Crowding-Out Giving? The Effect of Government Transfers on

Private Donations in Canada

Mai Bui

Dr. Lemieux- ECON 594 - UBC

Aug. 1st 2008

The theory of crowding-out predicts an increase in government expenditure will decrease private propensity to donate. The National/Canadian Survey of Giving, Participating, and Volunteering for 1997, 2000, and 2004 is used to test whether Canadian private donations to health, education, environment, and social welfare are affected by changes in government transfers to the equivalent targets. Empirical evidence from this study suggests there is no support for the theory of crowding-out. Government expenditure appears to have no significant effect on private donations across all 4 categories. Other specifications suggest that the crowding out (or in) effect may depend on individual awareness of government actions.

We believe that one of the greatest costs of our present welfare system is that it ...poisons the springs of private charitable activity.

Milton and Rose Friedman1

1 Friedman, M., and Rose D. Friedman, Free to Choose: A Personal Statement (New York: Harcourt Brace Jovanovich, 1980) 123.

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INTRODUCTION

The behavioural effect of governmental transfers on private propensity to donate has

been a source of interest, for the last couple of decades, to public economists looking to

extricate the actual outcome from the intended outcome of governmental policy. Spending

policies rely on an accurate picture of how donors react to public application of their tax

money, and optimal government spending differs depending on whether private donations are

complements, substitutes, or neural with respect to government expenditure. Furthermore,

understanding the effect of public expenditure on private philanthropy would shed some light

on how donating enters utility functions and the determinants of willingness to donate.

Theories predict a dollar of government transfers will crowd out a dollar of private

donations, thereby neutralizing the purported effect of government spending policy and

suggesting a suboptimal use of tax dollars. And yet, subsequent models have made room for an

incomplete crowd out, and others, still, have responded with models allowing for a crowd-in of

donations. Much empirical research has been conducted on the subject, as well, with the

results varying widely. While there have been some studies done in the UK, most are on the US

and none currently exist on the effect of Canadian government expenditure on propensity to

donate. However, results may not be validly extrapolated from one particular country and time

period and applied to another. Whether crowding out happens depends on donor awareness

of government policy and actions, and whether or not information is accurate and donors tend

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to follow current events are characteristics dependent on the particular national demographic

and time period.

The goal of this paper is to add to the literature by focusing on Canadian donors’

reaction to their respective provincial governments’ spending. It provides an update using

recent microdata from the National/Canadian Survey of Giving, Volunteering, and Participating

for the years 1997, 2000, and 2004. Figure 1 shows provincial government expenditure per

capita from 1995 to 2005 for four important subsectors. Even though there is an upward trend,

spending varied significantly among the subsectors throughout the decade. Instead of looking

only at social welfare, I focus on a wider range of public goods: education, healthcare, social

welfare, and the environment. Regression analysis shows that there is no support for crowding

out or in for any of the public goods; that is, government transfers appear to have no significant

effect on donor behaviour across the four subsectors.

The rest of the paper is organized as follows. After a summary of the theoretical work

on the crowding-out hypothesis is a survey of the literature. Next is an overview of the

empirical approach used, followed by a discussion of the results.

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Figure 1

Source: Statistics Canada, Table 385-0002 - Federal, provincial and territorial general government revenue and

expenditures.

THEORIES OF CROWDING-OUT

The theory of crowding-out predicts that when transfers to a public good increase,

private contributions to that good falls. The mechanics can be divided into two familiar effects:

a substitution and an income effect. With the substitution effect, an increase in government

transfer lowers the need for charitable funding, thereby decreasing the marginal benefit of

donating and increasing the marginal benefit of private consumption. Under the income effect,

an increase in government transfers lowers disposable income through taxes and reduces the

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financial ability of taxpayers to make donations2. Both effects operate in the same direction to

crowd out private donations, with the income effect being the less-significant of the two3.

The existence of the substitution effect is subject to some assumptions about the way

giving enters a donor’s utility function. In the Ultra-Rational case, a donor’s decision to give is

made by equating marginal cost (to the donor) and marginal benefit (to the recipient). A donor

will give until the marginal cost of giving equals the marginal benefit. Thus, donations are made

only to raise welfare, and the government is seen as a direct agent in transferring income from

the contributor (through taxes) to the recipient (through transfers). Donations and government

transfers are perfectly substitutable, implying a complete crowd-out. In the case of

Interdependent Utility Functions, the donor’s marginal benefit from contributing is affected by

the recipient’s marginal benefit from receiving. Government transfers lower the marginal

utility of donations for the recipient, and thus for the donor as well, although the effect is

incomplete and implies an imperfect (fractional) crowding-out. The Warm Glow Hypothesis4

(Andreoni, 1990) acknowledges that the act of giving itself has intrinsic benefits to the donor

independent of the effect it may have on the recipient or to the total supply of the public good.

