We preregistered hypotheses and a pre-analysis plan at https://osf.io/k74gm and https://osf.io/z82vc (See Supplementary Note SN1).
Majority of participants chose to make a donation
In both countries, a majority of survey respondents were willing to forego some or all of their bonus money to contribute to collective welfare. In the U.S., 63% of survey participants chose to donate at least some of their bonus to a charity; in Italy, 77% of participants made a donation. For those who chose to donate, the average donation amount in the U.S. was $2.75 (.55 of bonus fund) and in Italy €2.48 (.63 of bonus fund). Overall, 40% of the bonus money was donated to the charities.
Personal exposure, and not county-level exposure, predicted giving
We used a hurdle model to assess simultaneously the effects of COVID-19 exposure on both the probability of choosing to donate to a charity (P) and conditional donations (CD), that is, the amount donated conditional on being a donor (see Methods). The model included a set of demographic variables, the participant’s political orientation and area of residence, and the exogenously assigned prompt treatment (see Supplementary Information: Supplementary Table 1 for descriptive statistics of the variables). We used as our measure of environmental exposure to disease the county-level count of cases per 100,000 inhabitants in the county where the participant resided (see Methods). This environmental exposure measure proved to have no significant effect on either P (p=0.99) or CD (p=0.74) in the U.S.. In Italy, it was at the margin of statistical significance for P (p=0.098) and had no significant effect on CD (p=0.59) (see Table 1, columns 1-2 and 5-6).
We conjectured that the lack of significant effects may have been due to county-level data providing only a coarse, though the most disaggregated available, measure of exposure. It is plausible that only when an individual or their close acquaintances are personally afflicted by the disease does the perception of the threat of the disease become psychologically compelling. We therefore added to our pre-analysis plan a dummy variable of participants’ self-reported personal exposure. Participants were identified as “exposed” if they, their family members, or their acquaintances, had been diagnosed with, or had died from, COVID-19 (see Methods and Supplementary Table 1). This variable was positively and significantly associated with both P (p= 0.020 for U.S.; p=0.093 for Italy) and CD (p= 0.016 for U.S.; p=0.092 for Italy; see Table 1, columns 3-4 and 7-8 and Figure 1 for means). When the data for the two countries were combined into one analysis, the COVID-19 personal exposure effect was significant (p=0.005 for P, and p=0.002 for CD) and there was no significant difference between countries in the size of this effect (p=0.82 for P, and p=0.73 for CD) (see Supplementary Table 2c).
|
United States
|
Italy
|
|
Model 1
|
Model 2
|
Model 1
|
Model 2
|
DEP VAR
|
P
|
CD
|
P
|
CD
|
P
|
CD
|
P
|
CD
|
|
(1)
|
(2)
|
(3)
|
(4)
|
(5)
|
(6)
|
(7)
|
(8)
|
Age
|
0.003**
|
0.003**
|
0.003**
|
0.004**
|
0.002
|
0.001
|
0.002
|
0.001
|
|
[0.001]
|
[0.001]
|
[0.001]
|
[0.001]
|
[0.001]
|
[0.002]
|
[0.001]
|
[0.002]
|
Female
|
0.101***
|
0.104***
|
0.097***
|
0.100**
|
0.055*
|
0.049
|
0.053*
|
0.046
|
|
[0.023]
|
[0.031]
|
[0.023]
|
[0.031]
|
[0.026]
|
[0.031]
|
[0.026]
|
[0.031]
|
Conservative scale
|
-0.051***
|
-0.058***
|
-0.049***
|
-0.055***
|
-0.092***
|
-0.088***
|
-0.090***
|
-0.086***
|
|
[0.010]
|
[0.013]
|
[0.010]
|
[0.013]
|
[0.014]
|
[0.016]
|
[0.014]
|
[0.016]
|
Income
|
0.019***
|
0.015+
|
0.018**
|
0.013+
|
0.006
|
0.007
|
0.006
|
0.007
|
|
[0.006]
|
[0.008]
|
[0.006]
|
[0.008]
|
[0.007]
|
[0.008]
|
[0.007]
|
[0.008]
|
County-level COVID Exposure
|
0.001
|
0.00
|
0.001
|
0.00
|
0.001
|
0.003+
|
0.001
|
0.003
|
|
[0.002]
|
[0.003]
|
[0.002]
|
[0.003]
|
[0.002]
|
[0.002]
|
[0.002]
|
[0.002]
|
Personal COVID Exposure
|
|
|
0.058*
|
0.077*
|
|
|
0.045+
|
0.054+
|
|
|
|
[0.024]
|
[0.033]
|
|
|
[0.027]
|
[0.032]
|
LR chi2
|
88.66
|
48.62
|
95.04
|
53.96
|
83.81
|
41.23
|
87.07
|
44.05
|
Observations
|
932
|
932
|
932
|
932
|
723
|
723
|
723
|
723
|
