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How do donors reason and justify their choices when faced with dilemmas in a charitable context? In two studies, Swedish students were confronted with helping dilemmas based on the identifiable victim effect, the proportion dominance effect and the ingroup effect. Each dilemma consisted of two comparable charity projects and participants were asked to choose one project over the other. They were then asked to provide justifications of their choice by stating the relative importance of different types of reasons. When faced with an identified victim dilemma, participants did not choose the project including an identified victim more often than the project framed statistically, but those who did emphasized emotional reasons (e.g., ＂Because I had more empathic feelings＂), but not any other reasons, more than those choosing the statistical project. When faced with a Proportion dominance dilemma, participants more often chose the project with a high rescue proportion (e.g., you can save 100% out of 30) than the project with a low rescue proportion (e.g., you can save 4% out of 800), and those who did emphasized efficacy reasons (e.g., ＂Because my money can make a greater difference there＂), but no other reasons, more than those favoring the low recue proportion project. Finally, when faced with an Ingroup dilemma, participants more often chose the project that could help ingroup-victims over the project that could help outgroup victims, and those who did emphasized responsibility reasons (e.g., ＂Because I have a greater obligation＂), but no other reasons, more than those favoring outgroup projects. These results are consistent with and extend previous findings about how different helping effects are related to different psychological processes.

In ten studies (N = 9187), I systematically investigated the direction and size of seven helping effects (the identifiable-victim effect, proportion dominance effect, ingroup effect, existence effect, innocence effect, age effect and gender effect). All effects were tested in three decision modes (separate evaluation, joint evaluation and forced choice), and in their weak form (equal efficiency), or strong form (unequal efficiency). Participants read about one, or two, medical help projects and rated the attractiveness of and allocated resources to the project/projects, or choose which project to implement. The results show that the included help-situation attributes vary in their: (1) Evaluability – e.g., rescue proportion is the easiest to evaluate in separate evaluation. (2) Justifiability – e.g., people prefer to save fewer lives now rather than more lives in the future, but not fewer identified lives rather than more statistical lives. (3) Prominence – e.g., people express a preference to help females, but only when forced to choose.

Faced with endlessly repeated opportunities to save drowning children, most people think morality intuitively permits us to indulge in at least some goods that are not nearly as important as a child’s life. Some philosophers argue that this intuition gives us an important (though defeasible) reason to think we may sometimes permissibly refuse to save a life even when we can do so at insignificant cost. I argue that recent psychological experiments should make us wary of this claim.

In ten studies (N = 9187), I systematically investigated the direction and size of seven helping effects (the identifiable-victim effect, proportion dominance effect, ingroup effect, existence effect, innocence effect, age effect and gender effect). All effects were tested in three decision modes (separate evaluation, joint evaluation and forced choice), and in their weak form (equal efficiency), or strong form (unequal efficiency). Participants read about one, or two, medical help projects and rated the attractiveness of and allocated resources to the project/projects, or choose which project to implement. The results show that the included help-situation attributes vary in their: (1) Evaluability – e.g., rescue proportion is the easiest to evaluate in separate evaluation. (2) Justifiability – e.g., people prefer to save fewer lives now rather than more lives in the future, but not fewer identified lives rather than more statistical lives. (3) Prominence – e.g., people express a preference to help females, but only when forced to choose.

Proportion responding (PR) is the preference for proportionally higher gains, such that the same absolute quantity is valued more as the reference group decreases. This research investigated this kind of proportion PR in decisions about saving lives (e.g., saving 10/10 lives is preferred to saving 10/100 lives). The results of two studies suggest that PR does not stem from an overall tendency to choose higher proportions, but rather from faulty deliberative reasoning. In particular, people who display PR are less likely to engage in deliberative reflection as measured by response time, the Process Dissociation Procedure, the Cognitive Reflection Test, a numeracy test, and a task assessing denominator neglect. This association between faulty deliberation and PR was observed only when choosing the highest proportion was non-normative because it came at the expense of absolute gains (e.g., saving 10/10 lives is preferred to saving 11/100 lives). These results help to make sense of discrepant findings in previous research, pertaining to how PR relates to biased reasoning and decision making.

Proportion responding (PR) is the preference for proportionally higher gains, such that the same absolute quantity is valued more as the reference group decreases. This research investigated this kind of proportion PR in decisions about saving lives (e.g., saving 10/10 lives is preferred to saving 10/100 lives). The results of two studies suggest that PR does not stem from an overall tendency to choose higher proportions, but rather from faulty deliberative reasoning. In particular, people who display PR are less likely to engage in deliberative reflection as measured by response time, the Process Dissociation Procedure, the Cognitive Reflection Test, a numeracy test, and a task assessing denominator neglect. This association between faulty deliberation and PR was observed only when choosing the highest proportion was non-normative because it came at the expense of absolute gains (e.g., saving 10/10 lives is preferred to saving 11/100 lives). These results help to make sense of discrepant findings in previous research, pertaining to how PR relates to biased reasoning and decision making.