In other words, the contributor’s utility function is independent from the utility of recipient,

2 Information on the substitution and income effects, Interdependent Utility Functions and Ultra-Rational is summarized using

Burton Abrams and Mark Schmitz, “The ‘Crowding-Out’ Effect of Governmental Transfers on Private Charitable Contributions”, Public Choice, 33(1): 1978, 31 3 If a government wants to increase hospital funding, they are likely to reallocate tax money from, say, financing a museum

renovation rather than directly increasing taxes in order to do so. Thus, a taxpayer partial to health care improvement will find her disposable income (ability to donate) might be unchanged, but she might decide to substitute away from donating to health care now that it is being publicly taken care of. 4 James Andreoni, “Impure Altruism and Donations to Public Goods: A Theory of Warm-Glow Giving”, The Economic Journal,

100(401): 1990, 464-477

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and donating enters the donor’s utility function positively by increasing the ‘warm glow’. In the

case of complete independence, with warm-glow being the only motivation for giving,

government spending does not crowd out private donations.

Fewer theories seek to explain the crowding-in phenomenon, which suggests that

potential donors somehow see government transfers as complements to their own giving.

Brooks (2000) and Khanna et al (2000) note that charities can use the fact that they are able to

attract public funding as a signal to potential donors that their money would be put to good

use. Furthermore, government attention stimulates the interest of private donors who might

otherwise have overlooked the cause and organization5. Matching grants, where the

government pledges an amount for every X dollars of private donations a charity can fundraise,

are another (albeit, trivial) way for crowding-in to take place6. One might also speculate that

crowding-in happens because of the bystander-apathy effect7. Experiments in psychology have

shown that a person witnessing an emergency, such as someone else having a seizure, is less

likely to help when she is one of many apathetic bystanders than if she were the only witness.

Being one of many in the same situation diffuses the sense of responsibility amongst all

witnesses. If we see that others are not reacting, then we will not react, either, because we

suspect the blame for not reacting is negligible when many are responsible, or because we look

to each other’s reactions to discern whether or not the situation requires action. In the case of

5 A. C. Brooks, “Is there a Dark Side to Government Support for Non-Profits?” Public Administration Review, 6(3): 2000.,212

6 R. D. Hood et al, “Economic Determinants of Individual Charitable Donations in Canada”, Canadian Journal of

Economics, 10( 4): 1977), 667. 7 Darley, J. M. and B. Latané. “Bystander Intervention in Emergencies: the Diffusion of Responsibility”. Journal of Personality

and Social Psychology. 8(4): 1968. pp. 377-83

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a crowding-in, a lack of ‘reaction’ from the government justifies donor apathy and discourages

action. On the other hand, a move by the government to fund carbon-emission reduction

projects, for example, could trigger similar gestures from citizens. Although bystander apathy is

seldom used to explain economic phenomena, it could provide insight into why some studies

have shown that private donations vary positively with government transfers.

EVIDENCE FROM THE LITERATURE

There is no consensus in the empirical literature on whether government expenditure

crowds in, out, or is independent of, private donations. Noticing a drop in private charitable

contributions right around the time government expenditures increased, Abrams & Schmitz

(1978) focus on the crowding out of donations to social welfare programs in the US. Using US

census and tax returns for the period 1948-1972, they create a quasi-panel to determine the

extent to which state and federal social welfare transfers crowd out private donations.

Charitable donations are grouped by AGI class, while federal transfers are in per capita terms.

After controlling for average disposable income (before contributions) and the price of

donating, p=1-t, where t is the marginal tax rate on the first dollar contributed in class and year,

they find a small but significant crowd out effect. Their estimates suggest that a 1% increase in

government transfers to social welfare programs is met by a 0.2% fall in private donations.

However, that analysis did not account for individual characteristics such as age,

education, and gender. They also are basing their estimations during a very particular time

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period, the post-WWI and Cold War era. It is possible that part of the drop in private charitable

contributions is owed to a change in sentiments particular to that time in history, for example,

an increasing sense of mistrust during the Cold War. In order to correct for strong time trends

and year-specific characteristics as well as control for some personal characteristics, their next

study (Abrams & Schmitz, 1984) uses instead a cross section of US tax returns in 1979. This

time, they focus on state level spending and include a poverty variable as well as religion and

race. Their estimation shows $1 of state transfers leads to 30 cents less of donations.

Though using tax returns has its benefits, including the ability to access detailed

information about income and applicable tax rates, it precludes accounting for donations not

claimed for tax purposes. Since only donations to registered charities are eligible to receive a

tax credit, if there are donations made to unregistered charities or donations that are

unclaimed, the results could be biased. Furthermore, depending on filing status, many US

citizens have the choice between itemizing their charitable contributions, which requires

recordkeeping, and taking a standard deduction8. Where the standard deduction is greater

than or equal to the itemized deduction, it is more advantageous to take the standard

deduction because each itemized deduction must pass under greater scrutiny. Thus, there are

quite a few donations missing from US tax data, and their absence could be correlated with tax

8 “Publication 501”, IRS - US Department of Treasury, Aug. 1

st 2008, Aug. 1

st 2008

<http://www.irs.gov/publications/p501/ar02.html#d0e5569>

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filer status9. Instead of using tax data, Schiff (1985) uses survey data to study the effect of

social welfare spending on donations10. He presents a model in which government transfers

need not crowd out donations, and might indeed encourage donations. Using the National

Survey of Philanthropy in 1974, he finds that the effects vary between local and state transfers,

and vary between cash and welfare program transfers. Local transfers and transfers to welfare

programs crowd in, while state transfers and transfers in cash crowd out.

