-out question in vari-ous ways for more than 20 years. At issue is whether the effect of govern-ment subsidies to nonprofits displaces (crowds out), encourages (crowdsin), or has a neutral effect on private philanthropic giving to these firms.

At the theoretical level, several authors (Warr 1982, 1983; Roberts1984, 1987) have predicted that complete crowding out will occur;that is, one dollar of government subsidies will displace one dollar ofphilanthropy. In contrast, some theoretical work (Bergstrom, Blume,and Varian 1986; Andreoni 1990; Duncan 1999) has predicted frac-tional crowding out (e.g., a dollar of subsidies will displace less than adollar of donations), a prediction that is also supported by experimen-tal evidence (Andreoni 1993). Few theorists have predicted crowdingin, although this has occasionally surfaced in the literature as well(Rose-Ackerman 1986; Seaman 1980).

AUTHOR’S NOTE: For their helpful suggestions, I am grateful to Jim Alm and two anonymousreferees.

PUBLIC FINANCE REVIEW, Vol. 31 No. 2, March 2003 166-179DOI: 10.1177/1091142102250328© 2003 Sage Publications

166

Most of the numerous empirical investigations on the question haveencountered fractional crowding out on the order of 10 to 50 cents indonations for each dollar in government support.1 A few studies havefound crowding in (e.g., Schiff 1985, 1990; Hughes and Luksetich1999). Recent work (Brooks 2000b) has combined these findings,suggesting that crowding out is not a linear phenomenon but rather de-pends on the level of public subsidies.

Another common empirical finding is that of no significant effect ineither direction, and it is this finding that is especially interesting forthe purpose of this paper. Investigations that have come to this conclu-sion (Reece 1979; Lindsey and Steinberg 1990; Khanna, Posnett, andSandler 1995; Brooks 1999) find the sort of independence betweenfunding sources that much public policy often takes for granted. I sug-gest, however, that these findings may not indicate true neutrality. In-deed, traditional studies of crowding out might be overlooking signifi-cant effects in philanthropic patterns.

Although total donations might not vary with changes in govern-ment intervention, the behavior of individual donors might wellchange. To understand this, note that donative behavior can be sepa-rated into two parts: the give/don’t give decision and providing, the de-cision is GIVE, the decision of how much to donate. These decisionsare manifested in the level of total donations, in that

Dit = Ait × Nit, (1)

where Dit is the total level of donations to firm or subsector i at time t,Ait is the average donation size, and Nit is the total number of donors.Modeling private donations as a function of government subsidies to iat t, Git, as well as a vector of other pertinent variables, θ, yields

Dit = D(Git, �). (2)

Importantly, however, a finding of neutrality between Git and Dit, or∂

∂D

Git= 0, should not necessarily lead one to the conclusion that govern-

ment subsidies do not affect private giving. Assuming that the averagedonation size and the number of donors are also functions of publicsubsidies, and setting to zero, it follows that

Brooks / GOVERNMENT SUBSIDIES 167

NA

GA

N

Git it

∂∂

∂∂

=− ; (3)

that is, a finding of no effect on private donations by public funding in-dicates that either this funding also has no effect on average donationsize and the number of donors, or these variables do indeed change butin opposite directions. Even further, by similar logic it is apparent thata small aggregate crowding-out or crowding-in finding might be hid-ing a large effect in either A or N by leading them to offset one another.Put differently, the traditional test of the public-goods crowding-outeffect may mask important impacts of government subsidies on pri-vate giving because this work confounds the effects of subsidies onaverage donation size versus their effects on the number of donors.

This point may help to resolve the apparent incompatibility be-tween theoretical predictions of positive crowding out and commonempirical findings of negative or zero crowding out. Allowing for het-erogeneous agents, practically any relationship between D and Gcould be consistent with one-to-one crowding out for some agents butcrowding in by others. For example, although new donors might becrowded in by new public money (N rises), complete displacementmight occur with respect to donation size (A falls).

