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Do individuals' donations and trust levels depend on their perception of the mind's dimensions? In this project we measure voluntary donations and trust across stimuli that vary in the dimensions of the mind. Voluntary donation is measured in a one-shot dictator game, and trust is measured in a one-shot standard trust game. We also measure important subjective ratings of attributes across the various stimuli such as "Human Likeness", "Trustworthiness", "Agency", and "Experience" through asking participants to rate each attribute via a Likert-scale.

This project aims to investigate international return migration in China amid the pandemic. Against the backdrop of globalization and temporary migration regimes, a significant number of overseas Chinese citizens returned to China from their host countries as the pandemic unfolded and return policies tightened. Evidence from self-reported incidents and social media trends suggest prejudice and discriminatory behavior towards overseas returnees. Migration scholars have studied the phenomenon of cities and corporations in China vying for talents returning from overseas in recent years; however, little is known about public attitudes and how these opinions are affected by the pandemic. Relying on an online sample in China, we investigate the consequences of the pandemic-induced out-group formation, document potential differential treatment towards Chinese returnees comparing to non-returnees, and probe the pathways that may mitigate the undesirable effects of out-group formation. We employ a give-or-take dictator game to examine potential differential treatment and apply information priming to investigate whether increased awareness of the returnee experience would affect the mistreatment via the reduction of statistical discrimination.

We are filing an amendment to the pre-analysis plan (egap.org, \#20190912AD), from now on PAP, after the analyses of the outcome data. In the attached document first, we discuss the motivation for this amendment, then, we discuss the content of the amendment. We do not restate the particulars of the experiment, conditions and procedures here, as no change have been made to the particulars of the experiment, or its procedures.

As machines become increasingly intelligent, many roles that have been traditionally taken by people are now supplemented or augmented by machines and algorithms. Such roles require various levels of interaction between people and machines/algorithms that make decisions, thus research into human-machine interaction is timelier than ever. Recently, research on this topic has adopted methods from behavioral economics and behavioral psychology to study how people react and interact with bots by employing incentivized games. This interdisciplinary approach is gradually recognized and increasingly adopted by the academy and industry to understand the underlying mechanisms that drive differences from platform to platform, and to forecast each platform's performance. We build on recent work by Jordan and colleagues \cite{Jordan} to investigate the behaviors of fairness, punishment and trust when people are interacting with bots. We carried out an online experiment that includes a two stage game following \cite{Jordan}; a third-party-punishment game followed by a trust game with approximately 3500 participants who interacted with one another and/or with bots that made pre-determined decisions over the summer of 2019. We are building on this prior work in this study where we now investigate the impact of manipulating norm-consensus (in the form of injunctive norms, i.e., what one should do), and their relationship to the trust one gains when being seen as behaving according to the consensus. We are conducting a within-person design where we invite participants from the first experiment conducted a year ago, and give them norm-consensus information. We expect that the information-treatment we administer will increase the trust-gain gap between those who behave according to the consensus-behavior compared to those who do not behave in such a manner as compared to the setting that participants made decisions a year ago, receiving no such information. Out of the 3 experimental conditions, we anticipate such effects where prior measurement shows lower levels of believed consensus. In the third condition, where we give participants truthful information on the basis of prior measurement we do not have clear expectations.

Environmental degradation continues to pose one of the greatest threats to human well-being, disproportionately burdening communities of color. Environmental action, however, fails to reflect this urgency, leaving social-behavioral research at the frontier of environmental conservation, as well as environmental justice. Broad societal consensus for environmental action is particularly sparse among conservatives. The lack of even small personal sacrifices in favor of the environment could be attributed to the relatively low salience of environmental threats to white Americans and the partisan nature of environmentalism in America. We evaluate if (1) environmental action is causally related to the partisan value framing of an environmental issue; and (2) if the perceived race of impacted communities influences environmental action as a function of racial resentment. Using a large-scale, original survey experiment examining the case of air-pollution, we advance our understanding of environmental justice advocacy and environmental inaction in the United States.