The discussion so far has been limited to studies of social welfare as the public good of

interest. There are many target areas that concern citizens and their governments, and it is

possible that donations to different public goods will experience varying crowding-out effects.

Brooks (2000) looks at arts and culture from 1966-1997, and welfare, health, and education

from 1955-199511. Lagged values of government spending at the state and federal level for the

period 1955-95 are compared with data of the same period on private donations to nonprofits

in those subsectors. Controlling only for GDP, year trends, and last year’s private giving, Brooks

finds donations to welfare and health care experience a small crowd out with state spending,

while education and the Arts are unaffected by any level of government spending.

9 Usually standard deductions are for those ‘financially deserving’ of deductions, (i.e. dependents, disabled) who

are likely to be the ones donating small amounts, which means lower donation amounts could be systematically missing from the data. 10

Jerald Schiff, “Does Government Spending Crowd Out Charitable Contributions?” National Tax Journal, 38(4): 1985, 535-546. 11

A. C. Brooks, “Is there a Dark Side to Government Support for Non-Profits?” Public Administration Review, 6(3): 2000.,212

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This present paper aims to contribute a number of things to the literature on crowding

out. Firstly, it makes an effort to fill the gap in the literature by focusing on Canada, whose tax

treatment of donations differs from that of the US. Secondly, it focuses on a wider range of

public goods, to acknowledge the extent of politically salient subsectors as well as to allow for

any “crowding” effects to vary across public goods.

EMPIRICAL APPROACH AND DATA

Based on specifications in the literature, for each of the four subsectors, empirical

estimation will take the form:

Yijt = βo + β1Gjt + β2Xijt + β3PROVj + β4YEARt + εij

where Gjt is the dollar amount of transfers per capita in province j at time t, and G denotes

healthcare, social welfare, environment, and education. Xijt represents the vector of controls

for donor characteristics. PROVj is the vector of provincial dummies, and YEARt is the vector of

survey-year dummies. Yijt is the dollar amount contributed per person i in province j at year t to

cause Y, where Y denotes healthcare, social welfare, environment, and education. Although

different variations on this model will be estimated, they will be uniform across four subsectors

in order to be consistent12.

12

A. C. Brooks, “Is there a Dark Side?”, 213.

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A quasi-panel dataset, grouped at the provincial level, is created out of survey and

government statistics. Year dummies capture time-specific effects, and province dummies

capture group-specific effects. Because the main explanatory variable, Gjt, is invariant with

respect to individuals in the same province and year, standard errors may be biased. As such,

group effects are clustered at the province-year level where possible13. Because about a third

of the survey respondents reported donating nothing, ordinary least squares estimation should

produce biased results. The Tobin (Tobit) estimation procedure is typically used in this situation

as it corrects estimates for censoring. A more general variant, Interval Regression (Intreg), will

be used instead, as it allows for the clustering of standard errors while yielding numerically

equivalent results to the Tobit14.

Data for private contributions comes from the National Survey of Giving, Volunteering,

and Participating (NSGVP) of 1997 and 2000 and the Canadian Survey of Giving, Volunteering,

and Participating (CSGVP) of 2004. The NSGVP was conducted as a supplement to the Labour

Force Survey. With the increasing importance of donations to research, funding was granted to

establish a permanent survey within Statistics Canada in 2001, which was renamed the CSGVP.

All three survey thousands of Canadians aged 15 and over, in the ten provinces from September

to December. All donations reported were made within the last 12 months before the survey,

and donations made to charities for purposes of international benefit are listed separately.

There is not much that is different between the two surveys, aside from a few questions asked

13

The small number of clusters (i.e. 30 clusters for a three-year quasi panel, 20 clusters for a two-year quasi-panel) places restrictions on the number of standard errors (parameters) that can be estimated in each regression. 14

The tobit command allows for clustering in Stata 10, but not Stata 9.

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in the NSGVP that have been dropped from the CSGVP. Two questions of interest are whether

the respondent voted in the provincial election, and whether the respondent watches

television at least a few times a week. A few questions are also differently worded; to control

for any effects this might have on the responses, survey-year dummies are included.

As the federal tax treatment of charitable donations in the US is different from that in

Canada, so, too, are the implications for data choice. Charitable donations in the US can be

deducted from taxable income, while in Canada they are used as non-refundable tax credit and

subtracted from the total tax paid. However, in Canada, the first $200 dollars of donations

claimed per tax return is given a credit of 17% in 1997 and 200015 (16% in 200416), while

anything in excess is given 29% credit. There is no similar credit scheme in the US. There is

thus an incentive for Canadians to hold onto charity donations receipts and claim all of them at

once, anytime within five years of the year the donation was made, to receive a higher credit

rate. It is highly probable that the use of tax returns in Canada as data would be misleading,

depending on how detailed the data is, as the year in which donations are claimed may not be

the year in which the donor felt compelled to donate. Furthermore, as mentioned above, not

all donations made are claimed. A taxpayer who has made a single small donation, for

example, might feel it unnecessary to bother to keep or track down the documentation to gain

