1. Social anchoring and hypothetical bias in stated WTP
Study
Social anchoring and hypothetical bias in stated WTP
Mark Koetse1, Jetske Bouma2 1 Institute for Environmental Studies (IVM), VU University Amsterdam 2 PBL Netherlands Environmental Assessment Agency, Den Haag
23rd EAERE conference, Athens, 29 June – 1 July 2017

2. CV experiment
One-time donation to a fund that stimulates a transition from intensive to extensive farming, using a payment card
Description of the two types of farming (increased water levels an no use of pesticides for non- intensive farming)
Description of biodiversity benefits of non-intensive farming

3. CV experiment Description of landscape changes Intensive farming
Non-intensive farming

4. Treatments
T1 (control): hypothetical donations, no feedback on expectations
T2 (social anchoring): hypothetical donations, feedback on expectations 1
T3 (social anchoring): hypothetical donations, feedback on expectations 2
T4 (hypothetical bias): hypothetical donations, cheap talk script
T5 (hypothetical bias): real donations

5. Data collection
Respondents from TNS-NIPO online panel (200,000 households)
Independent identical sampling procedure for each treatment
Response rate around 45% for hypothetical treatments, around 18% for actual donation treatment
400+ useable respondents for each treatment

6. Social anchoring in donating to a public environmental good Mark Koetse1, Jetske Bouma2, Dominic Hauck1 1 Institute for Environmental Studies (IVM), VU University Amsterdam 2 PBL Netherlands Environmental Assessment Agency
23rd EAERE conference, Athens, 29 June – 1 July 2017

7. Problem
Contingent valuation and choice experiments often used for valuation of public environmental goods
Donations often used as the payment vehicle
Possible problems with using donations:
Free riding and inequality aversion (Kotchen, 2015)
Dependence on other people’s behaviour does not correspond to theoretical concept of WTP as individual assessment of value
Evidence of anchoring: e.g. Carlsson et al. (2010); Shang and Croson (2009)

8. Theoretical background: Free riding vs. inequality aversion
We argue that many different types of behaviour, such as conditional cooperation and reciprocity, can be modelled as inequality aversion. Inequality aversion model by Fehr & Schmidt (1999) as starting point:
Inequality aversion (in original version):
- People dislike having more/less than others
- Dislike having less > dislike having more

9. Theoretical background: Free riding vs. inequality aversion
Donations to public good, two possible effects of other people’s behaviour:
When incentive to free ride increases with total donations, there are opposing effects of other people’s donations on one’s own donation:
Increased donations by others imply more free riding and a lower donation.
Increased donations by others imply a higher donation through inequality aversion.

10. Theoretical background: Free riding vs. inequality aversion
Donations of others generally unknown
Expectations used as substitute
Although entire distribution may be relevant, we test the impact of expectations on the mean:

11. Hypotheses
Inequality aversion dominates free riding (based on experimental literature) H1: Increase in expectations about donations of others increases donations H2: Donations increase when expectations were too low, decrease when expectations were too high Dislike donating more > dislike donating les H3: Adjustments in donations are larger for over- than for under- estimations

12. Research design
Measuring people’s expectations (before donation question, all treatments):
Prior to this study we asked a large amount of households how much they would be willing to donate once to the fund discussed before. We would like to know your assessment of the results of this study.
How many percent of the households do you think were willing to contribute to the fund?
……… %
What do you think was the average donation in Euro of households that were willing to donate to the fund?
……… Euro

13. Research design Three treatments
T1 (control): hypothetical donations, no feedback on expectations
T2 (social anchoring): hypothetical donations, feedback on share of donating households
T3 (social anchoring): hypothetical donations, feedback on share of donating households AND average donation of donating household

14. Research design Feedback text:
The actual study results were as follows:
Percentage of donating households: 25%
This means your percentage assessment was 5% LOWER/HIGHER than the actual percentage.
Average donation of a donating household: 15 Euro
This means your donation assessment was 5 Euro HIGHER than the actual donation.

15. Results: treatments effects on donations
Mean shares of donating households
Split sample: people with expectations below and above the true share (25%)
Treatment
Full sample
t1: control
0.529 t2
0.548 t3
0.527
Expectations: Share of donating households a
< 25%
>= 25%
0.425
0.656
0.451
0.657
0.434
0.649

16. Results: treatments effects on donations
Full sample
Mean donation
Mean donation DH
t1: control
€ 10.0
(€ 17.0)
€ 19.0
(€ 19.5) t2
€ 11.0
(€ 27.1)
€ 20.0
(€ 34.1) t3
€ 10.8
(€ 21.3)
€ 20.6
(€ 25.8)
Expectations< €15
Mean donation
Mean donation DH
€ 4.38
(€ 9.06)
€ 9.35
(€ 11.4)
€ 4.65
(€ 7.83)
€ 9.52
(€ 8.91)
€ 3.77
(€ 5.96)
€ 8.75
(€ 6.23)
Expectations >= €15
Mean donation
Mean donation DH
€ 17.9
(€ 21.8)
€ 29.2
(€ 21.1)
€ 18.8
(€ 38.3)
€ 30.2
(€ 44.9)
€ 19.7
(€ 29.0)
€ 30.4
(€ 31.2)
Slight decrease for people with low expectations
Substantial increase for people with high expectations
Slight increase overall