How much do people contribute to a public good in a society where people and bots —artificial, decision-making agents—coexist? The proposed experiment, the first in a series of two, exam- ines this question through the lens of a repeated public goods game (repeated PGG), where some of the participants are incorrectly labeled as bots, while others are correctly labeled as people. The goal is to understand whether and how people’s contributions are affected when increasing the number of participants labeled as bots.

As machines become increasingly intelligent, many roles that have been traditionally taken by people are now supplemented or augmented by machines and algorithms. Such roles require various levels of interaction between people and machines/algorithms that make decisions, thus research into human-machine interaction is timelier than ever. Recently, research on this topic has adopted methods from behavioral economics and behavioral psychology to study how people react and interact with bots by employing incentivized games. This interdisciplinary approach is gradually recognized and increasingly adopted by the academy and industry to understand the underlying mechanisms that drive differences from platform to platform, and to forecast each platform’s performance. We build on recent work by Jordan and colleagues to investigate the behaviors of fairness, punishment and trust when people are interacting with bots [1]. We carried out an online experiment that includes a two stage game following [1]; a third-party-punishment game followed by a trust game with approximately 3500 participants who interacted with one another and bots that made pre-determined decisions. We are building on this prior work in this study where we now investigate the level of consensus around behaviors, and their relationship to the trust one gains when behaving according to the consensus view. In short, we have two main expectations (1) the higher the level of consensus about a behavior, the higher the level of trust; and (2) the differences in trust gain can partially or fully account for the differences between trust gains across experimental conditions that manipulate the signalled identity of interaction partners.

We study mentorship in scientific collaborations, where a junior scientist is supported by potentially multiple senior collaborators, without them necessarily having formal supervisory roles. We identify 3 million mentor–protégé pairs and survey a random sample, verifying that their relationship involved some form of mentorship. We find that mentorship quality predicts the scientific impact of the papers written by protégés post mentorship without their mentors. We also find that increasing the proportion of female mentors is associated not only with a reduction in post-mentorship impact of female protégés, but also a reduction in the gain of female mentors. While current diversity policies encourage same-gender mentorships to retain women in academia, our findings raise the possibility that opposite-gender mentorship may actually increase the impact of women who pursue a scientific career. These findings add a new perspective to the policy debate on how to best elevate the status of women in science., Here, the authors study mentorship in scientific collaborations, and find that mentorship quality predicts the scientific impact of protégés post mentorship. Moreover, female protégés collaborating with male mentors become more impactful post mentorship than those who collaborate with female mentors.

Environmental degradation continues to be one of the greatest threats to human well-being, posing a disproportionate burden on communities of color. Environmental action, however, fails to reflect this urgency, leaving social-behavioral research at the frontier of environmental conservation, as well as environmental justice. Broad societal consensus for environmental action is particularly sparse among conservatives. The lack of even small personal sacrifices in favor of the environment could be attributed to the relatively low salience of environmental threats to white Americans and the partisan nature of environmentalism in America. We evaluate if (1) environmental action is causally related to the ideological value framing of an environmental issue; and (2) if the perceived race of impacted communities influences environmental action as a function of racial resentment. Using a large-scale, original survey experiment examining the case of air-pollution, we advance our understanding of environmental justice advocacy and environmental inaction in the United States.

