Look who's crowding-out!

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, r.bekkers@vu.nl and Arjen de Wit, a.de.wit@vu.nl

• ‘Giving in the Netherlands’, Center for Philanthropic Studies, Faculty of Social Sciences, VU University Amsterdam, www.giving.nl