look who's crowding-out!
DESCRIPTION
Presentation with René Bekkers at 42nd ARNOVA Annual Conference, Hartford, CT. November 21, 2013TRANSCRIPT
Arjen de WitRené Bekkers
ARNOVA 42nd Annual ConferenceHartford, CT
November 21, 2013
Look who's crowding-out!
Crowding-out
Lower government contributions, higher private donations
Previous studies are not conclusive Estimated effects of a change in government
contributions vary strongly between studies
Two questions
1. Why do previous studies find different results?
2. How do individuals differ in their response to changes in government contributions?
Our first question
1. Why do previous studies find different results?
2. How do individuals differ in their response to changes in government contributions?
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 2007 We include only original empirical quantitative
results N = 218 estimates from 34 articles
Our meta-analysis sample
Our meta-analysis sample
Books
Our meta-analysis sample
DissertationsBooks
Our meta-analysis sample
DissertationsTheses
Books
Our meta-analysis sample
Dissertations
Not in Web of Science
Theses
Books
Our meta-analysis sample
Dissertations
Not in Web of Science
Not accepted
Theses
Books
Our meta-analysis sample
Dissertations
Not in Web of Science
Not accepted
Theses
Books
Not submitted
Our meta-analysis sample
Dissertations
Not in Web of Science
Not accepted
Theses
Books
Not submitted
Non-English
Crowding-out estimates
Mean crowding-out effect
Excl. outliers
All
-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1
Findings
Analyses of tax records and lab experiments produce more crowding out than surveys and field experiments.
Analyses of organizational level data produce more crowding out than individual level data.
Studies from Europe find the weaker estimates of crowding out than US studies.
Units of analysis
Multilevel random-effects regression on COE estimates (excl. outliers)
Units of analysisIndividuals (ref.)Organizations -0,18 (0,20)
(Constant) -0,27 (0,10) -0,20 (0,13)
Between-study SD 0,42 0,43Rho 0,72 0,73Studies 21 21Observations 85 85
Type of government contribution
Multilevel random-effects regression on COE estimates (excl. outliers)
Type of govt contributionSubsidies to orgs (ref.)Expenditures 0,34 (0.17) *Rebate 0,87 (0,21) **Match 0,47 (0,16) **Taxing respondents - 0,12 (0,17)
(Constant) -0,47 (0,11) **
Between-study SD 0,25Rho 0,49Studies 21Observations 85
Awareness
Multilevel random-effects regression on COE estimates (excl. outliers)
Rs aware of govt contributionsNo (ref.)Yes 0,18 (0,20)
Rs aware of need donated toNo (ref.)Yes 0,14 (0,20)
(Constant) -0,37 (0,16) * -0.33 (0,14) *
Between-study SD 0,43 0,43Rho 0,73 0,73Studies 21 21Observations 85 85
Discussion
Random sample? Should tax and price elasticities be included? Are we comparing apples and oranges? ‘Bad studies’ in the sample?
Our second question
1. Why do previous studies find different results?
2. How do individuals differ in their response to changes in government contributions?
The Civic Voluntarism Model
Resources Change in contribution
Engagement Recruitment
The scenario experiment
• In the Giving in the Netherlands Panel Survey 2012 we included a scenario experiment.
• 1,448 participants evaluated 3 scenarios, constructed randomly by combining information on budget cut levels and sectors.
• Participants were reminded of their households’ contribution in the past year.
Example of scenario
“With your household you donated €100 to health in the past year. If the government cuts 5% in this area, how would you react?”
Response categories:• I will give the same as last year• I am willing to give more• I will also give less
[if more/less] What will be the new amount?
How the Dutch respond to cutbacks
Average response across all 4,344 scenarios
Responses vary by sector
Support for the civic voluntarism model
Odds ratios from logistic regression of willingness to contribute more after government cutback in at least one scenario (GINPS12, n=1,478; including controls for gender, age,
income from wealth, home ownership, number of donation areas)
Values, reputation and efficacy
Odds ratios from logistic regression of willingness to contribute more after government cutback in at least one scenario (GINPS12, n=1,478)
Conclusions of meta-analysis
• On average, a $1 reduction in government support is associated with a $0.28 increase in private contributions.
• However, crowding-out estimates vary considerably from study to study.
• Differences in the methodology used to measure the influence of government contributions on private giving are driving these differences.
Conclusions of scenario experiment
• Individuals also vary systematically in their responses to changes in government contributions.
• Those with more resources, receiving more solicitations and more generous donors are more likely to contribute more after government cutbacks.
• The principle of care, reputation and charitable confidence are key mechanisms in crowding-out.
• The principle of care is the only characteristic predicting the level of crowding-out.
Contact details
• René Bekkers, [email protected] and Arjen de Wit, [email protected]
• ‘Giving in the Netherlands’, Center for Philanthropic Studies, Faculty of Social Sciences, VU University Amsterdam, www.giving.nl