Person:

Tingley, Dustin

I study experimentally a single-shot trust game where players have the opportunity to choose an avatara computer-generated faceto represent them. These avatars vary on several dimensionstrustworthiness, dominance, and threatidentified by previous work as influencing perceptions of those who view the faces (Todorov, Said, Engell, & Oosterhof, 2008). I take this previous work and ask whether subjects choose faces that are ex ante more trustworthy, whether selected avatars have an influence on strategy choices, and whether individuals who evaluate faces as more trustworthy are also more likely to trust others. Results indicate affirmative answers to all three questions. Additional experimental sessions used randomly assigned avatars. This design allows me to compare behavior when everyone knows avatars are self-selected versus when everyone knows they are randomly assigned. Random assignment eliminated all three effects observed when subjects chose their avatars.

Mates appear to assort on political attitudes more than any other social, behavioral, or physical trait, besides religion. Yet the process by which ideologically similar mates end up together remains ambiguous. Mates do not appear to consciously select one another based on ideology, nor does similarity result from convergence. Recently, several lines of inquiry have converged on the finding that olfactory processes have an important role in both political ideology and mate selection. Here we integrate extant studies of attraction, ideology, and olfaction and explore the possibility that assortation on political attitudes may result, in part, from greater attraction to the scent of those with shared ideology. We conduct a study in which individuals evaluated the body odor of unknown others, observing that individuals are more attracted to their ideological concomitants.

Cooperation among genetically unrelated individuals can be supported by direct reciprocity. Theoretical models and experiments with adults show that the possibility of future interactions with the same partner can promote cooperation via conditionally cooperative strategies such as tit-for-tat (TFT). Here, we introduce a novel implementation of the repeated Prisoner’s Dilemma (PD) designed for children to examine whether repeated interactions can successfully promote cooperation in 10 and 11 year olds. We find that children cooperate substantially more in repeated PDs than in one-shot PDs. We also find that girls cooperate more than boys, and that children with more conduct problems cooperate less. Finally, we find that children use conditional cooperation strategies but that these strategies vary by gender and conduct problem rating. Specifically, girls and children with few conduct problems appear to follow an altruistic version of win-stay, lose-shift (WSLS), attempting to re-establish cooperation after they had defected. Boys and children with more conduct problems appear to follow a Grim strategy, defecting for the duration after the partner defects. Thus we provide evidence that children utilize the power of direct reciprocity to promote cooperation in strategic interactions and that, by late elementary school, distinct strategies of conditional cooperation have emerged.

We investigate theoretically and experimentally the crisis bargaining model, a dynamic game of two-sided incomplete information with player types drawn from a commonly known distribution. Little work has been done to analyze whether and how players update their beliefs in such games. Within the experiment we elicited beliefs from players about their opponent's type using a proper scoring rule. We implement two treatments that vary the cost of backing down to the first mover after initial entry, generating sharp comparative static predictions in both beliefs and strategies. We find that players do update their beliefs in the predicted directions after observing some of the action choices. However, we highlight evidence of conservative updating relative to rational expectations.

Recent advances in research tools for the systematic analysis of textual data are enabling exciting new research throughout the social sciences. For comparative politics, scholars who are often interested in non-English and possibly multilingual textual datasets, these advances may be difficult to access. This article discusses practical issues that arise in the processing, management, translation, and analysis of textual data with a particular focus on how procedures differ across languages. These procedures are combined in two applied examples of automated text analysis using the recently introduced Structural Topic Model. We also show how the model can be used to analyze data that have been translated into a single language via machine translation tools. All the methods we describe here are implemented in open-source software packages available from the authors.

What factors determine firms' attitudes toward trade policy? This paper considers producers' policy preferences and political behavior in light of two key patterns in modern international trade: industries that face import competition often have many exporters, and foreign sales are concentrated in the hands of a small number of "superstar" exporters. Using a new survey of Costa Rican firms matched to systematic firm-level data on export behavior, we find that firm features are generally more important predictors of attitudes toward trade liberalization than industry-wide comparative advantage. We also show that export intensity is strongly associated with interest and lobbying activity on trade policy. The largest exporters, who are the strongest supporters of global integration, dominate trade politics.

Identifying causal mechanisms is a fundamental goal of social science. Researchers seek to study not only whether one variable affects another but also how such a causal relationship arises. Yet commonly used statistical methods for identifying causal mechanisms rely upon untestable assumptions and are often inappropriate even under those assumptions. Randomizing treatment and intermediate variables is also insufficient. Despite these difficulties, the study of causal mechanisms is too important to abandon. We make three contributions to improve research on causal mechanisms. First, we present a minimum set of assumptions required under standard designs of experimental and observational studies and develop a general algorithm for estimating causal mediation effects. Second, we provide a method for assessing the sensitivity of conclusions to potential violations of a key assumption. Third, we offer alternative research designs for identifying causal mechanisms under weaker assumptions. The proposed approach is illustrated using media framing experiments and incumbency advantage studies.

In this paper, we describe the R package mediation for conducting causal mediation analysis in applied empirical research. In many scientific disciplines, the goal of researchers is not only estimating causal effects of a treatment but also understanding the process in which the treatment causally affects the outcome. Causal mediation analysis is frequently used to assess potential causal mechanisms. The mediation package implements a comprehensive suite of statistical tools for conducting such an analysis. The package is organized into two distinct approaches. Using the model-based approach, researchers can estimate causal mediation effects and conduct sensitivity analysis under the standard research design. Furthermore, the design-based approach provides several analysis tools that are applicable under different experimental designs. This approach requires weaker assumptions than the model-based approach. We also implement a statistical method for dealing with multiple (causally dependent) mediators, which are often encountered in practice. Finally, the package also offers a methodology for assessing causal mediation in the presence of treatment noncompliance, a common problem in randomized trials.