The explanations generally given for crowding in (in either A or N)have to do with the information effects of government grants: Donorsbecome more aware of potential recipients through their receipt ofpublic money (Brooks 1999). On the other hand, the explanations forcrowding out usually revolve around a lower perceived necessity onthe part of nonprofits after receiving public funds. These explanationsmight lead us to predict that higher government support for nonprofitsshould raise the number of donors but lower the average donation. Wemight plausibly assume that there are two types of donors: potentialnew givers and those who have already established a pattern of giving.The former group will probably be more affected by the crowding-inarguments than the latter because people who presently give presum-ably already possess information on recipients. This information pres-ently possessed may also feed the crowding-out effect for the secondgroup, as these donors watch the balance of public funds increase inrecipients’ total revenues. Thus, we might expect higher governmentfunding to nonprofits to crowd in new donors (N rises) and crowd out

168 PUBLIC FINANCE REVIEW

average donations (A falls); A may fall as well if the new donors tend togive less than established ones do.

In the rest of this article, I make these points by example. First, Ipresent the general form of the empirical crowding-out model, as wellas a panel data set to test it, and fit the model along traditional lines.Following this, I enrich and refit the model and data set to examine theimpact on A and N. I present and interpret the results and then offer im-plications and suggestions for future research.

TESTING THE TRADITIONALCROWDING-OUT HYPOTHESIS:

AN EXAMPLE WITH NEGATIVE RESULTS

Although the empirical crowding-out models used in the studiescited above differ in ways specific to the sector they test or data theyemploy, their general form in most cases resembles the following:

Dit = β0 + β1Git + Xitβ2 + Zitβ3 + β4Tt + εit, (4)

where Xit is a vector of socioeconomic characteristics of donors to firm(or subsector) i at time t, such as average age, education level, income,and wealth; Zit is a vector of firm (or subsector) i’s characteristics at t,such as scale of operations, percentage of market controlled, and fund-raising expenditures; and Tt is some measure of the tax benefits of giv-ing philanthropically at time t.

Estimation has been undertaken in a variety of ways in the litera-ture, taking into account the possibility of endogeneity between Git

and Dit. Most notably, these include the use of instrumental variablesor simultaneous systems, or the assumption of an information lag ingovernment support (e.g., either government support this period is notaffected by this period’s philanthropy for some reason or this period’sphilanthropy is regressed on last period’s public support).2

To fit Equation 4, I have assembled panel data on the nonprofit sec-tor and government spending from 1986 to 1995.3 The variables aredefined as follows: Dit = total donated revenues to nonprofit subsectori in year t, where i = social welfare, education, health, arts and culture,the environment, and international relief. Gi, t – 1 = total public spending

Brooks / GOVERNMENT SUBSIDIES 169

(from all levels of government) on subsector i last year, t – 1.4 Xt =GDP (real GDP) and AGE (the percentage of the population older thanage 45) at time t.5 Zit is a vector of dummy variables corresponding toeach subsector (except international relief, which is captured in theconstant term), including welfare, education, health, arts, and envi-ronment. Tt is the average effective federal tax rate for a couple earning$50,000 (with one or more dependents) in year t. Given the laggedvalue of G, the time series in each of the six panels is reduced to 9years, for a total sample of 54. The summary statistics for these data,as well as for the average donation level and number of donors, arecontained in Table 1.

The trends through the time series in G, D, A, and N are depicted inFigures 1 and 2. Both figures are summed across the six subsectors.

It is clear from Figure 1 that both private donations and governmentsubsidies have generally been rising over time, in real terms. Figure 2illustrates that the story is a bit more complicated when total donationsare disaggregated into average donations and numbers of donors. Spe-cifically, although the number of donors has generally been rising (al-though nonmonotonically), average donations have been volatile andnot increasing.

Figures 1 and 2 sum across the nonprofit economy, masking con-siderable differences between the six subsectors in the data. These dif-ferences are evident when we consider the ratio of government to pri-vate funding, as well as that of the average donation to the number ofdonors. These ratios are summarized in Table 2. Certain subsectors(the arts, health) are particularly heavily funded through private dona-tions, whereas others (education, the environment) are mostly fundedthrough government. Similarly, some subsectors (the arts, education,international relief) rely on relatively few, large donations; others (en-vironment, health, welfare) rely on numerous, small donations.

