Current Research Projects

Women of similar ability are less likely than men to choose competitive compensation schemes, a gap with important implications for career advancement (Niederle & Vesterlund, 2007). This project investigates how interpersonal discussions influence individuals’ decisions in this domain. Many real-life career decisions—such as negotiating a raise or applying for a new job—are made after discussing with a romantic partner, yet the influence of such discussions remains largely understudied. 

We design an experiment in which participants are assigned to male–female dyads, with one partner (the Decision Maker) choosing between a piece-rate and a tournament compensation scheme for a task, first individually, then after a conversation with the other partner. By comparing real-life couples to stranger pairs, we assess whether intimate partners are better positioned to improve decision quality and reduce the gender gap in willingness to compete. 

Our design includes rich measurements of individual preferences and beliefs about the partner’s preferences to uncover potential mechanisms. We explore two main channels: (1) a debiasing effect, where the partner helps shift the Decision Maker’s confidence (e.g., women gain confidence and become more willing to compete); and (2) preference transmission or integration, where individuals align with their partner’s preferences (e.g., risk-averse women agree to take more risk and enter the tournament). These mechanisms may increase WTC for women, decrease it for men, and ultimately reduce the gender gap.

Couples frequently face major household decisions—such as buying a home, planning a move, or making a career change—that depend on interpreting complex, ambiguous, and often fragmented information. In these situations, each partner may have access to different, potentially relevant information, but this information is rarely common knowledge. For instance, one partner may have better insight into housing market trends through peers, while the other tracks household finances. While communication is a natural solution, recent research suggests that information exchange within couples is often imperfect—even when incentives are fully aligned.

Recent evidence highlights important gender asymmetries in how information flows within households. Fehr et al. (2024) show that men are less likely to integrate information provided by their wives, despite mutual incentives. Similarly, Conlon et al. (2021) find that women incorporate their partner’s signals when updating beliefs, whereas men tend to underweight their partner’s input. However, these findings have not been consistently replicated (e.g., Mustafi, 2024), and the underlying mechanisms—whether men discount input or women under-communicate—remain unclear.

This project contributes to this literature by examining how couples communicate and process information about home price dynamics, a domain that is both high-stakes and widely relevant. In a controlled experiment, participants are paired in gender-mixed dyads (either real-life couples or strangers) and receive different pieces of information about past housing price inflation. Drawing on prior evidence that perceived past inflation shapes beliefs about future prices (Armona et al., 2019), we elicit participants’ beliefs about past and future home price changes before and after receiving the information, and again after a recorded discussion between partners. Final individual beliefs allow us to identify how discussion affects belief updating and whether gender patterns emerge in responsiveness to a partner’s input.

With this design, we aim to disentangle gender differences in information pooling—do men disregard input, or do women under-communicate? Does this vary by partner type (spouse vs. stranger)? More broadly, we shed light on how households handle real-world ambiguity and whether communication effectively helps combine dispersed, noisy information.

This project investigates whether the quality of user interfaces (UI) in economic experiments affects the reliability of behavioral data. While experimental economists devote significant attention to designing robust incentives and clear instructions, comparatively little scrutiny has been given to the interfaces through which participants interact with experimental tasks.  Drawing on insights from web design and human-computer interaction, we test whether UI flaws—ranging from poor visual design to inconsistent input methods—can degrade data quality by increasing noise, reducing engagement, or inducing bias.

In our experiment, participants complete three widely used experimental tasks—a Bayesian updating task, a real-effort task, and a risk preference elicitation task—under either a well-designed or a poorly designed interface condition. The two versions differ along several dimensions: text presentation (e.g., inconsistent fonts and typos), visual layout (e.g., misaligned elements and clashing colors), input methods (e.g., inconsistent use of keyboard and mouse), and responsiveness (e.g., unnecessary confirmation steps). In addition, this design allows us to examine both involuntary effects (e.g., distraction or cognitive load) and voluntary reactions (e.g., reduced effort or strategic disengagement).

The project aims to inform best practices for experimental design in economics and behavioral sciences, particularly in online settings where UI issues are both more likely and more consequential. By identifying the behavioral impact of seemingly superficial design flaws, we seek to promote greater attention to interface quality as a key determinant of data validity.