We investigate the trade-off between public support in the form of charitable contributions to healthcare needs versus income support for displaced workers during the Covid-19 pandemic. We conducted a nationwide incentivized experiment in the United States with 586 participants using Amazon-MTurk. Each participant makes a split-the-pot allocation of 100 tokens (10 tokens = $1.00) between two charitable organizations. The Health-charity works to reduce the health consequences of Covid-19 by equipping medical professionals with lifesaving medical resources. The Income-Charity provides assistance for hourly workers who have lost their jobs due to Covid-19 and are not able to work and do not have another source of income. The allocation is public, in the sense that participants allocate all the tokens between these two organizations using the experimenter’s money with a 10% probability. We identify five types of agents. Health exclusive and income exclusive agents use the entire 100 token allocation to support the health and income cause respectively. Pro-health and pro-income agents provide a majority of tokens to their preferred cause. Equal-split participants evenly allocate 50 tokens to each organization. In the control participants are only provided with general information about the Covid-19 outbreak without any reference to health or income issues. A Health treatment provides additional information about the pandemic’s devastating effects to public health. An Income treatment highlights the rise in unemployment and income issues of the coronavirus crisis. Finally, a Combined treatment includes information provision of the Health and Income treatments combined. Due to the nature of the pandemic, in which health information is naturally more salient, on average subjects allocate more funds to the Health Charity compared to the Income Charity across all treatments. In the Combined Treatment, participants do not behave differently than in the control condition. The socio-political profile of participants appears to significantly affect the token allocations.
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Samir Huseynov |
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Texas A&M University |
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