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The Price Elasticities of Charitable Contributions: A Meta-Analysis
By
John Peloza, University of Calgary
Piers Steel, University of Calgary
1
The Price Elasticities of Charitable Contributions: A Meta-Analysis
ABSTRACT
Tax deductibility has been recognized as a motive for charitable donations. The current
paper considers charitable donations as purchases made by consumers and examines the
effects of changes in the tax deductibility (i.e., the price of donating) on charitable
donations. The meta-analysis includes almost four decades of estimates of the price
elasticity of charitable giving. Implications for policymakers and the marketers of
charities are discussed.
2
The Price Elasticities of Charitable Contributions: A Meta-Analysis
“If charity cost nothing, the world would be full of philanthropists.”
– quoted in Leo Rosten’s Treasury of Jewish Quotations
INTRODUCTION
Charitable giving is prevalent. Eighty-nine percent of Americans donated to
charity in 2001 (Sullivan 2002), and an estimated $200 billion was donated by
individuals to charities in the United States alone in 2000 (Lindahl and Conley 2002).
This generosity is in part due to altruistic motivations, but researchers have also argued
that egoistic motives play important roles in the decision to donate to charity (Bendapudi
Singh and Bendapudi 1996; Piliavin and Charng 1990). Altruistic motives focus on the
benefits received by the recipient while egoistic motives are based on the reward given
back to the donor, including intangibles such as self- and other-esteem. Consequently,
Smith (1980) argues that an increase in the cost of giving can be expected to lead to a
decrease in charitable support for many charitable organizations. Specifically, researchers
have found tax incentives to be egoistic motivators for donation to charity (Cermak File
and Prince 1994; Dawson 1988).
For the donor, the level of tax deductibility determines the “cost” of the
contribution. Therefore, when the tax deductibility of donations changes so does the cost
of the purchase. Steinberg (1990) argued that donations, therefore, are no different than
any other purchase made by consumers. The current paper examines the effects of
changes in the tax deductibility of donations on charitable support. If changes in tax
deductibility have a disproportional effect on charitable contributions, public policy
3
concerning the tax deduction can be used as an effective stimulus for increasing
charitable support. In other words, if a reduction in tax costs of $1 results in an increase
of more than $1 in charitable contributions, such tax policy can be effective support for
charitable organizations. This potential for lower tax costs of donations to lead to
increased charitable support has important implications for both governments that allow
tax deductions, and to charities that rely on donations from individuals. For example,
such an effect would allow the government to justify the transfer of responsibility for the
provision of some public services to charities and nonprofit groups.
The literature examining the effects of changes in tax deductibility of charitable
donations has produced mixed results, with the debate spanning the past four decades
(Auten Sieg and Clotfelter 2002). The current paper uses meta-analytic techniques to
provide robust estimations of the elasticities of charitable donations with respect to
changes in tax deductibility, and estimates moderators affecting those elasticities.
The paper is organized as follows. First, a brief overview of the debate concerning
the efficiency of the tax deduction for charitable donations is presented. Next, the
benefits of examining the price elasticities of donations through meta-analytic techniques
are discussed. Hypotheses concerning the effects of changes in tax deductibility of
charitable donations are then presented. Results of the meta-analysis are presented, along
with a discussion and future research opportunities.
THE EFFICIENCY OF CHARITABLE DONATIONS
Enacted in the U.S. in 1917, the charitable donation tax deduction was designed
so that income tax did not discourage charitable giving, and these deductions are often
seen as a close substitute for government programs (Clotfelter and Steuerle 1981).
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Historically, government funding has been second only to individual donations as the
largest source of operating revenue for nonprofits (Kirchhoff 2003). However since the
1980’s, governments have been re-evaluating and reducing their role in the delivery and
support of social services (Larson 1995). This trend is global, occurring in the U.S.
(Miller 1998), Canada (Foster and Meinhard 2003), Australia (Bednall et al. 2001), New
Zealand (Chaney and Dolli 2001) and Europe (Olabuenaga 2000), and this has pushed
many charities to rely even further on individual consumers as a key source of funding.
When governments forego tax revenue in exchange for charitable donations it can be
considered an indirect form of charitable support on the part of the government. The
aggregate amount of these deductions is significant, with estimated government revenue
lost due to the deduction in the U.S. from 2001-2005 to be $145 billion (Colombo 2001).
The price of charitable contributions is the portion of the donation not returned in
the form of a tax deduction or credit. For individuals who itemize charitable deductions
on their tax returns, the price of an additional dollar in contributions is (1 - Tm), where Tm
is the marginal income tax rate (Reece 1979). Bittker (1972) states: “In the indictment of
tax deductions for charitable contributions, the charge is that they are inefficient because
such a large fraction of charitable gifts would be forthcoming in any event that the
incremental contributions stimulated by the deduction are too small to justify their cost”
(p. 155). However, a policy of increased tax deductibility can be efficient if a drop in the
tax cost (i.e., the cost of the donation) results in a disproportionate increase in donations.
Steinberg (1990) labelled such programs treasury efficient, and states that programs are
efficient if and only if charitable giving is price elastic.
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The price elasticity of giving is defined as the percentage change in donations
resulting from a 1 percent change in the price of giving, all else equal (Steinberg 1990). If
price elasticity is a negative number, it means that a decrease in the price causes an
increase in giving. Under the assumption of a reasonably informed donor, for whom the
price of donation is at least somewhat important, price elasticities can be expected to be
negative. If elasticity exceeds 1 (in absolute value), giving is said to be elastic; otherwise,
giving is inelastic. For example, for negative but inelastic elasticities of charitable giving,
in considering the additional savings from the reduced cost, “people keep some and give
some away” (Hood Martin and Osberg 1977, p. 661). The current paper focuses on
negative elasticities, those that are theoretically supported based on a reasonably
informed donor (although two studies examined here provide estimates of positive
elasticity, the authors of those studies are unable to provide theoretical rationale
supporting positive price elasticities). “Lower” price elasticities refer to those elasticities
that are lower in absolute value (i.e., less elastic). In other words, an elasticity of -1.5 is
considered less elastic (i.e., lower) than an elasticity of -2.5.