Groupwise heteroskedasticity and within-group autocorrelationare evident in these panel data when I estimate Equation 4 using OLS.To remedy these problems, I first calculate a separate ρ for each paneland transform the data using the Cochrane-Orcutt procedure. Then,the transformed data are used to compute �σ ij = sij = e′iej /8 to correct thecoefficients as follows:

170 PUBLIC FINANCE REVIEW

�β= ′

′

∑∑ ∑∑−

s W W s W Dij j jji

ij j jji

1

, (5)

where Wi signifies the entire right-hand-side set of variables in Equa-tion 4.6 The regression results for Equation 4, using this two-step GLSestimator, are summarized in Table 3.

Brooks / GOVERNMENT SUBSIDIES 171

TABLE 1: Summary Statistics

StandardMean Median Deviation Minimum Maximum

Da $9,174.30 $9,713.20 5,699.45 $997.06 $18,351.00Gb $104.77 $52.83 117.71 $0.16 $355.60Ac $531.87 $471.46 317.78 $56.61 $1,169.07Nd 21.41 20.99 12.74 2.73 42.59GDPb $6,197.69 $6,136.30 328.50 $5,649.50 $6,742.10T 11.43% 11.20% 1.24 10.00% 13.28%Age 31.44% 31.09% 0.66 30.68% 32.63%

a. Millions of 1997 dollars.b. Billions of 1997 dollars.c. 1997 dollars.d. Millions.

$51,000

$52,000

$53,000

$54,000

$55,000

$56,000

$57,000

$58,000

$59,000

1987 1988 1989 1990 1991 1992 1993 1994 1995

Year

$500

$550

$600

$650

$700

$750

Priv

ate

dona

tions

at y

ear

t(M

illio

ns o

f 19

97 d

olla

rs)

Governm

ent funding at year t(B

illions of 1997 dollars)

G t-1

D t

Gt-1

D t

Figure 1: Trends in Private Donations and Government Funding

The most notable result from Table 2 is that the coefficient for lastperiod’s government funding is not significant. As discussed earlier,such a result is not atypical for studies examining the crowding-outhypothesis. However, as I will show, this is far from indicative ofindependence between government spending and philanthropic activity.

Most of the other coefficients are significant. For example, GDPpushes giving up (as expected), and the subsectoral dummies are sig-nificantly positive, indicating that they see significantly higher givinglevels than the control group (international relief). Somewhat surpris-ingly, higher federal tax levels are negatively associated with giving.

172 PUBLIC FINANCE REVIEW

$2,500

$2,750

$3,000

$3,250

$3,500

$3,750

$4,000

1987 1988 1989 1990 1991 1992 1993 1994 1995

Year

80

100

120

140

160A

vera

ge d

onat

ions

at y

ear

t(1

997

doll

ars)

Num

ber of donors at year t(M

illions)

At

N t

80

100

120

140

160

At

N t

Figure 2: Trends in Average Donations and Number of Donors

TABLE 2: Differences between the Subsectors

Subsector G/D A/N

Education 22.118 38.969Health 4.038 15.407Welfare 12.987 13.681Arts and culture 0.018 102.855Environment 17.827 2.203International relief 9.872 43.809

Most studies find the opposite (e.g., Steinberg 1990) because marginaltax rates are the inverse of the “price of giving” under a regime of char-itable tax deductions. However, this is almost certainly a by-productof the fact that GDP is not an especially precise proxy for net house-hold income, and thus the tax rate may be capturing part of the actualincome effect (e.g., net income falls when taxes rise, lowering giving).

ENRICHING THE TRADITIONALEMPIRICAL MODEL

I now redefine the model presented in the previous section to testfor crowding out by parts by fitting the equations

Ait = α0 + α1Gi,t–1 + Xitα2 + Zitα3 + α4Tt + uit, (6)

Nit = γ0 + γ1Gi,t–1 + Xitγ2 + Zitγ3 + γ4Tt + υit, (7)

where the exogenous variables are the same as those described in thelast section.7

Consistent with Equation 3, the finding that government spendingis not significantly related to the level of private donations indicatesthat one of three results should obtain when we fit Equations 6 and 7.

