45th ARNOVA Annual Conference, Washington, DC, USA

1. Government Expenditures and Philanthropic Donations:
Exploring Crowding-Out with Cross-Country Data
Arjen de Wit Vrije Universiteit Amsterdam
Michaela Neumayr WU Vienna
Pamala Wiepking Erasmus University Rotterdam
Femida Handy University of Pennsylvania
45th ARNOVA Annual Conference
Washington D.C., USA
November 18, 2016

2. The crowding-out hypothesis
Alexis de Tocqueville
1840
Robert Nisbet
1953
Milton Friedman
1962

3. The crowding-out hypothesis
“For every welfare state, if social obligations become
increasingly public, then its institutional arrangements
crowd out private obligations or make them at least no
longer necessary”
(Van Oorschot and Arts 2005: 2)
Alexis de Tocqueville
1840
Robert Nisbet
1953
Milton Friedman
1962

4. Crowding-out…
 Altruistically motivated donors
 Changing need
 Fundraising efforts

5. …or crowding-in?
 Government funding as source of information
 Institutions shape people’s values

6. What role for the state?

7. What role for the state?

8. What role for the state?

9. What role for the state?

10. What role for the state?

11. Cross-country comparison
 Individual International Philanthropy Database (IPD)

12. Cross-country comparison
 Individual International Philanthropy Database (IPD)
 The Matthew Effect in
Philanthropy: How Philanthropic
Structure Enables Philanthropic
Giving
 Sat, November 19, 12:15 to
1:45pm, Thornton C

13. Cross-country comparison
 Individual International Philanthropy Database (IPD)

14. Cross-country comparison
 Individual International Philanthropy Database (IPD)
 19 countries: Australia, France, UK, the Netherlands,
US, Canada, Norway, Finland, Mexico, South Korea,
Japan, Austria, Indonesia, Taiwan, Ireland, Israel,
Russia, Germany and Switzerland.
 Context data: IMF

15. No strong correlation

16. Different nonprofit regime types

17. Different nonprofit regime types

18. Multilevel regression model (1)
p(Y)ij / (1 – p(Y)ij)
= β0 + uj + β1Gj + … + εij
Probability that respondent i in country j donates
uj is the country-specific intercept
Gj is government expenditures in country j
εij is the error term for each observation
Controls: GDP per capita (L2), age, education, gender,
marital status, income (L1)

19. Multilevel regression model (2)
ln(Yij)
= β0 + uj + β1Gj + … + εij
Natural logarithm of amount donated by respondent i
in country j, conditional on donating
uj is the country-specific intercept
Gj is government expenditures in country j
εij is the error term for each observation
Controls: GDP per capita (L2), age, education, gender,
marital status, income (L1)

20. Total giving: No association
P<.05

21. However…
 Positive and negative correlations may cancel each
other out
 There could be different effects in different nonprofit
subsectors
 Government support in social welfare could drive
donors to other ‘expressive’ subsectors

22. Multilevel regression model (3)
p(Y)ijs / (1 – p(Y)ijs)
= β0 + ujs + β1Gjs + … + εijs
Probability that respondent i in country j donates to
sector s
ujs is the country/sector-specific intercept
Gjs is government expenditures to sector s in country j
εijs is the error term for each observation
Controls: GDP per capita (L2), age, education, gender,
marital status, income (L1)

23. Multilevel regression model (4)
ln(Yijs)
= β0 + ujs + β1Gjs + … + εijs
Natural logarithm of amount donated by respondent i
in country j to sector s, conditional on donating
ujs is the country/sector-specific intercept
Gjs is government expenditures to sector s in country j
εijs is the error term for each observation
Controls: GDP per capita (L2), age, education, gender,
marital status, income (L1)

24. Crowding-in of donors per sector
P<.01 P<.05 P<.05

25. Crosswise crowding-in (1)

26. Crosswise crowding-in (1)

27. Crosswise crowding-in (2)
 Yijs = Donations to environment, international aid, or
arts and culture
 Gjs = Government expenditures to social protection and
health

28. Crosswise crowding-in (3)
P<.01
P<.10
P<.01

29. Conclusions

30. Conclusions
 Crowding-in of donors

31. Conclusions
 Crowding-in of donors
 But less so in health and social protection subsectors

32. Conclusions
 Crowding-in of donors
 But less so in health and social protection subsectors
 Social welfare expenditures seem to drive donors
towards ‘expressive’ subsectors

33. Conclusions
 Crowding-in of donors
 But less so in health and social protection subsectors
 Social welfare expenditures seem to drive donors
towards ‘expressive’ subsectors
 No crowding-out of amounts donated

34. Conclusions
 Crowding-in of donors
 But less so in health and social protection subsectors
 Social welfare expenditures seem to drive donors
towards ‘expressive’ subsectors
 No crowding-out of amounts donated
 Important null finding

