Key Findings

We are very grateful to everyone who submitted suggestions for this poll. We hope the findings are useful to all of you.

For other sources, we know it can be hard to know who to trust. Our research team recommends using Media Bias/Fact Check to help you figure it out–it’s what we ourselves use. They provide a simple rating for each news source, but also a detailed Methodology section fully outlining how they arrive at their ratings so that you can see that they themselves are not biased.

Many rumors about COVID-19 are circulating online and in less-reputable news sources, so Faunalytics wants to remind our readers of how important it is to rely on unbiased media and scientific reporting. Faunalytics is one such source, as the articles that are published in our library come from academic journals, and we vet the stories we share on social media.

Before diving into respondents’ understanding of COVID-19, it is important to be clear on the scientific community’s understanding. The latest and most reliable scientific evidence indicates that the virus likely originated in a wet market in Wuhan, China ( Riou & Althaus, 2020 ). Wet markets sell both live and dead animals from wild and farmed sources. They bring humans and animals into close proximity, providing a way for viruses to jump from species to species, as has also been observed in previous outbreaks ( Woo, Lau, & Yuen, 2006 ).

The current project was designed to gather information for animal welfare organizations in light of the current outbreak of COVID-19 (also referred to as the novel coronavirus). We asked advocates to submit survey ideas in order to determine what information would best help the organizations who make up our audience. We combined those ideas into a set of questions to administer as a poll in the U.S.

Methods & Results

Method

Participants were 1,000 general population U.S. citizens who were at least 18 years old, recruited through Ipsos’ KnowledgePanel. Panel participants are recruited based on physical addresses, which provides a more representative sample than convenience samples or those that require participants to have a cell phone or computer.

Survey data is weighted based on population demographics and the margin of error for the full sample is 3 percentage points, nineteen times out of twenty. Participants completed the survey between Friday, March 27th and Sunday, March 29th, 2020.

There were five groups of questions in the survey. Each is described with results below. In addition, the full survey instrument as well as the preregistration and data are available on the Open Science Framework.

Overall Results

These top-line results describe the answers from all respondents, weighted to be maximally representative of the U.S. population. The results for demographic groups–gender, age, income, and region, Ipsos’ default options–are provided in a separate section below.

Understanding And Beliefs About COVID-19

One of the primary goals of this poll was to understand, in a timely manner, whether members of the general public are making the connection between the COVID-19 outbreak and human abuses of animals. Several questions were included to help us understand that connection. The results for these questions are laid out in this section.

Circumstances Leading To The Pandemic (Open-Ended Responses)

The first poll question was one asking participants to describe how the outbreak started. The survey was designed in this way so that subsequent closed-ended questions would not alter the responses that participants gave.

The question read:

The world is currently fighting a pandemic due to the novel coronavirus (COVID-19). Please explain your understanding of where this disease came from. We are not asking about the city or country where it started, but rather what circumstances led to the disease.

We analyzed the responses to this question using a combination of automated text analysis and interpretation. For more details, see the Supplementary Materials tab. The full set of responses, with codes, is available on the Open Science Framework project page.

Table 1. Themes in Participants’ Understanding of COVID-19’s Origins.

As you can see in the table above, only a small minority of the U.S. population has a strong understanding of the novel coronavirus’ origins, probably 10 to 20% at most, based on the percentage who mentioned wet markets. (In case you missed it, we outlined the current scientific information about its origins on the Key Findings tab).

Although about half of respondents were aware that the virus had originated with animals in some way, many were unsure of the details, and a substantial number had clearly been misled by rumor and misreporting.

Beliefs About Animals And COVID-19 (True/False)

Second, respondents were presented with a question set assessing their beliefs about animals and COVID-19. This was a series of statements for which they were asked to “select all TRUE statements from the following list, or choose the last option if none of the statements are true.”

The graph below shows the percentage of respondents who believed each statement to be true.

Figure 1. Beliefs & Understanding About COVID-19.