Brooks / GOVERNMENT SUBSIDIES 173

TABLE 3: Estimates of the Simple Crowding-Out Effect

Independent Variable Coefficient Standard Error

Gt – 1 3.82 3.83GDP 0.41* 0.13Age –100.69 64.81T –147.97* 17.67Welfarea 14,964.1* 678.24Educationa 11,545.6* 1322.7Healtha 8,892.63* 2211.78Arts and culturea 7,404.67* 130.67Environmenta 1,224.76* 236.66Constant 3,659.01* 1475.7

NOTE: Dependent variable: Dt.a. Dummy variable.*Significant at the .01 level.

Either α1 = γ1 = 0, α1 > 0 and γ1 < 0 (average donations increase whilethe number of donors falls), or α1 < 0 and γ1 > 0 (average donations fallwhile the number of donors rises). The results in Table 4 show that,consistent with the a priori expectations, the last result is the one foundin these data.8

The coefficients in both regressions on government spending aresignificant at appropriately high levels. Their signs and magnitudesindicate that an additional billion dollars in government spendingleads to approximately 75 cents less in the average private donationbut the participation of an additional 30,000 donors. In the first case,the 75-cent figure should perhaps not be interpreted as especially lowbecause this refers to an extra $1 billion in total spending in thesebroad areas, not just in subsidy to nonprofit firms.

Other effects in these regressions, hidden in the traditional model,are of interest as well. First, increases in income (GDP) are associatedwith decreases in A but increases in N. This may be a function of new,small givers in a good economy swamping increases in the averagegift size. Second, as more of the population falls into the age group atwhich philanthropic giving is generally highest (45 and above), N fallsbut A rises; that is, later in life, people tend to sort themselves morestrictly into givers and nongivers, and the givers give more, on aver-age, than they did earlier in life. The dummy variables are fairly con-

174 PUBLIC FINANCE REVIEW

TABLE 4: Estimates of Crowding-Out Effect on Donations and Donors

Dependent Variable: At Dependent Variable: Nt

Standard StandardIndependent Variable Coefficient Error Coefficient Error

Gt – 1 –0.75* 0.16 0.03* 0.000GDP –0.20* 0.023 0.01* 0.001Age 99.8* 13.02 –6.5* 0.64T 6.26 4.17 –0.89* 0.24Welfarea 299.52* 74.33 25.02* 1.62Educationa 709.86* 84.83 4.47* 1.64Healtha 119.35 74.73 21.95* 1.88Arts and culturea 699.42* 51.5 4.75* 0.49Environmenta –206.02* 60.13 29.62* 2.25Constant –1,609* 321.92 144.73* 17.83

a. Dummy variable.*Significant at the .01 level.

sistent in sign and significance between the three models, except in thecase of environmental giving, which is lower in average gifts than thecontrol group.

The differences in A and N between the subsectors in these data, ex-posed in Table 2, prompt a cautionary note about the regression re-sults. It is clear that some subsectors have a few large donors, whereasothers rely on many smaller ones. Hence, notwithstanding my empiri-cal attempts to neutralize panel complications in these estimations,some of the effects seen in Table 4 may be a by-product of subsectoralheterogeneity. For example, it may be that environmental groups tendto see little crowding out because their many, small donors are un-likely to be informed of government subsidy levels (low donationswould make donors less likely to incur philanthropic informationcosts, all else equal). Coupled with a high ratio of G to D, this couldprop up N for the full panel. One could imagine a similar argumentabout A for arts and culture organizations, which have a high ratio of Ato N and a low ratio of G to D.

SUMMARY AND IMPLICATIONS

In this article, I have argued that the traditional test of the public-goods crowding-out effect does not give a full description of the im-pact that government funding might have on donative behavior be-cause it gives no information on changes to the size or number of do-nations. Consequently, a finding of independence between publicsubsidies and the total amount of private giving might not indicate trueneutrality between the two revenue sources but rather a countervailingeffect between average donation size and the number of donors. In thisarticle, I have made this point using panel data on the U.S. nonprofitsector. Although the goal was to present an example as opposed to set-tling the issue empirically in a comprehensive way, the regression re-sults are nonetheless suggestive: Although increased public funding isinsignificantly related to total private giving, it is associated with de-creased average donations and increased numbers of donors.