35. Thank you

36. Donated (0/1), total giving
(1) (2) (3) (4)
Govt expenditures / 1,000 0.009 0.019 0.040 -0.139
(0.015) (0.039) (0.041) (0.117)
Regime: Liberal Ref
Regime: Social-Democratic -1.973
(2.289)
Regime: Corporatist -3.346*
(2.002)
Regime: Statist -3.369*
(2.009)
Soc-Dem * Govt expenditures / 1,000 0.145
(0.139)
Corporatist * Govt expenditures /1,000 0.180
(0.131)
Statist * Govt expenditures / 1,000 0.127
(0.139)
Constant 0.457* 0.490* -0.186 3.250
(0.241) (0.268) (0.280) (2.029)
Observations 126,923 126,923 126,923 126,923
Number of country 19 19 19 19
Rho 0.082 0.082 0.088 0.043
(2) Controlled for GDP
(3) & (4) Controlled for GDP, Age, Education, Male, Married, Income (ln)
Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

37. Amount donated (ln), total giving
(1) (2) (3) (4)
Govt expenditures / 1,000 0.067** -0.093 -0.053 -0.317
(0.032) (0.076) (0.089) (0.297)
Regime: Liberal ref.
Regime: Social-Democratic -6.277
(5.818)
Regime: Corporatist -4.332
(5.089)
Regime: Statist -5.018
(5.106)
Soc-Dem * Govt expenditures / 1,000 0.324
(0.354)
Corporatist * Govt expenditures /1,000 0.278
(0.334)
Statist * Govt expenditures / 1,000 0.133
(0.353)
Constant 0.457* 0.490* -0.186 3.250
(0.241) (0.268) (0.280) (2.029)
Observations 126,923 126,923 126,923 126,923
Number of country 19 19 19 19
Rho 0.082 0.082 0.088 0.043
(2) Controlled for GDP
(3) & (4) Controlled for GDP, Age, Education, Male, Married, Income (ln)
Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

38. Donated (0/1), per sector
(1) (2) (3) (4)
Govt expenditures per sector / 1,000 0.127 *** 0.120 ** 0.129 ** 2.700 ***
(0.043) (0.056) (0.059) (0.535)
Sector: Environment ref
Sector: Education -1.050
(1.283)
Sector: Health 0.461
(0.500)
Sector: Social services 1.852 ***
(0.566)
Education * Govt expenditures / 1,000 -1.913 **
(0.815)
Health * Govt expenditures / 1,000 -2.435 ***
(0.541)
Social * Govt expenditures / 1,000 -2.741 ***
(0.536)
Constant - 0.905*** - 1.005
***
- 1.601
***
-2.234 ***
(0.114) (0.187) (0.446) (0.437)
Observations 157,392 157,392 157,392 157,392
Number of country-sector 39 39 39 39
Number of respondents 40,899 40,899 40,899 40,899
Rho 0.177 0.177 0.177 0.132
(2) Controlled for GDP
(3) & (4) Controlled for GDP, Age, Education, Male, Married, Income (ln)
Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

39. Amount donated (ln), per sector
(1) (2) (3) (4)
Govt expenditures per sector / 1,000 -0.022 -0.087 -0.068 -1.453
(0.055) (0.063) (0.071) (1.741)
Sector: Environment Ref
Sector: Education 1.367
(1.634)
Sector: Health -0.363
(0.878)
Sector: Social services -0.357
(1.107)
Education * Govt expenditures / 1,000 0.594
(1.871)
Health * Govt expenditures / 1,000 1.382
(1.741)
Social * Govt expenditures / 1,000 1.409
(1.763)
Constant 3.878*** 3.082*** 1.919*** 2.052**
(0.210) (0.455) (0.505) (0.926)
Observations 49,725 49,725 49,725 49,725
Number of country-sector 26 26 26 26
Number of respondents 27,453 27,453 27,453 27,453
Rho 0.225 0.196 0.208 0.242
(2) Controlled for GDP
(3) & (4) Controlled for GDP, Age, Education, Male, Married, Income (ln)
Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

40. Donated (0/1), crosswise
(1) (2) (3)
Social protection and health expenditures / 1,000 0.154 *** 0.108 * 0.146 ***
(0.030) (0.057) (0.056)
Constant -2.342 *** -2.695 *** -3.193 ***
(0.239) (0.302) (0.434)
Observations 115,825 115,825 115,825
Number of country-sector 28 28 28
Number of respondents 40,899 40,899 40,899
Rho 0.123 0.119 0.115
Y = giving to organizations in the fields of social services, health, environment,
international relief or arts and culture
(2) Controlled for GDP
(3) Controlled for GDP, Age, Education, Male, Married, Income (ln)
Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

41. Amount donated (ln), crosswise
(1) (2) (3)
Social protection and health expenditures / 1,000 -0.032 -0.077 -0.016
(0.055) (0.067) (0.046)
Constant 4.326*** 3.778*** 2.497***
(0.469) (0.664) (0.477)
Observations 11,245 11,245 11,245
Number of country-sector 17 17 17
Number of respondents 9,180 9,180 9,180
Rho 0.175 0.169 0.181
Y = giving to organizations in the fields of social services, health, environment,
international relief or arts and culture
(2) Controlled for GDP
(3) Controlled for GDP, Age, Education, Male, Married, Income (ln)
Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1