17. Results: Expectations and the decision to donate
Logit model: donation vs. no donation
Sample: Expectations < 25%
Sample: Expectations >=25% t1 t2 t3
Constant
-1.48***
-0.762***
-0.769***
-0.650
-0.494
-0.868*
Expectations: share of donating households
0.101***
0.050***
0.046**
0.033***
0.030***
0.038***
NOBS
266
246
251
218
219
191
Adjusted pseudo R2
0.073
0.021
0.016
0.030
0.026
0.043
Increased expectations on share of donating households increases the probability of donating
Feedback on actual share of donating households substantially changes the relationship for people with too low expectations

18. Results: Expectations and the decision to donate
More free- riding
More Matching

19. Results
Poe et al. (2005) combinatorial test on differences between Logit slope coefficients
Also for model with covariates for robustness (income, education, age, gender, household size, political preference, distance decay, expectations average donation)
 Logit
Expectations < realisation
t1 > t2
t1 > t3
t2 > t3
Model without covariates
0.035**
0.025**
0.444
Model with covariates
0.020**
0.478
Expectations >= realisation
t1 > t2
t1 > t3
t2 > t3
0.431
0.398
0.339
0.466
0.317
0.347

20. Results: Expectations and donations
OLS: Donation amount (excluding Donation = 0)
Sample: Expectations < €15
Sample: Expectations >= €15 t1 t2 t3
Constant
-0.433
1.19
3.32**
15.8***
14.7**
11.5**
Expectation: donation of donating household
1.28***
1.12***
0.716***
0.337***
0.360**
0.463***
NOBS
132
126
106
124
129
127
Adjusted R2
0.111
0.144
0.113
0.164
0.038
0.140
Increased expectations on average donations increases the Donation amount
Feedback on actual share of donating households substantially changes the relationship

21. Results: Expectations and donations
More free- riding
More Matching

22. Results: Robustness
Poe et al. (2005) combinatorial test on differences between OLS slope coefficients
 OLS
Expectations < realisation
t3 < t1
t3 < t2
t2 < t1
Model without covariates
0.054*
0.089*
0.331
Model with covariates
0.039**
0.132
0.209
Expectations > realisation
t3 < t1
t3 < t2
t2 < t1
0.149
0.283
0.436
0.096*
0.131
0.390

23. Conclusions
H1: Increased expectations about donations of others increases donations: inequality aversion dominates free riding
Our results confirm this to a large extent: expectations increase donations
Donation amount
Tendency to match stronger for people with low expectations
More free riding in sample of people with high expectations

24. Conclusions
H2: Donations increase when expectations were too low, donations decrease when expectations were too high
Treatments averages suggest that results are exactly opposite to our hypothesis
Donation decision
Expectations too low: Adjustment towards matching instead of overmatching
Donation amount
Expectations too low: No clear interpretation in terms of FR vs. IA
Expectations too high: Adjustment towards matching

25. Conclusions
H3: Adjustments in donations are larger for over- than for under- estimations (dislike donating more > dislike donating less)
Some evidence of opposite, i.e., larger adjustments for underestimations, especially for decision to donate

26. Discussion
Valuation
Using donations in contingent valuation is problematic because of the influence of expectations on other people’s behaviour
Further research: How problematic is this?

27. Discussion
Financing
Interesting findings for financing nature, e.g. through crowdfunding: donations may be affected by changing expectations on what other people donate/invest, and by providing information on this issue
Further research: Our study is on expectations on averages for the entire population: people may anchor to different groups in society (Chen & Li, 2009) and to different moments of the distribution (Shang & Croson, 2009)

28. References
Carlsson F, JH García, A Löfgren, 2010, Conformity and the Demand for Environmental Goods, Environmental and Resource Economics 47, 407–421.
Chen Y, SX Li, 2009, Group Identity and Social Preferences, American Economic Review 99, 431– 457.
Fehr E, KM Schmidt, 1999, A Theory of Fairness, Competition, and Cooperation, Quarterly journal of Economics 114, 817–868.
Kotchen MJ, 2015, Reconsidering Donations for Nonmarket Valuation, Environmental and Resource Economics 62, 481–490.
Shang J, R Croson, 2009, A Field Experiment in Charitable Contribution: The Impact of Social Information on the Voluntary Provision of Public Goods, The Economic Journal 119, 1422–1439.