15

“TI-97 Donations and Gifts” Canada Revenue Agency, 1997, Aug. 1st

2008 < http://www.cra-arc.gc.ca/formspubs/prioryear/t1/1997/5006-s9/5006-s9-10-97e.pdf > “TI-00 Donations and Gifts” Canada Revenue Agency, 2000, Aug. 1

st 2008

<http://www.cra-arc.gc.ca/formspubs/prioryear/t1/2000/5000-s9/5000-s9-00e.pdf> 16

“TI-2004 Donations and Gifts”, Canada Revenue Agency, 2004, Aug. 1st

2008 <http://www.cra-arc.gc.ca/formspubs/prioryear/t1/2004/5000-s8-9/5000-s8-9-04e.pdf >

P a g e | 13

a small tax credit, making it likely that smaller donations will be systematically omitted from tax

returns. Because donation documentation is not required on the survey, a donor is more likely

to report it on a survey than list it on a tax return17.

What could be a major shortcoming of using survey data, however, is that it does not

allow for direct tax-incentive controls. Whereas most contributions to the literature control for

the price of giving, p=1-t, where t is the marginal tax rate on the first dollar donated, income is

noted in survey data in broad categories, thereby making it difficult to determine the applicable

tax rates for each respondent. The effect of tax credit on donations is partially captured by an

income variable. The higher the income, the higher the ability to donate and the higher the tax

benefits to donating. Nevertheless, p should have a negative effect on charitable giving, and its

absence suggests that the coefficient on government transfers may be negatively biased.

Government expenditure is represented by provincial transfers, the data on which are

obtained from CANSIM. The federal government does exert some spending power over the

four subsectors through conditional and unconditional grants to the provincial governments18,

for example, but not enough data exists to be able to create statistics for total per-province

(provincial and federal), per-subsector expenditure. Nonetheless, education, healthcare, and

17

On the other hand, donors are more likely to exaggerate donations or even make them up. 18

“The Spending Power – Scope and Limitations”. Library of Parliament. 2008. Aug. 1st

2008. <http://www.parl.gc.ca/information/library/PRBpubs/bp272-e.htm>

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welfare are all within provincial jurisdiction,19 and provincial spending statistics provide an

adequate picture of where taxpayers in each province see their tax dollars going20. The overlap

between what the measures of government spending encompasses and what is measured for

private donations in the survey is imperfect. It should matter little to our analysis, however.

More detailed descriptions of what each transfer and donation variable encompasses is given in

Table 1.

In order to account for differently sized provinces, all government transfer variables are

converted into per capita terms using provincial population data from CANSIM. Although it

would have been informative to include them, the data are incomplete for the Yukon and

Northwest Territories and they are excluded from this analysis. Government transfer variables

are constructed by taking an average of spending in the previous two years. For example, if the

expenditure measure is to correspond to private donations in the year 2000, an average was

taken of provincial spending in the year 1998 and 1999. The lagged values correct for

simultaneous correlation. For example, an increase in government transfers to healthcare in

1997 might be brought about by a demand for increased funding to hospitals in the same year.

The demand for increased funding to hospitals will, in the same year, encourage private

donations to healthcare as well.

19

Environment is a recent subsector and the federal-provincial lines are unclear. For more, see Gibson, Dale, “Constitutional Jurisdiction over Environmental Management in Canada”, University of Toronto Law Journal, 23(1): 1973, p. 54. 20

Brooks (2000) finds state spending, not federal spending, has a significant effect on donations, which suggests that donors are more sensitive to local government actions.

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Table 1

Transfers and donations Private donations

Health care Organizations engaged in out-patient health-related activities and support services, education and research for specific conditions (i.e. Heart and Stroke Foundation), hospitals.

Education Organizations and activities administering, providing, supporting education and research. Includes primary, secondary, post secondary, adult, and vocational education as well as research organizations.

Social welfare

Organizations providing social services to the community. Includes emergency relief and income support and maintenance.

Environment Organizations promoting and providing environmental conservation, pollution control, environmental education and health, animal protection.

Government transfers

Health care Medical care, preventative care, other health service

Education Elementary and secondary education, post secondary, special retraining, other

Social welfare

Income maintenance, social security, family allowances, etc

Environment Water purification and supply, pollution control, other environmental services

Note: Information on private donations found in data codebook. Information on government transfers found on CANSIM accompanying data series.

Table 2

Summary statistics Private donations variables used

Median Mean Min Max Std Deviation

Health care 20 62.8773 0 17140 260.9197

Education 0 16.1991 0 23162 212.5742

Soc. welfare 3 37.4763 0 49759 369.8202

Environment 0 7.2211 0 7400 95.2769

Government transfers variables used

Health care 1701.907 1844.337 1310.34 2488.774 328.5941

Education 1295.276 1284.244 857.3799 2060.979 287.6167

Soc. welfare 1047.109 1050.18 562.2576 1425.514 194.9936

Environment 52.7986 63.0587 30.1699 215.058 32.8093

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RESULTS AND DISCUSSION

Overall, the regression results do not support the theory that government expenditures

will crowd out (or in) donations. This result is fairly consistent across all four subsectors;

although some specifications indicate crowding out in some sectors and crowding in for others,

those results suffer from biased standard errors and are of questionable significance. The

control variable coefficients have the expected signs and are consistent with the similar

estimates in the literature.

