Some stuff we're working on

Arjen de Wit

IUPUI School of Philanthropy

June 5, 2014

This is where we work

This is where we work

Crowding-Out: A Meta-Analysis

Meta-analysis Systematic literature review We collect effect sizes published in previous

research We seek to explain differences in effect sizes

between studies by characteristics of samples and publications

Meta-analysis: collecting studies Y = Amount of private donations X = Government contribution Retrieval in Web of Science through EndNote Our search now extends back to 1990 We include only original empirical quantitative

results N = 259 estimates from 47 articles

Crowding-out estimates

Mean crowding-out estimate

Mean effect of $1 increase in govt support

(N=190) – 0.231 (0,502)

Stronger crowding-out in lab experiments...

Mean effect of $1 increase in govt support

Lab experiments (N=49) – 0.628 (0.320)

Archival or survey data (N=141)

– 0.093 (0.481)

...models using instrumental variables...

Mean effect of $1 increase in govt support

Instrumental variables (N=43)

– 0.280 (0.711)

Other regression models (N=97)

– 0.019 (0.296)

(Lab experiments excluded)

...in the USA...

Mean effect of $1 increase in govt support

USA (N=159) – 0.227 (0.504)

Europe (N=141) 0.071 (0.435)

...and in the social sector.

Mean effect of $1 increase in govt support

Health (N=13) 0.033 (0.156)

International aid (N=11) – 0.035 (0.111)

Education (N=42) 0.061 (0.581)

Culture (N=21) 0.032 (0.392)

Social benefits (N=23) – 0.334 (0.292)

No big differences between regression models...

Mean effect of $1 increase in govt support

Fixed-effects / First-difference (N=95)

– 0.093 (0.551)

Other regression models (N=45)

– 0.112 (0.266)

(Lab experiments excluded)

...or the way government contributes.

Mean effect of $1 increase in govt support

Subsidies (N=128) – 0.099 (0.495)

Expenditures (N=13) – 0.041 (0.317)

(Lab experiments excluded)

Estimates nested in studies

Random-effects model

(Constant) – 0.281 (0.066)

Between-study SD 0.340

Rho 0.544

No. of studies 35

Observations 190

There is evidence for moderate crowding-out Finding strongly dependent on research design

Discussion

Crowding-Out in the Dutch Voluntary Sector

Giving in the Netherlands Panel Survey 2004 – 2012 (N=2,025)

Donations to 17 big charities Organizational information from Dutch Bureau

on Fundraising (CBF) Matching these data enables testing both

crowding-out and moderating effects

Longitudinal data

Trends in subsidies and donations (1)

Trends in subsidies and donations (2)

Trends in subsidies and donations (3)

Decreases (increases) in subsidies are not necessarily followed by increases (decreases) in donations

Instead, subsidies and donations often show the same pattern

Discussion

Estimate crowding-out in regression models Examine individual heterogeneity

–Altruistic values

– Income

–Political engagement

To do

Giving in the Netherlands Panel Survey (GINPS)

Deleted: scales on life satisfaction, joy-of-giving, crowding-out, modesty, self-reported altruism

New reciprocity measure More variables on culture Variables on active citizenship

What's new in GINPS14 – households

Second sample New reciprocity measure Even more variables on culture More variables on relation with charities

What's new in GINPS14 – HNW

Deleted: Measures of giving attitudes, self-reported altruism, social pressure, awareness of need, contact with country of origin, voting, subjective health

Variables on active citizenship Better measure of zakat and sadaqah

What's new in GINPS14 – immigrants

Zakat = obligation to give

–49% of muslims pays via mosque–33% pays through other method–18% doesn't pay zakat

Sadaqah = voluntary charitable contribution

–44% of muslims gives via mosque–29% gives through other method–27% doesn't give sadaqah

Zakat and sadaqah

We're more than happy to share our data User manual at www.giving.nl E-mail us with any questions

GINPS Data Use

Social innovation of the Third Sector in Europe (ITSSOIN)

European Commission FP7 project 10 research partners across Europe,

coordinated by U of Heidelberg What Third Sector characteristics drive social

innovation? Theory, quantitative analyses, case studies www.itssoin.eu

ITSSOIN

That's some stuff we're working on!

Contact

• Arjen de Wit, a.de.wit@vu.nl• Center for Philanthropic Studies, VU University

Amsterdam, www.giving.nl