Tellis (1988) defines elasticities as “the ideal measure for the meta-analysis, being
both units-free and easily interpreted” (p. 332). The majority of researchers use
regression to compute the price elasticity of charitable giving. However, correlational
data for the relationship between price (tax deductibility) and charitable giving would be
preferred, given the potential for other variables in the regression model to confound the
results. Fortunately, the relationship between correlations and elasticities (i.e., beta
coefficients) is typically strong (Peterson and Brown, 2005). Also, many of these
variables can be captured as potential moderator variables through the meta-analysis
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process. Furthermore, the use of regression-based data in the form of elasticities for meta-
analysis has a long history across a variety of disciplines including marketing (e.g.,
Andrews and Franke 1991; Assmus Farley and Lehmann 1984; Tellis 1988), economics
(Kremers Nijkamp and Rietveld 2002) and public health (Gallet and List 2003).
WHY A META-ANALYSIS?
The effects of tax rates and income on charitable contributions have been studied
extensively. However, the results to date span a wide range of samples, examine a diverse
set of variables and data sources, and often present conflicting findings. Estimates for
price elasticity range from -7.07 (Robinson 1990) to +.12 (Wu and Ricketts 1999).
Duquette (1999) states that “most studies have found giving to be highly price elastic” (p.
196), while other researchers have presented evidence that strongly challenges the
traditional consensus that tax incentives are a powerful stimulus to giving (Bristol 1985;
Steinberg 1990). Auten, Sieg and Clotfelter (2002) sum both the lack of and the need for
clarity in the literature: “Since there continues to be serious debate about tax proposals
that could permanently change the price of giving, the effect of persistent price changes
on charitable giving is therefore of considerable practical importance” (p. 380).
Although there have been numerous literature reviews in the area, they have
lacked the mathematical rigor of a formal meta-analysis. Brooks (2002) refers to the work
of Steinberg (1990) as a “meta-analysis” although it was essentially a literature review
updating new findings published subsequent to Clotfelter (1985), and contained no
mathematical analysis or conclusions based on those studies. Similarly, Clotfelter’s
(1985) seminal work on the effects of tax on charitable giving examined the effects of
7
variables such as income, but it did not provide a proper meta-analytic review of the
relevant literature. However, such literature reviews provide the basis for development of
hypotheses for the current paper.
HYPOTHESES
The current paper examines the effects of changes in tax deductibility on
charitable giving, which is of inherent importance to policymakers. Aside from
estimating this basic relationship, we explore eight moderators that can affect it. These
moderators fall into one of two broad categories – those that deal with measurement and
model specification issues and those that deal with donor or donation characteristics.
Measurement/Model Characteristics
Early research examining the price elasticity of charitable giving typically used
measures of tax costs based on the year in which the donation was made. Subsequent
researchers began to study a more long term measure of tax costs where the measure is
taken over a period of years. Such permanent measures of tax costs smooth year-over-
year changes in tax rates and are essentially averages of this variable across the years
surrounding the year of analysis. The hypothesis behind this measure is that donors will
change donation behavior to changes in tax rates they perceive as short term in nature,
and be less likely to respond immediately to changes they view as long term (Bakija
2002).
H1: Price elasticities reported in studies using permanent measures of tax rates
will be lower than those based on temporary measures.
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Similar to the previous variable, studies have used one of two data sources – panel
(i.e., multi-year) or cross-section (i.e., one year) data. A panel data set follows the same
group of donors over time. Steinberg (1990) noted that studies leveraging panel data sets
have obtained results that depress the elasticities of charitable giving, and Barrett (1991)
gave three benefits of panel data: 1) they are more likely to generate a more accurate
measurement of the price elasticity of giving, 2) they suffer to a smaller degree from
statistical biases caused by the omission of relevant variables, and 3) they enhance the
ability to distinguish between separate price and income effects. Clotfelter (1990) also
argues that there is a lag between changes to price and giving behavior.
Like the issue of permanent measures of tax costs, studies that consider only one
year will likely reflect only short-term donor response. Since we tend to respond to short-
term events far more strongly than to long-term developments (i.e., Bakija 2002; Steel
and König, In Press), cross-sectional (i.e., short term) data should provide higher
elasticities while panel data should generally provide lower elasticities.
H2: Price elasticities reported in studies using panel data will be lower than those
based on cross-sectional data.
Studies have developed data sets from one of two sources – taxfiler data or survey
data. Those that use taxfiler data benefit from behavioral data, inasmuch as the data
contain a supposedly more accurate measure of variables such as donation amount,
income, age, etc. since they are taken directly from income tax returns. Clotfelter (1985)
speculates that elasticities derived by survey data may be artificially high. In addition,
survey data, particularly when measuring the phenomenon of charitable donation, are
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expected to suffer from socially desirable responses (Fisher 2000; Fisher and Ackerman
1998), and therefore the price elasticities are expected to be inflated.
H3: Price elasticities reported in studies using taxfiler data will be lower than
those based on survey data.
Similar to the above hypothesis, the degree to which an individual can be
expected to cheat on income tax returns is expected to influence their measured price
elasticity. Slemrod (1989) shows that over-reporting of gifts for tax purposes probably
depresses measured price elasticities. Clotfelter (1985) also found slightly lower price
elasticities for audited tax data.
H4: Price elasticities reported in studies using audited deduction measures will be
lower than those based on self-reported tax data.