Table 3 displays results from the first (baseline) specification. Private donations are not

significantly affected by government transfers. Donation amounts increase with age, although

the areas of education and the environment are, understandably, less of a priority for retirees

relative to their middle-aged counterparts. As we might suspect, respondents with higher

household income donate more, and as mentioned before, it is likely that the coefficients show

the effects of both an income (ability to donate) and a tax credit (incentive to donate). Having

only a high school diploma or less lowers the amount donated by 17-33 dollars, depending on

the subsector, relative to those with a higher education. Where a respondent was born has no

significant effect on donation amount, and neither does religiosity, with the exception of the

environment, to which being non-religious increases donations. Membership in a political or

neighbourhood organization has a significant positive effect on donations to education, social

welfare, and the environment.

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Table 3

Model 1 results: 1997, 2000, & 2004 Health Education Soc. welfare Environment

Government transfers

Health care 0.0286 (0.0342)

Education 0.0237 (0.03432)

Social welfare -0.1187 (0.0799)

Environment -0.2291 (0.1592)

Age group (15-24 dropped)

25-34 12.2374 (6.9692)*

54.0603 (16.3541)***

80.1311 (36.5742)**

98.0889 (18.9231)***

35-44 26.2319 (9.7440)**

90.8502 (20.9377)***

113.6355 (39.9830)***

97.4129 (25.7663)***

45-54 36.6666 (10.6890)***

75.1268 (21.1438)***

156.2897 (58.7002)***

129.4887 (25.8502)***

55-64 40.3498 (13.3195)***

105.4233 (28.8368)**

168.4237 (57.9801)***

168.1662 (34.5422)***

65 + 49.1279 (17.5665)***

47.2298 (17.1464)***

175.698 (55.4448)***

155.7813 (29.3557)***

Household income group before taxes (< 20,000 dropped)

[$20,000, $40000)

26.0152 (4.5445)***

39.4371 (10.9623)***

42.2160 (12.0534)***

40.2973 (10.6862)***

[40,000, 60,000)

30.9435 (6.7434)***

77.2505 (18.6936)***

73.1700 (15.3577)***

101.7197 (17.0583)***

[60,000, 100,000)

36.3122 (8.1535)***

101.1627 (20.1301)***

80.6681 (15.5790)***

126.223 (17.0864)***

100,000 + 46.8610 (19.6171)**

179.6319 (38.8470)***

132.0525 (20.1697)***

166.3118 (27.4914)***

Completed education (≥ some post secondary dropped)

≤ High school -32.9966 (3.8398)***

-17.3121 (5.2183)***

-21.5186 (6.8992)***

-32.6341 (10.5048)***

Socio-demographic (Foreign born and Religious dropped)

Canadian born 5.5589 (7.2916)

12.6921 (12.0446)

11.0734 (7.7887)

7.8413 (10.7013)

Non religious 2.1660 (4.0580)

-8.9684 (8.0010)

6.3486 (14.8293)

84.6836 (15.2844)***

Gender Female 9.9728 (3.1593)***

11.96205 (6.0981)**

24.3468 (10.6126)**

73.9473 (12.7412)***

Membership in organization

Political 5.6181 (6.7667)

69.43304 (15.7013)***

94.8513 (43.3661)**

109.7404 (14.2284)***

Neighbourhood 7.9391 70.9151 40.8863 27.3570

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(4.5380)* (13.4335)*** (9.7843)*** (9.3449)***

Constant -140.4908 (70.6658)**

-560.9161 (135.6356)***

-250.4825 (93.3967)***

-1044.543 (147.8973)***

Wald chi-square (p-value)

4689.89 (0.0000)

5852.01 (0.0000)

671.6900 (0.0000)

1330.76 (0.0000)

Observations 46726 46726 46726 46726 Note: *, **, and *** indicate significance at the 90, 95, and 99 percent level, respectively.

Standard errors are reported in brackets. Provincial and Year dummies were used in all models. Standard errors are clustered by province-year.

Although results from the baseline model suggest a victory for the ‘warm glow’

hypothesis, it is likelier that imperfect information is the culprit. Whether or not government

transfers affect private donations depends on public awareness of government actions. The

second specification uses two measures of respondent awareness: whether or not they voted in

the last provincial election, and how often they watch the news. Unfortunately, the two

questions corresponding to these measures were dropped from the 2004 CSGVP survey, so a

quasi-panel of two years, 1997 and 2000, is used instead. This reduces the number of province-

year clusters from 30 down to 20, further limiting the number of parameters that can be

estimated. Rather than omit a number of key variables from the model to fit in new variables,

the clustering of standard errors is dropped. Thus, the significance of coefficients in Tables 4

and 5 is to be interpreted with caution.

