Likelihood of donating positive amounts

We start by analyzing the likelihood of giving any positive amount (see Table 3). Participants assigned to the impact group exhibited a higher donation rate (54%) than respondents in the seal (49%) and control (46%) groups. There is a remarkably consistent pattern across cities (averaging over all treatments), with an average of 50% of our respondents donating a positive amount. Among respondents assigned to the seal group, only those in Marburg had a relatively high giving rate (61%); however, the sample size was very small (n = 11).

Table 3: Likelihood of positive donations by city and treatment (completed surveys only)

Total Innsbruck Mannheim Marburg

Control 46% 46% 49% 39%

Impact 54% 55% 52% 56%

Seal 49% 46% 50% 61%

Total 50% 50% 50% 52%

As a next step, we use a binary probit model (Table 4) to estimate whether these differences are significant and whether some of our survey items influenced the likelihood of donating positive amounts. Our results suggest that the impact treatment has a positive effect on the likelihood of donation. Participants assigned to the impact treatment were 8% more likely (significant at the 10% level) to donate a positive monetary amount compared to the control group. When controlling for additional factors, this increases to 10% (significant at the 5%

level).

We further find that women were on average 11% more likely to donate than male respondents (significant at the 5% level), consistent with the findings by Eckel and Grossmann (1998). In addition, individuals who regularly visit a house of worship were 11%

more likely to give a positive monetary amount compared to people who never attend religious services (significant at the 5% level). Both these results corroborate previous

31

findings obtained in the long-term representative study conducted by Neumayr and Schober (2009) in Austria^g Right-wing voters were 17% less likely (significant at the 10% level) and centrist voters were 12% less likely (significant at the 5% level) to donate a positive amount than supporters of left-wing parties. Singles were on average 9% more likely (significant at the 5% level) to contribute than people in a partnership, which is the only result that does not confirm previous study findings. Finally, respondents interested in the topic of development cooperation were on average 19% more likely (significant at the 1% level) to donate than non-interested participants.

32

Table 4: Marginal effects after binary probit models for the likelihood of donating positive amounts

(1) (2) (3) (4)

Variables Positive donation Positive donation Positive donation Positive donation

Impact 0.0823* 0.0828* 0.102** 0.102**

33 4.2 Donation amounts

We now turn to the analysis of donation amounts. Table 5 shows average donation levels differentiated by treatments and city. Donations were highest for participants in the impact group (17 Euros), followed by the control group (13 Euros) and the seal treatment (10 Euros).

Individuals assigned to the impact treatment also gave the highest amount within each of the three different samples. When we examine effects by location, donation amounts were highest among students in Innsbruck (16 Euros), followed by Marburg (14 Euros) and Mannheim (9 Euros). Although giving in Mannheim was lower overall, we still find that the impact treatment gave on average 2 Euros (20%) more than the other two treatments. Thus, the Mannheim students in the impact treatment were more likely to donate but did not give as much as students in the other cities.^h

Table 5: Mean giving amounts in Euro by city and treatment (completed surveys only)

Total Innsbruck Mannheim Marburg

Control 13 (n=187) 15 (n=126) 9 (n= 43) 8 (n=18)

Impact 17 (n=225) 19 (n=151) 11 (n=58) 18 (n=16)

Seal 10 (n=166) 10 (n= 76) 8 (n=72) 17 (n=18)

Total (3 Treatments) 14 (n=578) 16 (n=353) 9 (n=173) 14 (n=52)

In Table 6, we estimate similar models, as in the case of the likelihood of donating positive amounts. We find that willingness to donate in the impact treatment was between 7 Euros and 8.50 Euros higher than in the control group. Given an average donation of 13 Euros in the control group, this effect size is very large (between 50% and 65% higher). Using a standard ex-post robustness test in CVM studies, we additionally test a regression (column 4 in Table 6) in which we exclude potential outliers who were willing to donate more than 100 Euro per year (i.e. eight observations from Impact, six from Control and one from Seal). Our estimation suggests that the Impact treatment remains strongly positive significant compared to the other two treatments.

In line with our descriptive results, donated amounts decreased on average by 7 Euros for participants in Mannheim compared to those in Innsbruck (significant at the 5% level).