Another final issue related to the models used to estimate elasticities is the net
worth of a donor, which can be expected to impact the ability to make donations and to
take advantage of changes in tax deductibility. For donors with a higher net worth, more
potentially disposable cash or assets means they are more likely to be in a position to take
advantage of changes in tax deductibility. Indeed, McNees (1976) found that net worth is
a significant variable affecting the price elasticity of donations.
H5: Price elasticities derived from models that include net worth will produce
higher elasticities than those that do not.
Donor/Donation Characteristics
Individuals donate to charity through a range of mechanisms, including cash
donations, gifts of assets, volunteer time, and bequests through ones estate. Each of these
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mechanisms has been studied to some extent in the literature. However, bequests are
more likely than other donations to be carefully planned, often with the help of financial
and tax planning professionals (Cermak et al. 1994) while “day-to-day” donations are
often the result of limited information searches and personal involvement (Hibbert and
Horne 1997). Given the degree to which bequests are planned and calculated based on
issues such as taxation, they are expected to be more responsive to changes in the price of
donation.
H6: Price elasticities for charitable donations in the form of bequests will be
higher than donations in other forms.
Researchers have argued that income level is a key determinant of the price
elasticity of charitable donations. For example, Auten Cilke and Randolph (1992) found
that the giving patterns of higher income individuals are more sensitive to changes in the
tax price of donations. In addition, lower income donors have been found to be price
insensitive (Anderson and Beier 1999). However, other researchers have found that lower
income consumers are more prone to be highly responsive to the tax price of giving
(Clotfelter and Steuerle 1981; Lankford and Wyckoff 1991). Davie (1985) found that,
despite increases in tax costs of giving, high income donors do not curtail their giving
behavior. Clotfelter (1985) summarizes: “simply put, the price elasticities for different
income groups have not been determined very precisely” (p. 71).
The motivations for high income donors are different from those of average
consumers. Although tax advantages have been identified as one motivator for donations
by individuals with high incomes, Cermak et al. (1994) found that this motivation was
dominant in only 27% of their sample. More prevalent motivators included reciprocity,
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family tradition and social ties. Similarly, Kottasz (2004) found that tax incentives were a
low priority for affluent donors. Long term relationships with nonprofits have also been
identified as key variables in donor behavior among higher income individuals (Lindahl
1995). To the extent that these other motivators are expected to be behind the decision of
higher income individuals to make donations, tax incentives can be expected to have
reduced effects. Income level among donors, therefore, is not expected to be a significant
predictor of price elasticity.
H7: Price elasticity among higher income donors will not be significantly
different from those among lower income donors.
Similar to the above, it has been argued that the use of taxfiler data by many
studies skews the sample toward higher income households by excluding those
households who do not itemize charitable donations on their tax returns (Robinson 1990).
Nonitemizers were allowed to deduct charitable donations from 1982-1986 under The
Economic Recovery Tax Act of 1981 (ERTA), with the intention of stimulating “giving
by all individual taxpayers, including those who do no benefit from itemizing” (U.S.
House 1985, p. 112). However, the provision was not extended beyond 1986, with
questions surrounding its efficiency at stimulating new donations:
Extension of the contribution deduction to nonitemizers creates
unnecessary complexity, while probably stimulating little additional
giving and presenting the IRS with a difficult enforcement problem….It is
doubtful that the first dollars of giving, or the giving of those who give
only modest amounts, are affected much by tax considerations. (U.S.
Department of Treasury, 1984, p. 82).
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Indeed, Reece and Zieschang (1985) found markedly different behavior between
itemizers and nonitemizers. Itemizer status is hypothesized to be connected to home
ownership status, with the majority of those who itemize doing so to claim the deduction
of mortgage interest paid (Duquette 1999). Duquette also hypothesized that nonitemizer
status is correlated with lower education levels, given the lack of understanding of the tax
code required. He states that nonitemizers may simply be “intrinsically less responsive to
tax incentives” than itemizers (Duquette 1999, p. 204).
H8: Price elasticity among itemizers will be higher than those found among
nonitemizers.
DATA AND METHOD
The study of charitable giving behavior has been explored in fields as diverse as
economics, sociology, psychology, marketing and political science. Therefore, a broad
and exhaustive search was required to gather appropriate data. Two significant literature
reviews served as the basis for the search. First, Clotfelter (1985) summarized to date the
research examining the tax effects on charitable giving. Steinberg (1990) updated the
work of Clotfelter, and examined specific advances in the study of tax effects on
charitable giving. These two sources were virtually all encompassing of the work prior to
1990. To augment these two reviews, the following steps were taken.
First, the following databases were searched for all available years to present:
ABI/INFORM, Business Source Premier, PsychINFO, Proquest Digital Dissertations,
MEDLINE, ERIC, and EconLit. Searches were based on the keywords: charitable
giving, charity, contribution, bequest, donation, elasticity, and tax effects. Second, the
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Social Sciences Index (i.e., Web of Science) was searched for all publications that cited
the seminal works of Clotfelter (1985) and Steinberg (1990) described above. Third, an
attempt was made to contact those authors who were found to have published two or
more articles on the effects of taxation on charitable giving. Although this effort was
intended to address the “file drawer” problem in meta-analysis, no responses were
received. Finally, the references contained in the articles were then scoured to uncover
additional publications, including conference proceedings. Where conference
proceedings or working papers were uncovered that were later published, the published
article was used.
Initially, 535 sources were identified for review. After excluding those that did
not specifically examine the effects of tax on charitable giving or failed to provide data,
this review considers 69 works, providing a total of 138 useable observations of the
dependent variable and an overall sample size of 1,418,212. The majority of the sources
are journal publications, although 4 books, 5 conference papers and 2 unpublished
manuscripts are included. Table 1 outlines the source material for the review.