Table 4 shows the results with two new controls entering the model linearly. The

inclusion of controls for awareness does not fundamentally change the insignificance of the

government transfers variables. The only significant government transfer coefficient is on

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transfers to healthcare, suggesting a very small crowd-out effect; however, a 10% level

significance is not enough to ensure that the coefficient would retain its significance if standard

errors were to be clustered, assuming there is no negative correlation within clusters. Even if

the coefficient remains or increases significance with correct standard errors, the crowd-out

effect would be very small, with a 4-cent drop for every dollar the government spends on

healthcare. The rest of the controls are similar in direction and magnitude to the above

baseline specification, with religiosity and country of birth gaining in significance. Interestingly,

being non-religious decreases the amount donated to healthcare, education, and social welfare,

but increases the amount donated to environmental causes, relative to religious persons.

Table 4

Model 2 results: 1997 & 2000 only Health Education Soc. welfare Environment

Government transfers

Health care -0.0417* (0.0247)

Education 0.0003 (0.0156)

Social welfare -0.0253 (0.0211)

Environment (0.4980) (0.4454)

Age group (15-24 dropped)

25-34 32.9580 (6.0978) ***

19.7096 (5.9437)***

17.4371 (5.2215)***

86.8062 (18.8418)***

35-44 54.7322 (5.9192)***

36.0786 (5.7599)***

37.2763 (5.0718)***

69.6226 (18.6077)***

45-54 67.9803*** (6.2340)

22.5844 (6.1270)***

52.55821 (5.3443)***

99.7956 (19.2926)***

55-64 87.8471 (6.6084)***

35.8431 (6.4922)***

57.5336 (5.6694)***

127.8144 (20.4315)

65 + 110.1534 (6.4370)***

17.6885 (6.4530)***

62.1672 (5.5324)***

102.3794 (20.4223)***

Household income group

[$20,000, $40000)

26.48938 (4.4081)***

19.0640 (4.4644)***

19.6679 (3.7924)***

28.76814 (14.2342)**

P a g e | 20

before taxes (< 20,000 dropped)

[40,000, 60,000) 48.3113 (4.7125)***

34.3361 (4.6950)***

33.9047 (4.0317)***

96.5704 (14.4429)***

[60,000, 100,000)

64.3909 (4.9399)***

47.6133 (4.8700)***

42.0658 (4.2209)***

116.7078 (14.9005)***

100,000 + 136.8106 (6.3383)***

84.9076 (6.0972)***

83.2176 (5.4132)***

143.7738 (17.9086)***

Completed education (≥ some post secondary dropped)

≤ High school -18.8762 (3.0754)***

-13.4092 (2.9120)***

-19.8968 (2.5402)***

-31.33838 (8.7087)***

Socio-demographic (Foreign born and Religious dropped)

Canadian born 24.3308 (4.5585) ***

10.6151 (4.5514)**

1.9778 (3.8984)

-9.9964 (12.5109)

Non religious -6.6945 (3.7679)*

-10.9073 (3.7287)***

-7.3586 (3.2635)**

75.3667 (9.9085)***

Gender Female 14.0588 (2.9293) ***

4.9351 (2.8754)*

5.7179 (2.5179)**

63.03706 (8.6524)***

Organization membership

Political 24.78059 (6.1248)***

36.7529 (5.7652)***

25.9944 (5.1791)***

78.6520 (15.7713)***

Neighbourhood 13.7636 (3.9793)***

28.8334 (3.7742)***

21.4380 (3.4066)***

27.4496 (10.7670)**

Measures of awareness (Follow news ≤ few times a month dropped)

Voted last Prov. Election

32.9339 (3.9889)***

7.1035 (3.9160)*

18.2323 (3.4366)***

13.2138 (11.5186)

Follow news ≥ several times a week

17.85913 (8.0723)**

-1.6747 (7.7296)

13.854 (6.8522)**

27.7707 (24.7680)

Constant -167.4568 (38.4414)***

-220.7575 (23.8496)***

-114.8057 (29.3000)***

-1033.534 (58.6470)***

Wald chi-square (p-value)

1838.41 (0.0000)

736.41 (0.0000)

954.70 (0.0000)

748.36 (0.0000)

Observations 28221 28221 28221 28221 Note: *, **, and *** indicate significance at the 90, 95, and 99 percent level, respectively.

Standard errors are reported in brackets. Provincial and Year dummies were used in all models. Standard errors are not clustered; see text for more information.

Intuition suggests that the crowding-out (or in) effect may be more evident in those

people who are more aware of the provincial government’s actions. Table 5 shows results from

P a g e | 21

the third estimation. In this specification, government transfer variables are interacted with

dummies indicating whether the respondent voted in the last provincial election, and whether

he or she follows the news at least several times a week. The results are mixed across

subsectors.