This thesis builds the micro foundations of the first modern social movement: the movement for the abolition of the slave trade in the early 19th century British context. I derive theories of action from the historical literature, and use work from historical sociology and movement theory to understand the decision to petition for abolition. Two major empirical undertakings are employed to adjudicate between different theories of action accounting for abolitionist petitioning. First, zooming in on Manchester, I deploy the signatures of an abolitionist petition to find the social-structural drivers of abolitionist mobilization. Through a careful reconstruction of the city’s historic geography, I place over 10000 residents in physical space along with important buildings, such as churches, inns and taverns: focal points that provided the basis of associational life and early civil society, places where politics was done at the time. I delineate the limits of the impact of the Quaker congregation, and demonstrate that in fact these focal points induced the spatial-clustering of abolitionist petitioners. Furthermore, I reveal that economic interests are not among key drivers of abolitionist petitioning, as no clear occupational-gradient is found among petitioners. Besides the theoretical contributions, I use innovative ways to test which social relationships were crucial for petitioning. Second, zooming out on the national petitioning campaign I use self-collected data on petitions form the Journals of the British Parliament to study the movement at the macro level. The analysis shows that contact with the London-based central movement-organization was key for the success of the first campaign, but it also reveals that the second campaign relied more on "horizontal" connections rather than hierarchical ones tying provincial towns to London. Second, I confirm that non-conformist religious organizations were pivotal for the inception, and scaling of the national campaign, but the Quaker church seem to exert more important and continuous influence compared to the Wesleyan Methodist organization. Last but not least, I show that industrialization plays a key, and increasingly important role in the campaign for abolition.

Collective violence when framed by its perpetrators as “citizen” justice is inherently a challenge to state legitimacy. To properly account for such violence, it is necessary to consider an opportunity structure incorporating the actions of both vigilantes and agents of the state. The motivation and lethality of lynch mobs in the South cannot be understood without considering how the state reacted to the legitimacy challenges posed by lynching. We trace the shifting orientation of state agents to lynching attempts between the end of Reconstruction and the start of the Great Depression. Analyzing an inventory of more than 1,000 averted and completed lynching events in three Southern states, we model geographic and temporal patterns in the determinants of mob formation, state intervention, and intervention success. Opponents of lynching often pled with mobs to “let the law take its course.” This article examines the course followed by the law itself, as state actors moved between encouraging, accommodating, and in many instances averting mob violence

Drawing on population level data of exceptional quality (including detailed diagnostic information on the autism status of sibling pairs of over 3 million different mothers), this study confirms that stoppage is the average fertility response to a child born with autism, thereby reducing observed concordance in sibling pairs and leading to potentially biased estimation of genetic contributions to autism etiology. Using a counterfactual framework and applying matching techniques we show, however, that this average effect is composed of very different responses to suspicion of autism depending on birth cohort, the character of the disorder (severe versus less severe), the gender of the child, poverty status, and parental education. This study also sheds light on when parents suspect autism. We find that parents’ fertility behavior changes relative to matched controls very early after the birth of a child who will later be diagnosed with autism.

Existing literature focuses on economic competition as the primary causal factor in Southern lynching. Political drivers have been neglected, as findings on their effects have been inconclusive. We show that these consensus views arise from selection on a contingent outcome variable: whether mobs intent on lynching succeed. We constructed an inventory of averted lynching events in Georgia, Mississippi, and North Carolina—instances in which lynch mobs formed but were thwarted, primarily by law enforcement. We combined these with an inventory of lynching and analyzed them together to model the dynamics of mob formation, success, and intervention. We found that low Republican vote share is associated with a higher lethality rate for mobs. Lynching is better understood as embedded in a post-conflict political system, wherein all potential lynching events, passing through the prism of intervention, are split into successful and averted cases.

The network of patents connected by citations is an evolving graph, which provides a representation of the innovation process. A patent citing another implies that the cited patent reflects a piece of previously existing knowledge that the citing patent builds upon. A methodology presented here (i) identifies actual clusters of patents: i.e. technological branches, and (ii) gives predictions about the temporal changes of the structure of the clusters. A predictor, called the {citation vector}, is defined for characterizing technological development to show how a patent cited by other patents belongs to various industrial fields. The clustering technique adopted is able to detect the new emerging recombinations, and predicts emerging new technology clusters. The predictive ability of our new method is illustrated on the example of USPTO subcategory 11, Agriculture, Food, Textiles. A cluster of patents is determined based on citation data up to 1991, which shows significant overlap of the class 442 formed at the beginning of 1997. These new tools of predictive analytics could support policy decision making processes in science and technology, and help formulate recommendations for action.