Insert Table 1 here
Moderator Search
Studies were coded to record potential moderators on the dependent variable. No
corrections were done – to account for potential range restriction or reliability issues, for
example – in order to allow for variables to be uncovered as potential moderator
variables. Some studies reported extreme values of price elasticity. The current paper
14
considers results both with and without those outliers. Specifically, descriptive data are
provided that represent the full data set and the data with outliers removed. The Figure 1
shows a plot for the data, with the five outliers being to the left of the vertical dashed line.
Attempts were made to use multiple individual estimates of elasticity from any
given study. For example, if a study reported elasticities from segments within the
sample, these individual estimates were used instead of the overall measure in order to
better account for potential moderators. However, some studies reported multiple
elasticities but failed to report other critical information such as segment sample sizes. In
the case of individual estimates based on potential moderators, estimates were coded but
used only in the analysis of that moderator. One of the significant problems with the data
concerned the income level of the donors being studied. There was little consistency
among studies with respect to the income levels of subjects. Although most studies used a
general population (with many excluding those donors with less than $4,000 in annual
income), very few of those studies that reported useable estimates for income brackets
did so using consistent income brackets. Therefore, coding was only completed for two
segments – those with over $100,000 in annual income and those below this level.
Because of the relatively broad categories a relatively crude estimate that did not factor in
inflation rates was used. All of the samples reporting income were in U.S. dollars and
authors were contacted to obtain needed information where possible. To ensure all the
data was accurately transcribed from the source articles, two independent coders were
employed. Following the coding procedure outlined by Orwin (1994), when
15
Insert Figure 1 here
inconsistencies between coders were detected, the source article was rechecked and the
data was corrected. Initial coding produced consensus on 93% of the observations (128
out of 138), with consensus reached on the remaining 10 observations as described
above.
To estimate moderator effects, the independent variables are regressed against the
dependent variable, price elasticity. Although a variety of techniques for detecting
moderators through meta-analysis are possible, weighted least squares (WLS) is expected
to provide the most accurate results (Steel and Kammeyer-Mueller 2002). Consequently,
WLS was employed in the review, with categorical variables dummy coded. Consistent
with the Hunter and Schmidt (1990) meta-analytic method, weights were based on
sample size. For studies that did not include a sample size, we conservatively substituted
a sample size of 3,966 as a proxy. It reflects a sample size of the average of the bottom 3
quartiles of the data and allows the study to be included in the analysis without it likely
being over weighted. Outliers greater than three standard deviations from the mean were
excluded from the analysis, though we do evaluate the impact of these exclusions on the
basic analysis. Sample sizes of each observation were used as weights. Analysis was
limited to cases where there were at least 5 cases (k) per moderator variable (Tabachnick
and Fidell 1989).
RESULTS
The weighted mean of the price elasticity of giving is –1.44, with a standard
deviation of 1.21. Thus, on average, a 1% reduction in the cost of charitable giving (i.e.,
an increase in the charitable deduction) is expected to provide an increase in donations of
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1.44%. The weighted mean of -1.44 is slightly above the previously accepted range of
-1.1 to -1.3 (Clotfelter 1985). However, when outliers more than three standard
deviations from the mean are removed from the analysis, the weighted average of price
elasticities falls to -1.11, at the lower end of the generally accepted estimates.
In addition, we consider the stability of this finding with respect to the “file
drawer hypothesis.” It suggests that there may be unpublished studies that that have lower
values, thus decreasing the estimate of our effect size. Though our literature review
specifically attempted to locate any unpublished work, we assess its possible impact here.
Initially, one can examine a graphical distribution of effects, as per Figure 1, to see if the
distribution is non-symmetrical. After excluding outliers, there still possibly remains a
skew of the data toward the more negative estimates of elasticity. Specifically, the data
appears to be truncated at an elasticity of (zero). This is quite possible since elasticity
estimates above zero are not theoretically supported and consequently there may be
publication bias excluding such findings. However, we can mathematically assess the
“file drawer” problem in various ways. First, Begg (1994) suggests the use of a rank
correlation test, specifically Kendall’s tau. Correlating sample sizes with correlations
generate a coefficients of .11, which is not significant (p = .067), suggesting potential
publication bias is not substantive. Second, we can consider how many of these
undiscovered studies must exist to affect our current analysis. Results indicate that at
least 70 estimates of negative but inelastic giving (i.e., less than 1 in absolute value) with
an average sample size of 11,000 are required to accept the null hypothesis that giving is
not negatively elastic. Similarly, at least 242 estimates of no elasticity (i.e., an elasticity
value of zero) with an average sample size of 11,000 are needed to accept the hypothesis
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that charitable donations are elastic below a level of -.5 (i.e., that consumers don’t donate
to charity at least half of cost savings due to increased tax deductibility). Consequently,
our basic finding appears to be robust.
However, the wide range of elasticities reported does suggest this basic estimate
is affected by moderator variables. To examine moderator effects, regressions on the data
set without outliers were conducted. Collinearity diagnostics indicate limited
relationships among the independent variables, with all inflation factors below 1.19.
Subsequent analysis was undertaken to examine the Mahalanobis’ distance for each
observation and identify potential outliers in addition to those 5 outliers already deleted
from the analysis. This examination revealed six additional outliers with a chi-square
value above the critical value of 22.458 (p=.001, df=6) which were removed from the
data and the analyses re-ran. The results of a multivariate analysis for 6 of the 8
hypotheses (H1 through H6) are presented in Table 2.
Insert Table 2 here
The variables for two hypotheses (donor income, H7, and itemizer status, H8)
were unable to be run in the primary multivariate model due to the limited umber of cases
and subsequent deletion of cases and variables from the multivariate model. Subsequent
multivariate analyses analyzed these remaining hypotheses using the limited number of
cases available. Specifically, Table 2a presents the results for the analysis including the
donor income variable (H7), with a reduced sample size of only 36 observations. Listwise
deletion resulted in only 3 variables being included in the second model. The results for
the remaining variables are presented in Table 2a.