For healthcare, estimates show that a dollar of government transfers crowds in 6 cents

of donations in general, and environmental spending crowds in a little over a dollar’s worth of

donations in general. A small crowd-out effect for healthcare is seen in respondents who voted

in the previous provincial election, while a small crowd-in effect is seen in respondents who

follow the news. This is perhaps because those who vote in the provincial election are

informed about promises to spend on healthcare, while those who watch the news may pick up

on criticising reports about the need for healthcare funding. Similarly, education donations are

crowded-in among respondents who follow current events. On the other hand, social spending

crowds out donations among those who follow the news. Figure 1 shows that health and

education are the two top spending areas in Canada, so it should come as no surprise that they

are under the most scrutiny in the media. It is harder to understand why social spending, which

comes in at a close third in spending priority, would yield a different result. That said, as the

standard errors may be biased, it would not be much use to overanalyze the results in Model 3.

P a g e | 22

Table 5

Model 3 results: 1997 & 2000 with interactions Health Education Soc. welfare Environ.

Government transfers

Health care 0.0668 (0.0293)**

Education 0.0003 (0.0179)

Social welfare -0.0162 (0.0277)

Environment 1.04763 (0.6313)*

Government transfers interacted with awareness measures (vprov = voted in last provincial election; news = follow news ≥ several times a week)

Health*vprov -0.0402 (0.0213)*

Education*vprov -0.0039 (0.0122)

Social*vprov 0.0164 (0.0162)

Environ*vprov -0.3048 (0.4096)

Health* news 0.0519981 (0.0222)**

Education*news 0.0249 (0.0112)**

Social *news -0.0221 (0.0135)*

Environ*news -0.3526 (0.3805)

Age group (15-24 dropped)

25-34 12.3973 (5.8549)**

19.8035 (5.9438)***

17.4565 (5.2212)***

86.8180 (18.8461)***

35-44 24.8726 (5.7043)***

36.1853 (5.7601)***

37.2839 (5.0714)***

69.8343 (18.6152)***

45-54 37.0906 (6.0470)***

22.7229 (6.1273)***

52.5738 (5.3438)***

99.8983 (19.2976)***

55-64 43.7689 (6.4437)***

36.0285 (6.4930)***

57.4469 (5.6691)***

127.7742 (20.4371)***

65 + 59.4326 (6.2400)***

17.9342 (6.4540)***

62.0567 (5.5325)***

102.1525 (20.4260)***

Household income group before taxes (< 20,000 dropped)

[$20,000, $40000)

27.5766 (4.3454)***

19.0360 (4.4643)***

19.5624 (3.7927)***

28.8345 (14.2382)**

[40,000, 60,000)

33.6685 (4.6577)***

34.3128 (4.6951)***

33.8364 (4.0317)***

96.6992 (14.4456)***

[60,000, 100,000)

40.1472 (4.8948)***

47.6658 (4.8701)***

41.9983 (4.2210)***

116.7097 (14.9026)***

100,000 + 79.6616 (6.3955)***

84.6627 (6.0986)***

83.1560 (5.4136)***

143.86 (17.9133)***

Completed education (≥ some post secondary dropped)

≤ High school -24.8126 (2.9959)***

-13.4608 (2.9123)***

-19.8385 (2.5402)***

-31.2525 (8.7099)***

Socio-demographic Canadian born 7.8322 10.6992 1.967524 -9.9273

P a g e | 23

(Foreign born and Religious dropped)

(4.5266)* (4.5527)** (3.8982) (12.512)

Non religious -2.4094 (3.7711)

-10.8968 (3.7293)***

-7.4310 (3.2635)**

75.3260 (9.9089)***

Gender Female 9.2512 (2.9545)***

4.9745 (2.8755)*

5.6903 (2.5178)**

63.1425 (8.6545)***

Organization membership

Political 19.1798 (6.3924)***

36.6943 (5.7662)***

26.0324 (5.1791)***

78.6631 (15.7772)***

Neighbourhood 2.5653 (4.1426)

28.9523 (3.7745)***

21.5016 (3.4067)***

27.3134 (10.7695)**

Measures of awareness (Follow news ≤ few times a month dropped)

Voted last Prov. Election

78.3671 (34.9407)**

11.5018 (14.9483)

1.6637 (16.7025)

30.9524 (26.4340)

Follow news ≥ several times a week

-44.9961 (35.7800)

-30.49852 (15.0973)**

36.9615 (15.7963)**

51.7527 (35.3560)

Constant -242.5931 (46.3132)***

-224.2166 (25.8524)***

-127.7774 (35.3868)***

Wald chi-square (p-value)

689.78 (0.0000)

741.36 (0.0000)

958.14 (0.0000)

(0.0000)

Observations 28221 28221 28221 28221

Note: *, **, and *** indicate significance at the 90, 95, and 99 percent level, respectively. Standard errors are reported in brackets. Provincial and Year dummies were used in all models. Standard errors are not clustered; see text for more information.

CONCLUSION

Although government spending in Canada is perpetually under scrutiny in the media and

academia alike, its effects on donations has been little studied. Using recent survey data from

the NSGVP and CSGVP, I estimate the effect of government transfers on private donations for

four public goods: healthcare, education, social welfare, and the environment. The baseline

estimations suggest no crowd-out (or in) effect. The second and third specifications control for

the respondent’s awareness of government actions. The results from including these new

controls show that awareness might be an important factor in whether determining whether or

not donations are affected by government spending. Health and education experience a crowd-

P a g e | 24

in among those who regularly watch the news, while health experienced a weakly significant

crowd-out among those who voted in the last provincial election. Social spending causes a

weakly significant crowd-out among followers of the news. Healthcare spending, in general,

crowds in donations, while environmental spending, in general, crowds out donations.

