government expenditures and philanthropic donations: exploring crowding-out with cross-country data

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

The crowding-out hypothesis

Alexis de Tocqueville 1840

Robert Nisbet 1953

Milton Friedman 1962

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

Crowding-out…

Altruistically motivated donors Changing need

Fundraising efforts

…or crowding-in?

Government funding as source of information Institutions shape people’s values

What role for the state?

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



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

No strong correlation

Different nonprofit regime types

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)


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,


Total giving: No association

P<.05

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


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,



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,


Crowding-in of donors per sector

P<.01 P<.05 P<.05

Crosswise crowding-in (1)


Yijs = Donations to environment, international aid, or arts and culture

Gjs = Government expenditures to social protection and health


P<.01

P<.10

P<.01

Conclusions

Conclusions

Crowding-in of donors

Conclusions

Crowding-in of donors But less so in health and social protection subsectors

Conclusions


Social welfare expenditures seem to drive donors towards ‘expressive’ subsectors

Conclusions



No crowding-out of amounts donated

Conclusions



No crowding-out of amounts donated

Important null finding

Thank you

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

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

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

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

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

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

government expenditures and philanthropic donations: exploring crowding-out with cross-country data

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