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Insert Table 2a here
Finally, the variable outlined in Hypothesis 8, itemizer status, was examined using
the same approach as that for donor income. It was included in a multivariate model with
a reduced sample size of 84. In addition to the deletion of the variable examining audited
donations (fewer than the required 5 cases available), the donation type variable
(bequest/non-bequest) was deleted because it became a constant after listwise deletion of
cases. Table 2b presents the results of this third model.
Insert Table 2b here
As suggested by the unstandardized regression coefficients in column 4 of Table
2, the significant moderators include the use of survey versus taxfiler data (p=.001),
whether or not the donation is a bequest or not (p=.043). An examination of the
standardized coefficients reveals that the use of taxfiler versus survey data appears to be
the single largest moderator of price elasticity. Each of the specific hypotheses is now
discussed in turn.
Permanent Measures of Tax Rates. Based on the multivariate regression, permanent
measures of tax rates do not appear to have a significant effect on the elasticity of
charitable contributions. Although the mean estimate from those samples that use
permanent measures is negatively inelastic at –.829, while the mean estimate from short
term measures is –1.14, the results are not significant (p=.432). Therefore, donors do not
19
appear to be more likely to take advantage of lower prices if they perceive those changes
to be more short term in nature, and Hypothesis 1 (H1) is not supported.
Data Source: Panel versus Cross-Section. In order to ensure that studies using cross-
sectional data are estimating elasticities at points in time following changes in tax costs,
we examined the average lag time between reported tax rate changes and observed
behavior. The average lag time was less than 1 year, indicating that the majority of cross-
sectional studies do capture short term effects while the average span of panel data was
6.8 years. Although the use of panel data does appear to have a marked effect on
lowering the estimated price elasticity of giving, with those estimates using panel data
reporting a mean of –1.00 compared to a mean of –1.50 for those studies using cross-
sectional data, these results are not statistically significant (p=.571). Therefore,
Hypothesis 2 (H2) is not supported.
Data Source: Taxfiler versus Survey. The use of taxfiler data as opposed to survey data
also appears to be strongly related to the price elasticity of giving. Means for estimates
using taxfiler data are –1.08 compared to –1.29 for those studies that use survey data. The
multivariate model supports this finding (p=.001) and therefore, Hypothesis 3 (H3) is
supported.
Audited Tax Data. The use of audited tax data as opposed to unaudited data appears to
have no effect on the dependent variable. Studies that use audited financial data report a
mean elasticity of –1.24, while those that report unaudited data find a mean elasticity of –
20
1.06. The lack of significance in the regression may be, in part, due to the relatively small
number of estimates that have used audited tax data (k=5, compared to 122 for unaudited
data). However, Hypothesis 4 (H4) is not supported.
Net Worth. Similar to permanent income, the inclusion of net worth as a variable is
insignificant (p = .077). Means between the two groups are also similar (–1.27 versus –
1.41). Therefore, Hypothesis 5 (H5) is not supported.
Donation Type: Bequests versus Donations. Hypothesis 6 considered the price elasticity
of giving in the form of a bequest compared to other forms of donation. The difference
between means is considerable (–1.50 for bequest estimates compared to –1.18 for other
forms of donation), and the multivariate regression finds the form of donation to be a
significant variable affecting price elasticity at the .05 level (p=.043). Therefore,
Hypothesis 6 (H6) is supported.
Donor Income Level. From Table 2a, the price elasticity of higher income donors
($100,000+ annual income) appears to be slightly higher than those of lower income
donors (-.91 for lower income donors, compared to –1.09 for higher income donors).
However, this result is not statistically significant (p=.825). Therefore, Hypothesis 7 (H7)
is supported.
Itemizer Status. The elasticities of itemizers versus non-itemizers are presented in the last
row of Table 2b. Although the analysis finds a large difference in means between the two
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groups (–1.05 for itemizers compared to –2.99 for nonitemizers), this difference is in the
direction opposite that described in Hypothesis 8. Further, the model fails to provide
support for a significant relationship (p = .650). This may again be due to the relatively
small number of useable observations recording price elasticities for nonitemizers (k=6).
Consequently, Hypothesis 8 (H8) is not supported.
It should be noted that the results examining the effects of both donor income and
itemizer status suffer from a relatively low sample size, and therefore may be more
important to the price elasticity of charitable donation than suggested by the data
presented here.
One final data characteristic that was examined was the effect of tax changes on
charitable giving over time. Although no specific hypothesis was developed, the elasticity
estimates were regressed on both year of publication and year of data collection to detect
possible linear, quadratic, or cubic trends in elasticities over significant periods of time
such as the tax cuts experienced in the U.S. during the Reagan presidency. No significant
results were found.
DISCUSSION
The results reported here generally support the hypothesis that tax deductions for
charitable giving are treasury efficient. That is, a decrease in one dollar in the cost of
giving can be expected, on average, to result in more than one dollar being donated to
charity through personal philanthropy. However, the treasury efficiency of charitable
donations is only one issue for consideration, albeit an important one. Previous
researchers have examined the benefits of replacing government support for charities
with individual support. Cordes (2001), for example, points to the correlation between
22
cash donations and volunteerism and argues that a system encouraging more individual
donations would also “help foster civic virtues that are needed to maintain a ‘civil
society’” (p. 3). Others have argued that charity is supported most efficiently through
private donations, and encouraging individual donations allows for consumer choice
(Brooks 2004). The current analysis highlighted several issues in the study of the effects
of tax on individual charitable giving.
To begin with, a number of issues thought previously to be significant in the
estimation of the price elasticity of giving were found to be relatively insignificant. For
example, although many researchers attempt to compute measures of permanent tax costs
(i.e., deductibility) to estimate price elasticity of charitable donations, the differences in
estimates between those models that use permanent and short term measures is
insignificant. For policymakers, this is perhaps the most important implication from the
analysis. Although the mean value of price elasticities measured using longer term
measures of tax costs is lower, tax deductions overall appear to be treasury efficient.