The main conclusion to take away from this exercise is that, as far as correct standard

errors are concerned, there does not appear to be any support for either a crowding-out or a

crowding-in effect. Private donation variables across healthcare, education, social welfare, and

the environment are unresponsive to respective changes in government transfers. Results from

the third specification may suggest that the effect of government transfers on donations varies

across individuals with different levels of awareness about government actions. However

interesting these results may be, their economic as well as policy significance is questionable.

Where the estimated crowding effect is large, the significance is weak and where the

significance is higher, the magnitude of the effect is negligible and not worth tailoring spending

policy around. There is thus ample room in the literature for more investigation into how

awareness interacts with changes in government expenditures to affect donations.

P a g e | 25

REFERENCES

Abrams, Burton and Mark Schmitz. “The ‘Crowding-Out’ Effect of Governmental Transfers on Private Charitable Contributions”. Public Choice. 33(1): 1978. 29-39.

Abrams, Burton and Mark Schmitz. “The Crowding-Out Effect of Governmental Transfers on Private Charitable Contributions: Cross-Section Evidence”. National Tax Journal. 37(4): 1984. 563-67.

Andreoni, James. “Impure Altruism and Donations to Public Goods: A Theory of Warm-Glow Giving”. The Economic Journal, 100(401): 1990. pp. 464-477

Brooks, A.C. “Is there a Dark Side to Government Support for Non-Profits?” Public Administration Review. 6(3): 2000. 211-17.

“Tax Advantages of Donating to Charity” Canada Revenue Agency. 2008. Aug. 1st 2008. <http://www.cra-arc.gc.ca/E/pub/tg/rc4142/rc4142-05e.pdf>

Darley, J. M. and B. Latané. “Bystander Intervention in Emergencies: the Diffusion of Responsibility”. Journal of Personality and Social Psychology. 8(4): 1968. pp. 377-83.

Estimates of population, by age group and sex . [Data file]. Retrieved from http://cansim2.statcan.ca (Table 051-0001). Statistics Canada.

Federal, provincial and territorial general government revenue and expenditures [Data file]. Retrieved

from http://cansim2.statcan.ca (Table 385-0002 ). Statistics Canada.

Friedman, M., and Rose D. Friedman. Free to Choose: A Personal Statement. New York: Harcourt Brace Jovanovich, 1980.

Gibson, Dale, “Constitutional Jurisdiction over Environmental Management in Canada”, University of Toronto Law Journal, 23(1): 1973, pp. 54-87.

Hood, R.D. et al, “Economic Determinants of Individual Charitable Donations in Canada”, Canadian Journal of Economics, 10( 4): 1977), pp. 653-669

Internal Revenue Service Publication 526 (2007), “Charitable Contributions”, 2007. Aug. 1st 2008. <http://www.irs.gov/publications/p526/index.html >

Khanna, Jyoti et al. “Charity Donations in the UK: New Evidence Based on Panel Data.” Journal of Public Economics. 56(2): 1995. pp. 257-272

Khanna, J. and T. Sandler. “Partners in Giving: The Crowding-In Effects of UK Government Grants”. European Economic Review. 44(8): 2000. pp. 1543-56.

“Publication 501”, IRS - US Department of Treasury, Aug. 1st 2008, Aug. 1st 2008 <http://www.irs.gov/publications/p501/ar02.html#d0e5569>

Roberts, Russell D. “A Positive Model of Private Charity and Public Transfers”. The Journal of Political Economy, Vol. 92, No. 1 (Feb., 1984), pp. 136-1.

P a g e | 26

Schiff, Jerald. “Does Government Spending Crowd Out Charitable Contributions?” National Tax Journal. 38(4): 1985. pp. 535-546.

“The Spending Power – Scope and Limitations”. Library of Parliament. 2008. Aug. 1st 2008. <http://www.parl.gc.ca/information/library/PRBpubs/bp272-e.htm>

“TI-97 Donations and Gifts” Canada Revenue Agency, 1997, Aug. 1st 2008 < http://www.cra-arc.gc.ca/formspubs/prioryear/t1/1997/5006-s9/5006-s9-10-97e.pdf >

“TI-00 Donations and Gifts” Canada Revenue Agency, 2000, Aug. 1st 2008 <http://www.cra-arc.gc.ca/formspubs/prioryear/t1/2000/5000-s9/5000-s9-00e.pdf>

“TI-04 Donations and Gifts”, Canada Revenue Agency, 2004, Aug. 1st 2008 <http://www.cra-arc.gc.ca/formspubs/prioryear/t1/2004/5000-s8-9/5000-s8-9-04e.pdf >

Veall, M.R. and Klaus Zimmermann. “Goodness of Fit Measures in the Tobit Model”. Oxford Bulletin of Economics and Statistics. 56(4): 1994. 485-501.