Many of the statements focused on the animal origins of the disease, but several addressed other issues. Very few respondents were aware of the threats to life faced by animals used in research (Science, March 2020) or those who depend on tourists for food (New York Times, March 2020): only 5% and 12%, respectively. However, almost all respondents were aware that companion animals (pet cats and dogs) are not generally spreading the virus (Medical News Today, April 2020).

In addition to the data shown in the figure above, 29.3% of the sample said that none of the statements were true, when in reality, six of them are factually correct. This fact, as well as the low endorsement rates of each of the true items, speak to a powerful lack of awareness about the animal origins of the disease.

Implications And Actions (Agreement Questions)

The third question set was a Likert-style matrix assessing participants’ agreement or disagreement with five statements. These statements, shown in the figure below, related to their understanding of COVID-19 and support for various legislative actions.

Figure 2. Beliefs & Support For Legislation.

Substantial proportions of respondents support restrictions on agriculture and trade to prevent future outbreaks of disease, and only a minority were opposed.

In addition, the results show that the majority of the U.S. population believes that animal shelters and sanctuaries should be considered essential services. Whether they are currently defined as such or not depends on jurisdiction.

Only a small proportion of respondents agreed that there was a direct connection between the outbreak and livestock farming. This suggests that the connection would need to be spelled out for most, similar to how we presented it in the final question of this survey (see section “Reaction To An Argument Connecting Disease And Animal Farming”).

Behavioral Intentions

The fourth survey question assessed how dietary and donation behaviors are likely to change as a result of the pandemic. Specifically, the question asked, “Since the COVID-19 pandemic started, how has your likelihood of doing each of the following changed?” The range of responses is shown in the table below.

Figure 3. Dietary & Donation Intentions.

The majority of people indicated that their diets and donation patterns were unlikely to be affected by the novel coronavirus, as indicated by the gray bars in the graph above. However, substantial numbers anticipated an impact of the pandemic on their dietary or donation behavior.

The proportions of people who said they are more likely to try plant-based eating or reduce their meat consumption because of the pandemic were about the same as the proportion who said it would have the opposite effect. From these results, it seems that we should not expect overall animal product consumption to change substantially because of COVID-19, although some individuals may shift their behavior.

Respondents also claimed that they were more likely than usual to donate to charity because of the pandemic, but we should treat these claims with skepticism, as data from the 2008 recession suggests that overall donations are likely to decrease substantially (Reich & Wimer, 2012). There may also be more targeting of donations toward poverty-related causes, which could divert funding away from animal charities.

When asked specifically about their likelihood of donating to an animal charity, the proportion of respondents who said they were more likely to donate because of the pandemic was similar to the proportion who said they were less likely. However, we do not know from these data what type of animal charity they might support or in what amount.

Reaction To An Argument Connecting Disease And Animal Farming

In the final section of the survey, respondents were asked for their reaction to an argument connecting diseases like COVID-19 to the conditions present in intensive animal agriculture. The statements in this paragraph were evidence-based but not cited. We did not include a strong advocacy message because the goal was to examine reaction to the argument itself–of a connection between disease and farmed animal welfare–rather than a particular style of advocacy using it.

The paragraph stated:

According to the CDC, 3 out of every 4 new or emerging infectious diseases in people–including COVID-19–come from animals. These animal-to-human diseases already account for millions of deaths a year, and are likely to increase as industrial farming becomes more widespread throughout the world. On these industrial farms, which are already the norm in the U.S., animals are crammed into small cages in unsanitary conditions, allowing disease to run rampant–or antibiotic-resistant strains to develop when drugs are used to control infections. Instead of reacting to outbreaks of animal-to-human diseases like SARS, Ebola, and COVID-19 as they occur, we need to prevent them by considering our treatment of all animals, wild and farmed.

The sources for these statements are provided in the Supplementary Materials.

Respondents’ reactions were measured as agreement/disagreement with five possible descriptors of the paragraph, and are shown in the figure below.

Figure 4. Reactions To Argument.