Further, this effect presents a potential marketing opportunity for charitable
organizations. For example, perhaps the changes are not communicated efficiently
through government organizations, providing an opportunity for charities to increase
awareness about changes in the tax deductibility of donations and create a sense of
urgency. Although many charities may be reluctant to market such egoistic motives to
consumers, the present study suggests that such motivations are important to consumers
and could therefore represent a potential increase in donations for charities.
Similarly, the use of panel data does not appear to have any marked effect on
elasticity estimates despite their increased usage over the past two decades. This has
23
important implications as cross-sectional data can be expected to be easier for researchers
to access and analyze.
An examination of the elasticities based on taxfiler and survey data indicates that
consumers appear to overestimate the effects of changes in tax deductibility on their
willingness to increase donations, as shown by the analysis for Hypothesis 3. This is
consistent with the social desirability hypothesis, but it might also be influenced by
consumers being simply more aware of tax advantages of charitable giving in survey
situations. Indeed, elasticities in giving situations where tax costs are expected to be
salient, such as a bequest, are shown here to be significant. This finding also represents a
potential advantage to researchers studying the effects of tax deductibility of charitable
donations. Survey data is often considered less accurate, although typically easier to
access. Researchers using survey data can assess expected actual effects in tax returns by
correcting for the difference found in this analysis. Survey data, on average, reports
elasticities that are approximately 50 percent higher than those found in taxfiler samples.
Finally, higher income consumers do not appear to be significantly more or less
price elastic than lower income consumers. This has implications for the marketers of
charities that perhaps incorrectly assume that higher income donors will be more
concerned with tax advantages. Lankford and Wyckoff (1991) found that price elasticities
decline in higher income groups, and Boskin and Feldstein (1977) found significantly
negative price elasticities of lower income groups above 2 in absolute value. Therefore,
potential tax advantages of charitable donations should not be promoted solely to higher
income donors.
24
There are a number of limitations to the present study. First, the effects of changes
in tax deductibility are estimated at given tax rates. This effect is relative, not absolute,
and consequently can be expected to vary depending on the actual cost of giving. For
example, it is possible that a decrease of 10 percent in the cost of giving from .5 to .45
can be expected to have significant effect. On the other hand, a 10 percent decrease in the
cost of giving from .2 to .18 may not have a similar effect. Although the current study
found no significant differences in elasticities estimated across periods of fluctuating tax
rates (for example, the lower taxes associated with the Reagan era of the 1980’s), more
examination of this possibility is required. This issue would benefit from more specific
study, perhaps under experimental conditions that would allow researchers to easier
manipulate the levels of taxation.
Second, the current study did not examine the effect of tax deductibility on
donations to specific types of charities, although Feldstein (1975b) showed that price
elasticities varied among the type of charitable organization. Similarly, other researchers
have found that specific charities maintain support better than others through periods of
financial challenge (Smith 1980). Therefore, although this analysis presents an overall
estimate of the phenomenon, specific charities may not expect the benefits presented
here.
Third, more complete reporting would lead to increased ability to meta-analyze
the field. For example, some studies did not report sample sizes and therefore those
estimates required substitute values of sample size in order to be included in this study.
Also, the use of correlational data would provide a more robust analysis of the effects of
25
tax deductibility on charitable contributions. Most studies provide no correlational data
(one notable exception is Robinson 1990).
FUTURE RESEARCH
Related to this meta-analysis’ limitations, there are two areas in which gaps exist
in the extant literature representing future research opportunities in the area of tax
elasticities of charitable contributions. The present research base is US-centric, has
several measurement issues; each of these issues is discussed in turn.
The current study included almost exclusively samples from the U.S. population.
More research is needed with non-U.S. sample populations to better understand the
effects of tax deductibility in emerging economies. The U.S. has a rich history of charity,
with the majority of Americans donating to charitable organizations (Sullivan 2002), and
Clotfelter (1985) states that the “U.S. is distinctive to the degree to which it subsidizes
nonprofits” (p. 1). As shown in Table 1 several studies have recently begun to explore the
phenomenon outside the U.S., including Canada, the U.K., Russia, and Singapore.
Emerging economies, such as Russia and Singapore, are perhaps more interesting to
study given the comparative similarities between Canada, the U.K. and the U.S. Indeed,
the few estimates that are available from these emerging cultures show significantly
lower elasticities than those found in other Western cultures. More study is needed to
confirm these estimates, and to uncover the moderator variables that lead to such extreme
values.
A second gap identified by the meta-analysis concerns the type of donations
studied and their measurement. For example, the effects of tax evasion have been only
marginally addressed (Clotfelter 1983; Slemrod 1989). Although the current study found
26
no statistical difference in audited tax data, the relatively small quantity of estimates
using this type of data point to an important gap in the literature. Further, use of audited
data would also shed light on the issue of nonitemizers price elasticities mentioned
earlier. Such research would be invaluable in the discussion concerning flat tax proposals,
and potentially alleviate concerns of charitable organizations that such a tax structure
would result in dramatically lower levels of public support. Similarly, the findings for the
price elasticities of nonitemizers presented in the current paper lack theoretical support. A
potential explanation is found in the U.S. Treasury Department report (1984) on the
Economic Recovery Tax Act (ETRA) from 1982-1986, where the authors of the report
note the “enforcement problem” created by the nonitemizers deduction. Indeed, none of
the taxfiler data used in nonitemizers estimates is audited data, creating a possible
inflation in elasticity due to fraudulent claims.