This finding might be helpful in settling the apparent inconsistencybetween some theoretical predictions and many of the empirical find-

Brooks / GOVERNMENT SUBSIDIES 175

ings about crowding out. At the most basic level, the crowding-out hy-pothesis is not supported by the finding of independence or negativedisplacement of private donations. At a slightly deeper level, however,these findings are not necessarily incompatible at all. Relaxing the as-sumption of agent homogeneity, we can envision one group of donorsthat is crowded in on the give/don’t give decision and another that ex-periences (potentially complete) crowding out in the decision of howmuch to give.

From a public policy standpoint, if this is indeed the case, it mightsuggest a role for public dollars in the “democratization” of philan-thropy. If government funding indeed pushes up the number of peoplewho donate to nonprofits while not crowding out total donations, itsinvolvement might have a social impact beyond just aiding these firmsand subsectors. In the opinion of some, the decrease in average dona-tions might be a price worth paying for broader philanthropic involve-ment. On the other hand, it may be the case that government subsidiesare nonuniform across the nonprofit economy and do not enhance anyphilanthropic democratization. I discussed one possibility in theprevious section. Another scenario involves the concentration of sub-sidies to nonprofits with relatively few, politically powerful donors,who subsequently give somewhat less (thus depressing A but not N).This implies a potentially complex, politically involved crowding-outprocess not apparent in a simple data analysis.

The results in this article suggest two potentially fruitful areas offuture research. First, my findings are based on a relatively small,highly aggregated data set. Data of finer granularity can make betterconsideration of a number of important technical points. For example,Steinberg (1993) showed that the type of specification I employ heresuffers from bias owing to the interaction between funds flowing fromdifferent levels of government. Similarly, Kingma (1989) argued thataccurate estimation of the crowding-out effect necessitates disaggre-gating funds to different firms, such that crowding out can be distin-guished from simple substitution between different public goods bydonors. And as I have already noted, some of the detected divergencebetween donors and average donations may be due to subsector heter-ogeneity, meaning that replication using data on individual subsectors(or even individual firms) would be useful.9

176 PUBLIC FINANCE REVIEW

The second area of future research is theoretical. The models citedearlier could be enriched by allowing for some heterogeneity of agentsor firms. Alternatively, the process of government funding might bemodeled as a nonuniform phenomenon across the nonprofit economy,such that “winners” and “losers” with different donor characteristicswould affect the composition of donations in different ways.

NOTES

1. For a comprehensive survey of these studies, see Brooks (2000a).2. Another issue of endogeneity involves what Steinberg (1993) referred to as “joint

crowdout,” in which different levels of government support affect one another.3. These data were assembled from figures published in Giving USA by the AAFRC Trust for

Philanthropy, the Statistical Abstract of the United States, United States Budget data, and theEconomic Report of the President for various years.

4. Note that this includes all spending in these general areas, not just to nonprofit firms in thissubsector.

5. Studies on giving patterns (Clotfelter 1980; Feldstein and Clotfelter 1976) show that phi-lanthropy after middle age is (in general) much higher than earlier in life.

6. This procedure is described in greater detail in Greene (1998, 347-50).7. Data for A and N were gathered by the Independent Sector and published in Giving and

Volunteering in the United States over various years. These data are only collected in odd-numbered years; consequently, the figures for 1988, 1990, 1992, and 1994 were imputed usingthe odd-numbered observations as well as the data on total private giving.

8. Equations 6 and 7 are estimated using the same procedure as Equation 4.9. The trouble with assembling such a data set is that data on average donations and number

of donors tend to be unavailable. These figures are not included on IRS Form 990.

REFERENCES

AAFRC Trust for Philanthropy. Various years. Giving USA. Indianapolis: AAFRC Trust for Phi-lanthropy.

Andreoni, James. 1990. Impure altruism ands donations to public goods: A theory of warm-glowgiving? Economic Journal 100 (401):464-77.

. 1993. An experimental test of the public-goods crowding-out hypothesis. AmericanEconomic Review 83 (5): 1317-27.

Bergstrom, Theodore C., Lawrence E. Blume, and Hal R. Varian. 1986. On the private provisionof public goods. Journal of Public Economics 29:25-49.

