Can Relational Contracts Survive Stochastic Interruptions?

This paper investigates the robustness of the “two-tiered labor market” experimental results of Brown, Falk and Fehr (2004) by subjecting relationships to stochastic interruptions. Using two different subject pools, we first replicate the basic pattern of high quality private contracting and low quality public contracting. We then study the impact of exogenous random ‘downturns’ in which firms cannot hire workers for three periods. Our hypothesis is that 1. job rents are lower in downturns 2. this will lower wages and effort, unless strong re-connection norms exist. We do find that job rents are lower, but surprisingly, the downturns do not harm aggregate market efficiency. Stochastic interruptions delay the formation of relationships, necessitating the use of public offers, which increases the competitiveness of the short term market. The high tier (private) markets responds by raising wages, thus increasing average worker surplus per trade. We also find evidence that 50-50 pre-downturn worker-firm surplus sharing predicts post-downturn re-connections

Prediction Markets: Alternative Mechanisms for Complex Environments with Few Traders

Double auction prediction markets have proven successful in large-scale applications such as elections and sporting events. Consequently, several large corporations have adopted these markets for smaller-scale internal applications where information may be complex and the number of traders is small. Using laboratory experiments, we test the performance of the double auction in complex environments with few traders and compare it to three alternative mechanisms. When information is complex we find that an iterated poll (or Delphi method) outperforms the double auction mechanism. We present five behavioral observations that may explain why the poll performs better in these settings

Community-centric Building of Digital Infrastructures Against Systemic Oppression: G2A police misconduct complaint support system

Digital infrastructure can make visible the problems hidden in policies that maintain systematic racism and oppression. However, these infrastructures are difficult to design because the technology workers that build them are often too far removed from those with lived experiences of oppression. Grief to Action pPUC is a novel community data-science engagement network that is building an online platform to help citizens navigate the police accountability process. The tool will collect municipal level police union contracts and misconduct complaint processes in one place, allowing anyone the ability to search and compare these previously highly fragmented and opaque information across time and space. We build upon the work and experiences of our partners such as Campaign Zero, Take Action Mon Valley, CONNECT, Western Pennsylvania Regional Data Center, and Data for Black Lives

Community Conversations about Data Science for Social Justice

Scoping data projects with the community can be a rewarding and challenging experience. This workshop will discuss several examples of bringing community partners into the classroom to do data-related projects. We will discuss the timeline, project scope, output, time commitments, and expectations from various perspectives

Information and Motivation In Organizations

My research focuses on incentive/information design for environments where contract enforcement is difficult and the information required for decision-making is dispersed. These environments are particularly challenging when the number of participants are small enough such that small perturbations have persistent influences. In these three chapters, I use theory, computation, and experiment to investigate the robustness of several basic economic mechanisms to stochastic noise. The first chapter analyzes the basic unit of information aggregation – the Geanakoplos and Polemarchakis (1982) posterior revision process. I find that if stochastic noise is present, then 1) the posterior revision process does not reliably give public statistics that approach the full information posterior, and 2) methods exist to rank information structures based upon the likelihood that they produce good public statistics through the posterior revision process. The last two chapters address the impact of stochastic noise on labor markets. The chapter coauthored with Margaret McConnell uncovers the image motivation behind prosociality by enforcing privately known stochastic stopping time in volunteering sessions. A unique cascade of quitting behavior suggests that volunteers are partially driven by stigma avoidance. The third chapter, coauthored with Colin Camerer, analyzes the robustness of contracting relationships to exogenous disruptions caused by stochastic drops in demand. We find that stochastic noise slows the formation of relational contracts, but high-quality contracts remain unaffected.</p

From Grief to Action

The Center for Analytical Approaches to Social Innovation (CAASI) is actively building tools to elevate racial equity and support the effort to fight the systematic racism that has been laid bare by the deaths of George Floyd, Breonna Taylor, Ahmaud Arbery and countless more before them

Volunteering and image concerns

We design an experiment to analyze the impact of image concerns and material incentives on volunteering. Our design retains the advantages of laboratory control while incorporating field context by engaging subjects in an actual nonprofit’s operation. We find that working in a public setting significantly increases volunteering. Monetary incentives have little impact, although they are slightly more effective in a private setting. Our results suggest that organizations have more to gain by catering to volunteers’ image concerns than by providing monetary benefits