As noted earlier, the analysis presented here for the potential moderators donor
income and itemizer status are perhaps less conclusive than the analysis for other
moderator variables given a relatively low number of observations. Future research
should extend the work of previous researchers in these areas, particularly donor income.
A final gap related to the type of donation relates to paucity of research examining
donations in non-traditional form. Although the vast majority of researchers have
examined traditional charitable donations in their analyses, the demographics of the
North American population suggest that charities can expect an increase in the
prominence of planned giving among their donors, illustrating the need to further study
the effects of tax deductibility on bequests for example. In addition, other emerging
forms of charitable donation such as fundraising events blend the ideas of purchase and
27
donation. For example, tickets to charity events are often only partially tax deductible
since the donor receives some value in return for the ticket price. Further research is
needed into these emerging forms of donation to understand how tax costs affect
consumer behavior.
CONCLUSION
The effect of changes in tax deductibility has been one of the most widely studied
areas in personal philanthropy. However, although the field has progressed significantly
from Taussig’s original estimates in 1967, there remains little consensus on the proper
means of estimating elasticities, and there remains serious debate about the effectiveness
of changes in tax deductibility to provide a stimulus for increased charitable giving. The
present analysis included almost 40 years of research to conclude that changes in tax
deductibility do indeed appear to have a marked effect on charitable giving. In fact, the
results reported here suggest that tax deductions are treasury efficient, with a decrease of
the cost of giving by one dollar resulting in more than one dollar being donated to charity
through private philanthropy. This presents an opportunity for policymakers to support
the transition of the provision of public services from governments to charities and non-
profit organizations. In addition, charities must ensure that the egoistic benefit of tax
deductibility is present in their charitable appeals, and ensure that their donor bases are
aware of decreases in the tax cost of giving.
28
TABLE 1
Summary of the Studies in the Meta-Analysis
Study Elasticity
(Range)
Permanent/
Temporary
Tax Measure
Panel/ Cross
Sectional
Data
Taxfiler/
Survey
Data
Donation
Type
Abrams and Schitz (1978) -1.1 Temporary Cross Section Taxfiler Donation
Abrams and Schitz (1984) -1.44 Temporary Cross Section Taxfiler Donation
Apinunmahakul and Devlin
(2004)*
-1 Temporary Cross Section Survey Donation
Auten and Rudney (1984) -.78 Temporary Panel Taxfiler Donation
Auten and Rudney (1990) -.14 to -1.4 Permanent Panel Taxfiler Donation
Auten, Cilke and Randolph
(1992)
-1.11 Temporary Panel Survey Donation
Auten and Joulfaian (1996) -1.1 to -2.50 Temporary Cross Section Taxfiler Donation
/Bequest
Auten, Sieg and Clotfelter
(2002)
-.4 to -1.26 Temporary Panel Taxfiler Donation
Auten, Clotfelter and
Schmalbeck (2002)
-.52 to -.95 Permanent Panel Survey Donation
Bakija (2002) -.2 to -.2.52 Permanent/
Temporary
Panel Taxfiler Donation
Bakija, Gale and Slemrod
(2003)
-.162 Temporary Cross Section Taxfiler Bequest
Barrett (1991) -1.09 Temporary Panel Taxfiler Donation
Barrett, McGuirk and
Steinberg (1997)
-.47 Temporary Panel Taxfiler Donation
Barthold and Plotnick (1984) -.75 Temporary Panel Taxfiler Bequest
Boskin (1976) -1.2 Temporary Cross Section Taxfiler Bequest
Boskin and Feldstein (1977) -2.14 to -2.44 Temporary Cross Section Survey Donation
Bradley, Holden and
McClelland (2000)
-.78 to -2.56 Temporary Panel Survey Donation
Broman (1989) -.39 Temporary Panel Taxfiler Donation
Brooks (2002)* -6.68 Temporary Cross Section Survey Donation
Brown (1987) -2.57 to -3.62 Temporary Cross Section Survey Donation
Brown and Lankford (1992) -1.62 to -1.79 Temporary Cross Section Survey Donation
Choe and Jeong (1993) -2.45 Temporary Panel Survey Donation
Christian and Boatsman
(1990)
-2.0 Temporary Cross Section Taxfiler Donation
Christian, Boatsman and
Reneau (1990)
-.99 to -1.56 Temporary Panel Taxfiler Donation
Chua and Wong (1999)* -.98 to -6.15 Temporary Cross Section Taxfiler Donation
Clotfelter (1980) -.24 to -1.55 Temporary/
Permanent
Panel Taxfiler Donation
Clotfelter and Steuerle (1981) -1.27 Temporary Panel Taxfiler Donation
Clotfelter (1983) +.06 to -1.60 Temporary Cross Section Taxfiler Donation
Clotfelter (1985) -.35 to -2.66 Temporary Panel Taxfiler Donation
/Bequest
Dunbar and Phillips (1997) -3.36 Temporary Panel Taxfiler Donation
Duquette (1999) -.64 to -1.24 Temporary Panel Survey Donation
29
Dye (1978) -2.25 Temporary Cross Section Survey Donation
Dye (1980) -.6 Temporary Cross Section Survey Donation