Research has shown that people perceive themselves as less biased than others, and as better than average in many favorable characteristics. We suggest that these types of biased perceptions regarding intentions and behavior of others may directly affect people’s decisions. In the current research we focus on possible influences in the context of helping behavior. In four experiments we found that, people believe that others, compared to themselves, are less inclined to help and cooperate, are less aware of the number of bystanders and more influenced by the “proportion dominance” bias and by the “identifiable victim effect.” We demonstrate that these perceptions are naïve and unrealistic by showing that decisions from both self and others’ perspectives are equally biased. Finally, we show how the perspective from which a decision is made (self vs. others) may affect private as well as public decisions in ways that might not be in the best interest of the decision maker and the public.

Research has shown that people perceive themselves as less biased than others, and as better than average in many favorable characteristics. We suggest that these types of biased perceptions regarding intentions and behavior of others may directly affect people’s decisions. In the current research we focus on possible influences in the context of helping behavior. In four experiments we found that, people believe that others, compared to themselves, are less inclined to help and cooperate, are less aware of the number of bystanders and more influenced by the “proportion dominance” bias and by the “identifiable victim effect.” We demonstrate that these perceptions are naïve and unrealistic by showing that decisions from both self and others’ perspectives are equally biased. Finally, we show how the perspective from which a decision is made (self vs. others) may affect private as well as public decisions in ways that might not be in the best interest of the decision maker and the public.

Introduction: The COVID-19 pandemic has become a serious challenge for humanity almost everywhere globally. Despite active vaccination around the world, the incidence proportion in different countries varies significantly as of May 2022. The reason may be a combination of demographic, immunological, and epidemiological factors. The purpose of this study was to analyze possible relationships between COVID-19 incidence proportion in the population and the types of SARS-CoV-2 vaccines used in different countries globally, taking into account demographic and epidemiological factors. Materials and methods: An initial database was created of demographic and immunoepidemiological information about the COVID-19 situation in 104 countries collected from published official sources and repository data. The baseline included, for each country, population size and density; SARS-CoV-2 testing coverage; vaccination coverage; incidence proportion; and a list of vaccines that were used, including their relative share among all vaccinations. Subsequently, the initial data set was stratified by population and vaccination coverage. The final data set was subjected to statistical processing both in general and taking into account population testing coverage. Results: After formation of the final data set (including 53 countries), it turned out that reported COVID-19 case numbers correlated most strongly with testing coverage and the proportions of vaccine types used, specifically, mRNA (V1); vector (V2); peptide/protein (V3); and whole-virion/inactivated (V4). Due to the fact that an inverse correlation was found between ‘reported COVID-19 case numbers’ with V2, V3, and V4, these three vaccine types were also combined into one analytic group, ‘non-mRNA group’ vaccines (Vnmg). When the relationship between vaccine type and incidence proportion was examined, minimum incidence proportion was noted at V1:Vnmg ratios (%:%) from 0:100 to 30:70. Maximum incidence proportion was seen with V1:Vnmg from 80:20 to 100:0. On the other hand, we have shown that the number of reported COVID-19 cases in different countries largely depends on testing coverage. To offset this factor, countries with low and extremely high levels of testing were excluded from the data set; it was then confirmed that the largest number of reported COVID-19 cases occurred in countries with a dominance of V1 vaccines. The fewest reported cases were seen in countries with a dominance of Vnmg vaccines. Conclusion: In this paper, we have shown for the first time that the level of reported COVID-19 incidence proportion depends not only on SARS-CoV-2 testing and vaccination coverage, which is quite logical, but probably also on the vaccine types used. With the same vaccination level and testing coverage, those countries that predominantly use vector and whole-virion vaccines feature incidence proportion that is significantly lower than countries that predominantly use mRNA vaccines.

This paper considers the adaptability of estimation methods for binary response panel data models to multiple fixed effects. It is motivated by the gravity equation used in international trade, where important papers use binary response models with fixed effects for both importing and exporting countries. Econometric theory has mostly focused on the estimation of single fixed effects models. This paper investigates whether existing methods can be modified to eliminate multiple fixed effects for two specific models in which the incidental parameter problem has already been solved in the presence of a single fixed effect. We find that it is possible to generalize the conditional maximum likelihood approach to include two fixed effects for the logit. Monte Carlo simulations show that the conditional logit estimator presented in this paper is less biased than other logit estimators without sacrificing on precision. This superiority is emphasized in small samples. An application to trade data using the logit estimator further highlights the importance of properly accounting for two fixed effects.