Table 1 | Econometric analysis of probability of being a donor (P) and of conditional donation (CD). Estimates of marginal effects from two-part hurdle models are reported. The dependent variable is the share of bonus donated to a charity, without identifying which charity had been chosen. CD is the amount donated conditional on being a donor. The first column in each model reports the marginal effects from a Probit model to estimate P. The second column in each model reports the marginal effects for CD. In addition to the covariates reported above, all models also include controls for education level, indicator for size of respondent’s location, income, whether the individual reported an income loss because of COVID-19, priming (state/country/world), macro-region dummies (South/North for Italy and South/Midwest/Northeast/West for the U.S.), and an indicator identifying individuals who were either born abroad or whose parents were born abroad. The full regression output is reported in the Supplementary Table 2a. Variables are defined in Supplementary Table 1. Standard errors are in brackets. *** = p<0.001, ** = p<0.01, * = p<0.05, + = p<0.10
|
Figure 2 provides a graphic representation of the distribution of contribution decisions for personally exposed and non-exposed respondents. It is of interest to note that willingness to donate 100% of the bonus money was greater for the personally-exposed participants, and this was particularly the case in Italy. For respondents in the U.S., knowing someone who was diagnosed with the illness increased both the probability of deciding to donate by 9% and the average donation by 9.2% of the bonus. The marginal effects were similar in Italy, with personal exposure increasing the probability of donating by 7.5% and the amount donated by 5.8% of the bonus. Thus, a significant effect of personal exposure to COVID-19 was replicated across the two countries, affecting both the propensity to donate and the amount given.
Charities at the most local level attracted most donations
In both countries, the modal option for donations was to donate to the charity at the most local level – namely, the participant’s state of residence in the U.S. and region of residence in Italy. As shown in Figure 3, 41.0% did so in the U.S. and 32.9% in Italy. The national charity was more frequently selected in Italy (26.6% of the sample) than in the U.S. (13.0%), and the same pattern occurred for the international charity, which was selected by 17.4% of participants in Italy and 9.33% in the U.S.. We call ‘Aggregate Donations’ (AD henceforth) the overall amount of money allocated to each of the four options (i.e. self and the three charities). AD offers a comprehensive measure of the money allocated to each charity, as it combines both the extensive margin (which charity is chosen) and the intensive margin (how much money is donated conditional on choosing a certain charity).
In the U.S., 65.2% of the bonus money available was kept for oneself, 21.5% went to the state-level charity, while 7.5% and 5.8% of AD was allocated to the national and international charity, respectively. AD allocated to the state-level charity was significantly higher than both country-level AD (p<0.001) and world-level AD (p<0.001), and country-level AD was also significantly higher than world-level AD (p=0.025), in a repeated-measures Tobit model (see Supplementary Table 4) having the same covariates as the model used previously.
In Italy, 51.8% of bonus money was kept, while 18.6% went to the regional charity. 16.3% and 13.2% of Italian participants allocated their AD to national and international charities, respectively. AD were more evenly distributed in Italy than in the U.S., as AD to the regional charity were not significantly different, at conventional levels, than AD to national charities (p=0.080), but AD to the world charity were significantly lower than AD to the regional charity (p<0.001) and to the national charity (p=0.005). AD allocated to state-level charities in the U.S. were significantly higher than AD allocated to regional charities in Italy (p=0.018) in a pooled repeated-observation Tobit model (Supplementary Table 4), and Italian participants donated significantly more to national charities (p<0.001) and to international charities (p<0.001) than U.S. participants.
People choosing the world charity donated significantly more than those choosing other charities
We analyzed CD – the intensive margin of donations – with respect to the distribution of allocations to the three different charities. CD to the world charity were the highest among the three in both countries, followed by CD to the national charity, and CD to the state/regional charity (Fig. 4). CD to the international charity were significantly higher than CD to the state charity in the U.S. (p=0.007), and significantly higher than either CD to regional charities (p<0.001) or national charities (p<0.001) in Italy (Supplementary Table 2b). In other words, participants who selected the world charity gave more than participants selecting the state or regional charities. Therefore, the finding that AD was highest for the state/regional charity must have been driven by the extensive margin of donation rather than the intensive margin.