Feenberg (1987) -1.63 Temporary Panel Survey Donation
Feigenbaum (1980) -.44 Temporary Panel Taxfiler Donation
Feldstein (1975a) -.29 to -1.8 Temporary Panel Taxfiler Donation
Feldstein (1975b) -1.24 Temporary Panel Survey Donation
Feldstein and Clotfelter
(1976)
-1.15 Temporary Cross Section Survey Donation
Feldstein and Taylor (1976) -1.09 to -1.28 Temporary Panel Survey Donation
Fisher (1977) -2.3 Temporary Cross Section Taxfiler Donation
Glenday, Gupta and Pawlak
(1986)*
-.15 Temporary Panel Survey Donation
Greene and McClelland
(2001)
-.54 Permanent Cross Section Survey Bequest
Greenwood (1993) -.43 Temporary Cross Section Taxfiler Donation
Hood, Martin and Osberg
(1977)*
-.86 Temporary Panel Taxfiler Donation
Jones (1983)* -.60 Temporary Cross Section Survey Donation
Jones and Posnett (1991)* -.07 Temporary Panel Survey Donation
Joulfaian (1991) -3.00 Temporary Cross Section Taxfiler Bequest
Joulfaian (2000) -.74 to -2.58 Temporary Cross Section Survey Bequest
Joulfaian and Rider (2004) -1.14 to -2.15 Temporary Cross Section Taxfiler Donation
Kingma (1989) -.43 Temporary Cross Section Survey Donation
Lawrence and Saghafi (1984) -1.18 Temporary Panel Survey Donation
Lindsey (1987) -1.23 to -
2.56
Temporary Cross Section Taxfiler Donation
McClelland (2004) -1.85 to -2.14 Temporary Cross Section Taxfiler Bequest
Newsome, Blomquist and
Romain (2001)
-.27 to -.58 Temporary Panel Taxfiler Donation
O’Neil, Steinberg and
Thompson (1996)
-.47 to -2.24 Temporary Panel Survey Donation
Randolph (1995) -.51 to -1.55 Temporary/
Permanent
Panel Taxfiler Donation
Reece (1979) -1.19 Temporary Cross Section Survey Donation
Reece and Zieschang (1985) -.85 Temporary Cross Section Survey Donation
Reece and Zieschang (1989) -2.72 Temporary Panel Survey Donation
Ricketts and Westfall (1993) -1.06 Permanent Panel Taxfiler Donation
Robinson (1990) -1.43 to -7.07 Temporary Panel Survey Donation
Rudney (1985) -.61 Temporary Panel Taxfiler Donation
Schiff (1985) -2.79 to -4.97 Temporary Cross Section Survey Donation
Schwartz (1970) -.376 to -1.23 Temporary Cross Section Taxfiler Donation
Slemrod (1989) -2.04 to -2.34 Temporary Cross Section Taxfiler Donation
Steinberg (1985) -.08 Temporary Cross Section Survey Donation
Taussig (1967) 0 to -.1 Temporary Cross Section Taxfiler Donation
Tiehen (2001) .02 to -2.41 Temporary Cross Section Survey/
Taxfiler
Donation
Wu and Ricketts (1999) +.12 to -.2 Temporary/
Permanent
Panel Taxfiler Donation
* Non-U.S. population samples
30
TABLE 2
Moderator Effects – Results of Multiple Regression
Multivariate model R2 = .140; F=3.256; p=.005. n=127
* Unstandardized coefficients
NOTE: Weighted differences may be substantially larger or smaller than raw differences.
31
32
Hypothesis/ Variable N
(1)
K
(2)
Mean
(3)
B*
(4)
t-value
(5)
Sig.
(6)
Hypotheses
Permanent Tax
Yes
No
58,385
1,119,266
10
117
-.829
-1.1368
.252 .824 .432 H1: Not Supported
Data Source
Panel
Cross-Section
1,004,590
173,061
75
52
-1.0044
-1.4985
.112 .568 .571 H2: Not Supported
Data Source
Taxfiler
Survey
472,226
705,425
63
64
-1.0807
-1.2914
.514 3.653 .001 H3: Supported
Donations
Audited
Unaudited
62,991
1,114,660
5
122
-1.24
-1.064
-.509 -.519 .605 H4: Not Supported
Net Worth
Yes
No
272,835
904,816
28
99
-1.2699
-1.4142
-.287 1.785 .077 H5: Not Supported
Type of Donation
Bequest
Other
34,171
1,143,480
8
119
-1.5038
-1.1762
.835 2.05 .043 H6: Supported
TABLE 2a
Moderator Effects – Results of Multiple Regression With Donor Income
Multivariate model R2 = .107; F=1.282; p=.297. n=36
* Unstandardized coefficients
NOTE: Weighted differences may be substantially larger or smaller than raw differences.
33
Hypothesis/ Variable N
(1)
K
(2)
Mean
(3)
B*
(4)
t-value
(5)
Sig.
(6)
Hypotheses
Data Source
Taxfiler
Survey
86,274
270,072
14
22
-.9729
-1.0291
.657 1.855 .073 N/A
Net Worth
Yes
No
201,215
155,131
15
21
-.802
-1.1538
.193 .566 .575 N/A
Donor Income
$100K and Above
Under $100K
129,389
226,957
16
20
-.9081
-1.0865
.064 .222 .825 H7: Supported
TABLE 2
Moderator Effects – Results of Multiple Regression
Multivariate model R2 = .144; F=2.614; p=.031. n=84
* Unstandardized coefficients
NOTE: Weighted differences may be substantially larger or smaller than raw differences.
34
Hypothesis/ Variable N
(1)
K
(2)
Mean
(3)
B*
(4)
t-value
(5)
Sig.
(6)
Hypotheses
Permanent Tax
Yes
No
106,773
839,021
10
74
-.826
-1.2412
.447 1.362 .177 N/A
Data Source
Panel
Cross-Section
839,925
105,869
58
26
-1.0757
-1.4996
-.156 -.464 .644 N/A
Data Source
Taxfiler
Survey
353,132
592,662
39
45
-.9875
-1.345
.420 1.809 .074 N/A
Net Worth
Yes
No
230,706
715,088
16
68
-.9213
-1.3028
.551 1.729 .088 N/A
Itemizer Status
Itemizer
Non-Itemizer
897,821
47,973
78
6
-1.0535
-2.99
.192 .456 .650 H8: Not Supported
FIGURE 1
PLOT OF EFFECT SIZES
35
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
-7 -6 -5 -4 -3 -2 -1 0 1
Elasticities
Outli
ers
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