The paragraph was seen as convincing and logical by the majority of participants. We think that if the citations for each claim were included, the number of people convinced by it could be even higher. Adding sources could also help to reduce the number of people who thought that the paragraph was misleading. (All sources used are in the supplemental materials for this study.)

In addition, comparatively few people found the paragraph annoying or offensive. However, it is important to bear in mind that this paragraph was almost purely factual, lacking any strong advocacy message like “go veg” or “reduce your meat consumption.” Thus, these results should be interpreted as a reaction to the argument itself. Adding the advocacy component is likely to increase reactance—perhaps substantially—for some people.

Specifically, older people were more likely to find the argument offensive than younger people. People in the Northeast tended to be more receptive than people from other geographical regions, as were people with higher incomes. The full details of subgroup differences are available in the Supplementary Materials.

Results By Question and Demographic

This section shows the breakdown of all quantitative questions by four key demographics: gender, age, income, and region.

Because the sample size for these demographics is much smaller than for the full survey sample, the margin of error for these estimates is correspondingly wider, as shown in the table below. When comparing percentages between groups, keep in mind that this is a sample of the population. The true number of people in the U.S. who hold each belief may be somewhat larger or smaller according to the margins of error, which are listed in Table 2.

Table 2. Subgroup Margins of Error.

Understanding And Beliefs About COVID-19

Beliefs About Animals And COVID-19 (True/False)

Table 3. Beliefs & Understanding About COVID-19: Demographic Breakdowns.

The following lists indicate which differences in the tables above are statistically significant. The full data tables indicating significance are available on the Open Science Framework.

Gender:

Men were more likely than women to correctly indicate that COVID-19’s spread to humans was because of markets where live animals were kept in close quarters with one another.

Men were also more likely to correctly indicate that new diseases that affect humans can come from either wild or farmed animals.

There were no other significant gender differences on this question.

Age:

People aged 35-49 were more likely than people aged 65+ to correctly indicate that the COVID-19 pandemic started because wild animals were being sold as food.

People aged 18-24 were more likely than people aged 50+ to correctly indicate that thousands of lab animals are being killed.

People under 50 were more likely than people over 65 to correctly indicate that animals who depend on tourists for food are going hungry.

People aged 35-49 were more likely than people over 65 to correctly indicate that COVID-19 spread to humans because of markets where live animals were kept in close quarters with one another.

People aged 35+ were more likely than those 25-34 to incorrectly say that there has never been a major human disease outbreak caused by farmed animals.

People aged 65+ were the most likely to incorrectly say that none of the statements were true.

There were no other significant age differences on this question.

Income:

People who make $25,000-49,000 were the most likely to incorrectly say that pet cats and dogs are a major reason that COVID-19 is spreading.

People making over $75,000 were more likely than people making under $25,000 to correctly indicate that the COVID-19 pandemic started because wild animals were being sold as food.

People making under $25,000 were more likely than people making over $75,000 to correctly indicate that thousands of lab animals are being killed.

People making under $25,000 or $50,000-74,999 were more likely than people making over $75,000 to say that a large number of farmed animals will need to be killed to control COVID-19.

People making over $75,000 were more likely than people making $25,000-49,000 to correctly indicate that COVID-19 most likely spread to humans because of markets where live animals were kept in close quarters with one another.

People making over $50,000 were more likely than people making under $25,000 to correctly indicate that new diseases that affect humans can come from either wild or farmed animals.

People making under $50,000 were more likely than people making over $50,000 to incorrectly say that none of the statements were true.

There were no other significant income differences on this question.

Region:

People in the Northeast and West were more likely than people in the Midwest to correctly indicate that the COVID-19 pandemic started because wild animals were being sold as food.

People in the Northeast were more likely than people in the South or West to correctly indicate that the COVID virus was able to jump from species to species because different types of live animals were kept in close quarters.

People in the Midwest were more likely than people in the South or West to say that a large number of farmed animals will need to be killed to control COVID-19.

People in the West were more likely than people in the South to correctly indicate that animals who depend on tourists for food are going hungry due to the pandemic.