Prompting had limited effects on where donations were directed
As described in the Methods section, each participant was randomly assigned to a different framing condition aiming to prompt individuals to portray COVID-19 as a problem for (a) the state of residence (in the U.S.) or region of residence (in Italy) (Local Prompt henceforth), (b) the country (National Prompt), or (c) the world (World Prompt). In the Control condition, no geographical connotation was provided.
After ascertaining the exogeneity of the prompt to the main demographic characteristics of the samples (Supplementary Table 6), we used a multivariate Tobit model to analyze the effect of the three prompts on aggregate donations at the local, national or world level. This model enables us to capture the interdependent nature of the charity choice for donation (see Methods). We found that none of the prompts increased donations significantly in the U.S. in comparison to the Control condition. This was the case for each of the three levels of donation, using the same covariates as in our previous models (Table 2, columns 1-3). In Italy, the World Prompt consistently had a significant effect in increasing donations to the world charity (p=0.027) while also having a negative effect on national donations (p=0.022). The National Prompt had no effect (p=0.44), while the Local Prompt was at the margins of significance at conventional levels in increasing contributions to the local charity (p=0.073; Table 2, columns 7-9).
Overall, then, the prompt manipulation proved to have little influence on donation decisions and apparently was not powerful enough to override participants’ prior perspective on the scope of the pandemic crisis. Nor did it affect the predicted mediator of social identification at the different levels (see Supplementary Table 7, and Supplementary Note SN2).
Social identity strongly affected donation choice
In experimental research on social dilemmas, the strength of social identification with an ingroup increases intragroup cooperation39,40. Social identity has been found to be a relevant factor to explain cooperation in a nested social dilemma game, particularly at the global level37. As laid out in our pre-registration plan, we conjectured that the same would be the case for the other levels of choice. Thus, further analyses were conducted to look at the effects of social identification itself, independent of the prompts.
Figure 5 displays the relationship between strength of social identity at each level and aggregate donations at the corresponding level for U.S. and Italy. We employed a Tobit multivariate model to predict AD from social identity, using the same set of covariates used in previous models. Our hypotheses were confirmed in that social identity at each level was a significant predictor of donation at that level. This was the case both in the U.S. and Italy for local (p<0.001 in both countries), national (p=0.021 in the U.S., p<0.001 in Italy) and global identity (p=0.001 in the U.S., p<0.001 in Italy; Table 2, columns 4-6 and 10-12). Moreover, we found that there were no significant differences in the effects of social identity in the two countries in a pooled model (Supplementary Table 5b), corroborating the robustness of this result. Similar results were obtained analyzing the effect of social identity on the probability of choosing one of the three charities (Supplementary Tables 5c-d). The effect of social identity was also robust to the inclusion of additional possible explanatory factors, such as trust in other people (see Supplementary Tables 5a-b and Supplementary Note SN3).
Donations were motivated by concern for others’ needs and charity efficiency
While social identity offers a general explanation for altruistic behavior that could span several contexts, the choices made by our participants may have been influenced by more specific factors connected with their perceptions about charities at each level. First, a participant may have been motivated to give to a charity expecting to be on the receiving end from that charity’s activity in the future. This may explain the larger share of overall giving to regional charities. In other words, people may expect that their Per Capita Return – i.e. the level of personal benefit from donations – would be higher for the regional charities than the national and the global charity. Several laboratory experiments confirm that individuals are indeed sensitive to the Per Capita Return when giving to a public good41 – even when the choice of giving runs against their self-interest, as in our experiment. Alternatively, according to generalized bounded reciprocity theory, people are motivated to cooperate by the expectation that other people within the group will also cooperate42. In other words, it is the willingness to comply with a commonly shared social norm of cooperation, thus reciprocating others’ altruistic behavior, that induces people to cooperate. If this motivation were active in our experiment, we would then expect people to donate at the level where they most expect others to donate. Other possible accounts concern the perceived capacity of a certain charity to achieve its goals, and its efficiency in meeting goals without wasting money43. Finally, people may be motivated by a purely altruistic desire to help people most in need because of the effects of COVID-19. Perceived need has been found to be a strong motivator of pro-social behavior44.
|
United States
|
Italy
|
|
Model 1
|
Model 2
|
Model 1
|
Model 2
|
DEP VAR: AD
|
State
|
Country
|
World
|
State
|
Country
|
World
|
Region
|
Country
|
World
|
Region
|
Country
|
World
|
|
(1)
|
(2)
|
(3)
|
(4)
|
(5)
|
(6)
|
(7)
|
(8)
|
(9)
|
(10)
|
(11)
|
(12)
|
Age
|
0.009***
|
-0.011*
|
0.00
|
0.008***
|
-0.011*
|
0.002
|
0.009**
|
-0.011**
|
0.002
|
0.007*
|
-0.010*
|
0.005
|
|
[0.002]
|
[0.005]
|
[0.006]
|
[0.002]
|
[0.005]
|
[0.006]
|
[0.003]
|
[0.004]
|
[0.006]
|
[0.003]
|
[0.004]
|
[0.006]
|
Female
|
0.131**
|
0.124
|
0.086
|
0.138**
|
0.117
|
0.07
|
0.144*
|
0.06
|
-0.127
|
0.165*
|
0.043
|
-0.142
|
|
[0.049]
|
[0.108]
|
[0.135]
|
[0.048]
|
[0.107]
|
[0.133]
|
[0.067]
|
[0.081]
|
[0.123]
|
[0.067]
|
[0.081]
|
[0.121]
|
Conservative scale
|
-0.032
|
-0.095*
|
-0.183**
|
-0.045+
|
-0.121*
|
-0.126+
|
0.084*
|
-0.051
|
-0.627***
|
0.044
|
-0.062
|
-0.511***
|
|
[0.021]
|
[0.047]
|
[0.063]
|
[0.023]
|
[0.054]
|
[0.069]
|
[0.036]
|
[0.046]
|
[0.088]
|
[0.037]
|
[0.048]
|
[0.084]
|
Priming State/Region
|
-0.022
|
-0.071
|
-0.134
|
-0.033
|
-0.078
|
-0.113
|
0.169+
|
-0.051
|
-0.069
|
0.198*
|
-0.098
|
-0.099
|
|
[0.068]
|
[0.151]
|
[0.196]
|
[0.067]
|
[0.150]
|
[0.195]
|
[0.094]
|
[0.110]
|
[0.179]
|
[0.092]
|
[0.109]
|
[0.174]
|
Priming Country
|
-0.091
|
-0.048
|
-0.083
|
-0.09
|
-0.064
|
-0.045
|
0.118
|
-0.087
|
0.084
|
0.128
|
-0.096
|
0.039
|
|
[0.069]
|
[0.149]
|
[0.194]
|
[0.067]
|
[0.148]
|
[0.192]
|
[0.096]
|
[0.113]
|
[0.176]
|
[0.094]
|
[0.111]
|
[0.171]
|
Priming World
|
-0.06
|
-0.121
|
0.228
|
-0.067
|
-0.12
|
0.256
|
0.125
|
-0.268*
|
0.372*
|
0.133
|
-0.287*
|
0.331*
|
|
[0.067]
|
[0.147]
|
[0.180]
|
[0.066]
|
[0.146]
|
[0.179]
|
[0.094]
|
[0.117]
|
[0.168]
|
[0.092]
|
[0.115]
|
[0.163]
|
Local Social Identity
|
|
|
|
0.214***
|
-0.208**
|
-0.270**
|
|
|
|
0.298***
|
-0.271***
|
-0.132
|
|
|
|
|
[0.035]
|
[0.080]
|
[0.102]
|
|
|
|
[0.055]
|
[0.069]
|
[0.100]
|
National Social Identity
|
|
|
-0.021
|
0.202*
|
-0.037
|
|
|
|
-0.119*
|
0.315***
|
-0.218*
|
|
|
|
|
[0.038]
|
[0.088]
|
[0.108]
|
|
|
|
[0.055]
|
[0.071]
|
[0.101]
|
Global Social Identity
|
|
|
|
-0.053
|
0.093
|
0.292**
|
|
|
|
-0.095*
|
-0.049
|
0.489***
|
|
|
|
|
[0.033]
|
[0.069]
|
[0.089]
|
|
|
|
[0.048]
|
[0.060]
|
[0.102]
|
LR chi2
|
100.34
|
174.58
|
178.66
|
270.91
|
Observations
|
932
|
932
|
723
|
723
|
Table 2 | Econometric analysis of Aggregate Donations (AD). We fit multivariate Tobit models to estimate AD for each of the three charities. AD is the overall amount of donations to each charity, combining both the extensive margin (which charity is chosen) and the intensive margin (conditional donations to each charity). In addition to the covariates reported above, all models also include controls for education level, indicator for respondent’s location size, income, whether the individual reported an income loss because of COVID-19, macro-region dummies (South/North for Italy and South/Midwest/Northeast/West for the U.S.), an indicator identifying individuals who were either born abroad or whose parents were born abroad, and county-level and personal exposure to COVID-19. The full regression output is reported in the Supplementary Table 5a. Variables are defined in Supplementary Table 1. Standard errors are in brackets. *** p<0.001, ** p<0.01, * p<0.05, + p<0.10
|
Preliminary data relevant to these questions about motives for donation were obtained from analyses of responses to an open-ended question at the end of the survey questionnaire in the U.S. survey. The question asked participants to give a short answer about why they made the decision to donate or not. No responses provided by participants made explicit reference to expectations that any of the charities would benefit themselves. Among those who chose to donate, 56% mentioned others’ need or wanting to help others as their reason for giving. In addition, a small percentage (4%) mentioned perceived effectiveness as their reason for choosing a particular charity and most of these referred to the state level.
To pursue this more systematically, the Italian survey included a set of structured questions regarding specific characteristics of charities at the regional, country, or world level that may have affected giving behavior. (This analysis was not part of our pre-analysis plan, so it should be considered as supplementary to our hypothesis-testing results). We had one item for each of the possible factors mentioned above: perception of (a) Per Capita Return, (b) bounded generalized reciprocity, (c) charity’s effectiveness and (d) efficiency, and (e) awareness of need (see Supplementary Table 1 for item wording and Supplementary Note SN4 for details on the analysis).
We applied the same multivariate Tobit model used in the previous section to explain AD, adding the five items jointly to the regression (see Supplementary Table 8). We did not find support for the idea that donating to a certain charity would have benefitted the individual, at any level of donation (p=0.37 for the regional charity; p=0.39 for the national charity; p=0.78 for the international charity). Likewise, the hypothesis that people were motivated by donating at the same level as others did not receive support for any level of donation (p=0.76 for the regional level, p=0.053 for the national level - with an opposite sign to the one expected-, p=0.66 for the world level). Support was found for the other three factors. The one having the highest weight was the perception of a charity’s effectiveness in pursuing its goal of providing relief from COVID-19: participants who rated a specific level of charity as most effective gave significantly more at the corresponding level (p=0.008 for regional level, p<0.001 for other levels). A somewhat weaker relationship was observed for the perception of the charity efficiency, with efficiency ratings significantly predicting donations at the regional level (p=0.001), and country level (p=0.041), but not at the world level (p=0.29). Finally, the perception of people’s needs also acted as a strong determinant of donations at the respective levels (p=0.001 for regional aid, p=0.31 for national aid; p<0.001 for international aid). Overall, it seems that participants had truly altruistic concerns in benefitting those charities better capable of providing relief and helping those in greater need, while assessments of which charity may benefit themselves in the future had a limited role.
Liberals and females donated significantly more than conservatives and men
Our pre-registration plan provided for analysis of political ideology and some demographic characteristics as potential determinants of donation choices. As is reported in Tables 1 and 2, we found that liberals were significantly more likely to donate in general, and particularly to world charities, than conservatives. This was the case in both countries, with the effect of conservatism being stronger in Italy than the U.S., after controlling for other factors (Supplementary Table 2c, Supplementary Table 5b). This result supports other literature indicating that political leaning is a strong factor affecting pro-sociality and social attitudes45. We also found that women were more generous than men in both countries. When looking at donation amount there was an overall effect of age with older people giving more on average. However, the direction of the effect of age varied across the different charities (Table 2). We found no effects for education and only sporadic effects for income in the U.S. but not in Italy, with richer people tending to give more to the state level (see Supplementary Notes SN3 for a more detailed analysis).
It should be noted that all of these demographic variables were included as control variables in our primary analytic models. Our pre-analysis plan included the analyses of other variables as possible factors of the choice to donate – specifically, psychological, social and economic vulnerability, and trust in other people. These variables generally had no significant effects, with the only exception of trust in others (Supplementary Tables 5a-b and Supplementary Note SN3).
All these demographic and attitudinal variables had essentially similar effects when analyzing the probability of choosing a certain charity, rather than AD to that charity (Supplementary Tables 5c-d and Supplementary Note SN3). We also examined the possibility of experimenter demand effects associated with our framing manipulation but found little evidence that participants guessed experimenter intent or that such guesses influenced their choice of charity (Supplementary Note SN5).