Brooks / GOVERNMENT SUBSIDIES 177

Brooks, Arthur C. 1999. Do public subsidies leverage private philanthropy for the arts? Em-pirical evidence on symphony orchestras. Nonprofit and Voluntary Sector Quarterly 28 (1):32-45.

. 2000a. Is there a dark side to government support for nonprofits? Public AdministrationReview 60 (3): 211-18.

. 2000b. Public subsidies and charitable giving: Crowding out, crowding in, or both?Journal of Policy Analysis and Management19 (3): 451-64.

Clotfelter, Charles T. 1980. Tax incentives and charitable giving: Evidence from a panel of tax-payers. Journal of Public Economics 13:319-40.

Council of Economic Advisors. Economic report of the president. Various years. Washington,DC: Government Printing Office.

Duncan, Brian. 1999. Modeling charitable contributions of time and money. Journal of PublicEconomics 72:213-42.

Feldstein, Martins, and Charles T. Clotfelter. 1976. Tax incentives and charitable contributionsin the United States: A microeconomic analysis. Journal of Public Economics 5 (1-2):1-26.

Giving and volunteering in the United States. Various years. Washington, DC: IndependentSector.

Greene, William H. 1998. LIMDEP user’s manual, version 7.0. New York: Econometric Soft-ware.

Hughes, Patricia Nold, and William Luksetich. 1999. The relationship among funding sourcesfor art and history museums. Nonprofit Management & Leadership 10 (1): 21-37.

Khanna, Jyoti, John Posnett, and Todd Sandler. 1995. Charity donations in the U.K.: New evi-dence based on panel data. Journal of Public Economics 56:257-72.

Kingma, Bruce R. 1989. An accurate measure of the crowd-out effect, income effect, and priceeffect for charitable contributions. Journal of Political Economy 97 (5):1197-207.

Lindsey, Lawrence, and Richard Steinberg. 1990. Joint crowdout: An empirical study of the im-pact of federal grants on state government expenditures and charitable donations. NationalBureau of Economic Research working paper no. 3226.

Reece, W. S. 1979. Charitable contributions: New evidence on household behavior. AmericanEconomic Review 69:142-51.

Roberts, Russell D. 1984. A positive model of private charity and public transfers. Journal of Po-litical Economy 92:136-48.

. 1987. Financing public goods. Journal of Political Economy 95:420-37.Rose-Ackerman, Susan. 1986. Do government grants to charity reduce private donations? In The

economics of nonprofit institutions: studies in structure and policy, edited by Susan Rose-Ackerman, 313-29. New York: Oxford University Press.

Schiff, Jerald. 1985. Does government spending crowd out charitable contributions? NationalTax Journal 38:535-46.

. 1990. Charitable giving and government policy: An economic analysis. Westport, CT:Greenwood.

Seaman, Bruce A. 1980. Economic models and support for the arts. In Economic policy for thearts, edited by William S. Hendon, James L. Shanahan, and Alice J. MacDonald, 80-95.Cambridge, MA: Abt.

Steinberg, Richard. 1990. Taxes and giving: New findings. Voluntas 1:61-79.. 1993. Does government spending crowd out donations? Interpreting the evidence. In

The nonprofit sector in the mixed economy, edited by Avner Ben-Ner and Benedetto Gui, 99-125. Ann Arbor: University of Michigan Press.

178 PUBLIC FINANCE REVIEW

United States budget, historical tables. Various fiscal years. Washington, DC: GovernmentPrinting Office.

U.S. Bureau of the Census. Various years. Statistical abstract of the United States. Washington,DC: U.S. Bureau of the Census.

Warr, Peter G. 1982. Pareto optimal redistribution and private charity. Journal of Public Eco-nomics 19:131-38.

. 1983. The private provision of a public good is independent of the distribution of in-come. Economics Letters 13 (3): 207-11.

Arthur C. Brooks is an associate professor of public administration at Syracuse Univer-sity’s Maxwell School of Citizenship & Public Affairs. His research focuses on nonprofitorganizations, philanthropy, and the economics of arts and culture.

Brooks / GOVERNMENT SUBSIDIES 179