People in the Northeast were more likely than people in any other region to correctly indicate that COVID-19 mostly likely spread to humans because of markets where live animals were kept in close quarters with one another.

People in the Northeast were more likely than people in the South to correctly indicate that new diseases that affect humans can come from either wild or farmed animals.

There were no other significant regional differences on this question.

Implications And Actions (Agreement Questions)

Table 4. Beliefs & Support For Legislation: Demographic Breakdowns.

The following lists indicate which differences in the tables above are statistically significant. The full data tables indicating significance are available on the Open Science Framework.

Gender:

Women were more likely than men to agree that any type of animal farming that has been linked to a serious human disease outbreak should be banned.

Women were more likely than men to agree that that animal shelters and sanctuaries should be considered essential services during a pandemic.

There were no other significant gender differences.

Age:

People aged 50-64 were less likely to support permanent trade bans than all younger groups were, and people over 65 were less likely to support them than those 18-34.

Those aged 25-34 were more likely to support restrictions on animal agriculture than those over 50, and those aged 35-49 were more likely to support restrictions than people over 65.

People aged 18-24 were more likely to see a direct connection between disease outbreaks land livestock farming than those aged 35+.

There were no other significant differences by age.

Income:

Compared to people in the top income bracket, people making $50,000-74,999 in income were more likely to agree that any type of animal farming that has been linked to a serious human disease outbreak should be banned.

Compared to people making under $25,000, those making over $75,000 were more likely to agree that animal shelters and sanctuaries should be considered essential services during a pandemic.

There were no other significant differences by income.

Region:

People from the South were more likely to support permanent trade bans than those from the West and Midwest.

Compared to people in the Midwest, people in the Northeast were more likely to see a direct connection between disease outbreaks and livestock farming.

There were no other significant differences by region.

Behavioral Intentions

Table 5. Dietary & Donation Intentions: Demographic Breakdowns.

The following lists indicate which differences in the tables above are statistically significant. The full data tables indicating significance are available on the Open Science Framework.

Gender:

More women than men said they were more likely than before the pandemic to donate to a new charity.

There were no other significant gender differences.

Age:

The proportion of people aged 65+ who said they were more likely to donate to an animal charity than before the pandemic was significantly larger than the proportion aged 35-49.

There were no other significant differences by age.

Income:

The proportion of people making over $75,000 who said they were more likely to donate to charity (in general) than before was significantly larger than the proportion of people making less than $25,000 or $50,000-74,999.

The proportion of people making $25,000-49,999 or over $75,000 who said they were more likely than before the pandemic to donate to a different non-profit than usual was significantly larger than the proportion of people making less than $25,000.

There were no other significant differences by income.

Region:

The proportion of people in the Midwest who said they were more likely to donate to charity (in general) than before was significantly larger than the proportion of people in the South.

The proportion of people in the Northeast who said they were more likely to donate to an animal charity than before was significantly larger than the proportion of people in the South. There were no other significant differences by region.



Reaction To An Argument Connecting Disease And Animal Farming

Table 6. Reaction To Argument: Demographic Breakdowns.

The following lists indicate which differences in the tables above are statistically significant. The full data tables indicating significance are available on the Open Science Framework.

Gender:

More women than men agreed that the paragraph was convincing.

There were no other significant gender differences in reactions to the paragraph.

Age:

People over 50 were more likely to say the paragraph was misleading than those aged 25-34.

People over 35 were more likely to say the paragraph was annoying than those aged 25-34.

People aged 50-64 were more likely to say the paragraph was offensive than those under 50; and people over 65 were significantly more likely to say the paragraph was offensive than all groups under age 50.

There were no significant differences in describing the paragraph as logical or convincing by age.

Income:

People making over $75,000 were more likely to say the paragraph was logical than those making $25,000-49,999.

People making more than $50,000 were more likely to say the paragraph was convincing than those making less than $25,000.

There were no significant differences in describing the paragraph as misleading, offensive, or annoying by income.

Region: