DOI: 10.5061/dryad.cnp5hqc4x
title: Exposure to the Covid-19 pandemic and generosity in Spain

description: We report data from an online experiment, which allow us to study how
 generosity has changed during the initial explosive growth of the Covid-19
 pandemic in Spain. We have gathered data from over a six-day period in
 which Covid-19-associated deaths in Spain, one of the most affected
 countries in the corresponding period, increased fourfold. In our
 experiment, participants could donate a fraction of a €100 prize to a
 charity. Our data are particularly rich in the age distribution and we
 complement them with daily public information about the Covid-19-related
 deaths, infections, and hospital admissions. We find correlational
 evidence that donations decreased in the period under study and scale down
 with the public information about the life and health impact of the
 pandemic. The effect is particularly pronounced among older subjects. Our
 analysis of the mechanisms behind the detected decrease in solidarity
 suggests that subjects’ expectations about others’ behavior, perceived
 mortality risk, and (alarming) information in behavioral adaptation play a
 key—but independent—role.
description: <em><strong>Recruitment and Sample</strong></em> We invited 103 university students of an Andalusian university to participate in an online experiment. The students were encouraged and incentivized to recruit further participants, with the objective of obtaining a richer subject pool in terms of age, non-student status, and other characteristics. Gender balance and homogeneity across different ages was explicitly encouraged. Neither the participation nor recruitment were compulsory. Those who decided to participate (<i>n=85</i>) recruited other participants from Andalusia, other Spanish regions, or outside Spain . The experiment focused on the region of Andalusia, but this not prevented participation of people from outside Andalusia (people from other parts of Spain, <i>n=191</i>, and from other countries, <i>n=20</i>). Given that the non-Andalusian participants came from many different locations and that their numbers within locations were small and unevenly distributed, they were excluded from the analyses. Our procedures resulted in a final sample of 969 Andalusian participants (mean age = 35.10; SD = 17.16) of which 55% were females. Our sample allows us to obtain small effects (<i>r </i>= 0.09) with 80% power and <i>alpha=</i><i>0.05</i>. The sample sizes for each day from March 20–25 were 163, 188, 139, 92, 129, and 258, respectively. Since the observations were not uniformly distributed across the six days of the experiment, we conservatively split the sample in half into two periods to ensure the right balance in our main analysis: March 20–22 (<i>n = 490</i>) and March 23–25 (<i>n = 479</i>). This allows us to obtain a relatively balanced sample between both three-day periods in terms of sample size, age, education and gender. Readers might raise certain concerns regarding our experimental design, namely its online nature and the endogenous selection of the participation in the date of the experiment. Note that our comparison is balanced in most respects (especially, gender, age, and student vs. non-student participants) and we control for individual differences. In addition, Snowberg and Yariv (2021) provide a large-scale incentivized survey and report little difference between online and lab behavior and, importantly, between fast and slow participants as well as participants who endogenously participate early vs. late—even those who are several times reminded to participate—in their online survey. Although we cannot rule out these concerns, they do not seem to be justified in contexts similar to ours. Most importantly, we do not claim that we provide causal evidence and interpret our results as suggestive but highly relevant for the understanding of human prosocial behavior and anti-pandemic policies. All participants signed an informed consent and the data were anonymized in accordance with the Spanish Law on Personal Data Protection 3/2018. There are no participants under 16 year old. The main purpose of this experiment was to gather data for teaching purposes. To study the Covid-19 was not the main goal. However, the home confinement was the reason to run the experiment online in order to have data to discuss in class. <em><strong>Experimental tasks</strong></em> As is standard in economic experiments, we used monetary incentives. We informed all participants that they would participate in a lottery in which two participants would earn €100. The identity and behavior of each participant were kept anonymous to prevent reputational concerns that could affect behavior. Experimental earnings (from decisions during the experiment) were converted into tickets for the two €100-lotteries. The entire experimental setting consisted of several tasks (Original instructions in Spanish and the translation to English are available here: https://repositorio.uloyola.es/handle/20.500.12412/2250.). In this paper, we focus on three behavioral measures elicited in the experiment: <i>Donations</i>. We elicit answers to the following question: "<i>If you win the 100€ prize, would you like to donate a fraction to an NGO?</i>" People could choose any donation between 0% and 100%, in 10% increments. This question was incentive compatible and implemented without deceiving participants. <i>Expected others’ donations</i> (not incentivized). Using the same question format, participants were asked to report their answer to the question “<i>How much money do you think the other participants will donate to the NGO?</i>”. This variable also ranges from 0 to 100%, in 10% increments. In line with previous evidence (e.g. Brañas-Garza et al., 2017), expected donations are lower than real donations (matched-pairs t-test, <i>p </i>&lt; <i>0.001</i>), although they are strongly correlated (Pearson’s <i>r </i>= <i>0.636, p &lt; 0.001</i>). That is, people expect others to be less generous than themselves and those who give more expect others to give more. <i>Self-reported solidarity and envy</i>. These social preference variables measure people’s self-reported aversion to advantageous inequality, often referred to as “compassion” or “guilt”, and disadvantageous inequality (Fehr and Schmidt, 1999), respectively. Using a Likert scale, we asked participants their agreement with the statement “<i>I do not care about how much money I have; what concerns me is that there are people who have less (more) money than I have</i>” (proposed in Espín et al. 2018). As in Espín et al. (2018), these measures predict donations (see Table A2), the participants report higher SR-solidarity than SR-envy (matched-pairs t-test, <i>p </i>&lt; <i>0.001</i>), and the two measures are only weakly correlated (Pearson’s <i>r = 0.117, p &lt; 0.001</i>). We additionally elicited certain socio-demographic variables, including gender, age, education, and province of residence. These variables were employed in the regression analysis. Appendix A2 and A3 provide an extensive description of the sample and the most relevant variables of this study. Subjects further participated in the following experimental tasks: the Cognitive Reflection Test (Frederick, 2005; Toplak et al., 2014), Risk Preferences (Holt and Laury, 2002), Time Preferences (Coller and Willians, 1999; Martin et. al., 2019), Stag Hunt Game (Skyrms, 2004) and the Big-5 personality inventory (Rammstedt and John, 2007). These data are not employed in this study. <strong><em>Other measures</em></strong> We complement our experimental data with the official Spanish statistics regarding the daily number of <i>deaths</i>, <i>infected</i> people, and patients at <i>intensive care units</i> (MSCBS, 2020). Since these data were released every day at 9 pm and immediately reported by virtually all Spanish media, we analyze whether the official figures from one day affect the next-day donations in our experiment. In our analysis, we interpret these figures as the public information that people perceived regarding the intensity of the pandemic threat.