However, when controlling for socio-demographic characteristics, this geographic effect becomes insignificant, indicating that it arises from the specific demographic composition of

34

survey participants in Mannheim. Mannheim is an elite university in Germany with a lower share of leftist voters and apparently less interest in development issues. Similar to the results of our probit analysis, we find that female participants donated on average 8 Euros more than male respondents (significant at the 5% level), and that people who regularly attend a house of worship donated on average 11 Euros more than survey participants who never do so (significant at the 1% level). Political orientation has a significant influence on donation levels as well: Centrist voters and right-wing voters gave on average 12 Euros (centrist voters, significant at the 1% level) and 20 Euros (right-wing voters, significant at the 5% level) less than respondents who supported left-wing parties. More surprisingly, singles contributed on average 8 Euros more than respondents in a partnership (significant at the 5% level). In accordance with expectations, interested individuals donated on average 13 Euros more than uninterested participants (significant at the 1% level). All these findings confirm the results found for donation likelihood (Table 4).

35

Table 6: Tobit regression: Treatment analysis including covariates and outlier control

(1) (2) (3) (4) variable WTD is the stated donation amount in Euro.

36 4.3 Answers to selected attitudinal items

After the donation request, we asked respondents several attitudinal questions that may shed some light on the mechanisms underlying the higher donation amount in the impact group.

Our focus here was on campaign comparisons, the effectiveness of aid in light of corrupt politicians, the demand for progress feedback, and the importance of pro-poor targeting, which had the most significant results (the other variables are reported in the supplementary material). Out of 578 respondents, only 11.76% regarded the “Info-Campaign” as the most cost-effective, and 15.74% viewed it as the least cost-effective out of the six alternatives. This highlights the need for providing information on proven effectiveness in the fundraising request. Participants ranked “measures to prevent teacher absenteeism” first (54%, n = 313) in terms of cost-effectiveness, followed by the “Info-Campaign” (15%, n = 85), “financial support for the family” (14%, n = 82), “supporting health measures for schoolchildren” (7%, n = 42), “supporting training courses for school teachers” (5%, n = 27), “building schools”

(3%, n = 17), and “financing of school fees” (2%, n = 12).

Surprisingly, even among respondents assigned to the impact group, only 11.56%

regarded the “Info-Campaign” as the most cost-effective, with 20.44% of participants perceiving it to be the least cost-effective. We might have expected the information revealed in our treatment to affect this ranking. However, we observe that participants in the impact treatment donated relatively high amounts independent of whether they regarded the “Info-Campaign” as the best (18 Euros) or worst (€14) aid measure among the six programs (see Figure 1). Thus, even sceptics were convinced by the aid appeal for the information campaign when it was combined with the RCT. Participants who ranked the information campaign the least cost-effective but were assigned to the seal treatment or the control group donated much less (€3 or €5, respectively). Thus, the donations of those who were not confronted with the impact treatment essentially correspond to their evaluation of aid effectiveness.

Across all treatments, the majority of respondents (81%) stated that corrupt governments make aid ineffective (OP2). People who thought that corruption makes aid completely ineffective donated less on average. Again, respondents assigned to the impact treatment did not adjust their donation levels according to the beliefs they held about the corruption of politicians: The average donation was 17 Euros for those believing that corruption makes aid ineffective and 16 Euros for those with the opposite opinion. The overall willingness to donate may be higher for participants in the impact group because the RCT signals that funds are being channelled through a transparent program, making the relative

37

fund waste less likely to affect giving behavior. Again, the donations of participants not in the impact treatment largely correspond to their evaluation of aid effectiveness.

The effects in the impact treatment were driven by two other attitudes in particular:

Demand for progress feedback, and the importance of pro-poor targeting. The majority (71%) of our 578 respondents were willing to accept that 5% of their donations would be used to provide feedback on the progress of the aid organization (Progress Feedback). People who favored feedback and were assigned to the impact or seal treatments also donated relatively higher amounts than those who did not want their donations to be used to provide feedback.

Respondents assigned to the control group (who were not made aware of the importance of information or accountability) did not donate differently based on whether they thought feedback was important. A similar pattern is found for the item Targeting. The regression results for the subsample of respondents in the impact treatment (see Table A.3 in the supplementary online appendix) indicate that these two variables are major drivers of high donation levels within this treatment and, to a lesser degree, also in the seal treatment. Thus, it seems that the impact treatment especially increased donations for people who highly value the accountability of aid agencies.

38 Figure 1: Donation levels and attitudinal questions

Notes: Upper-left panel shows results for Campaign Ranking; upper-right panel shows OP2;

bottom-left panel shows Progress Feedback; bottom-right panel shows Targeting.