Xin Jiang @ USC Welcome! I am a Postdoc Teaching Fellow in Economics at the University of Southern California. I obtained my Ph.D. in Economics from UC Santa Barbara, 2024. My research and teaching interests are Microeconomics, Experimental Economics and Behavioral Economics. Email: xjiang33 [at] usc.edu

Publication

• Fortune and Identity
  with Gary Charness, Economics Letters (2023). [Paper]
  [Slides] [Data] [Variables] [R code]
  
  While group identity can generate in-group bias, the topic of how activities generate group affiliation is largely unexplored. We experimentally study the effect of shared experience on group affiliation, varying shared experiences by paying subjects differently for the same task. The results show that shared fortune leads to in-group bias, while shared misfortune does not.

Working Paper

• Pinocchio's Clock: Using Response Time to Detect Deception in Sender-Receiver Games
  with Bohan Ye.
  
  The inclusion of response time stamps has become a common feature of contemporary social media sites and applications. What private information does the response time carry when there is a conflict of interest between users? Can people use it to improve their welfare in communication? We developed a framework and designed a sender-receiver experiment to examine the complex relationship between response time and private information in communication. We find a truth discovery preference, where senders tend to discover the truth before sending their messages. Given this preference, the response time reflects the deliberation process rather than the truth discovery process. Results show that deceptive messages are significantly longer than the honest ones. However, the inference from the RT is limited and receivers on average do not over-use RT for lying detection.

• Dynamic Binary Elicitation Method
  with Jing Zhou.
  
  Beliefs or perceptions play a central role in studying economic behavior, yet eliciting them accurately presents challenges. We introduce a novel elicitation method, called the Dynamic Binary Method (DBM), designed to address the common challenge individuals face in pinpointing the best point estimate of their beliefs, particularly when their beliefs are imprecise. Unlike Classical Methods (CM), which require respondents to form a point estimate of their true beliefs, DBM prompts respondents to make a series of binary relative judgments and enables them to express interval beliefs by exiting the process at any step. After the exit, we will randomly pick a number in the interval as their final beliefs.
  
  We conduct both within-subject and between-subject experiments using a diverse range of perception tasks drawn from previous literature and CM as a benchmark of performances in each task. We find that, at the aggregate level, DBM does not perform significantly differently from CM, regardless of whether the perception tasks involve artificial/laboratory settings or real-life scenarios, and irrespective of the performance metric used. However, DBM has an asymmetric effect relative to CM: individuals who performed poorly under CM tend to improve under DBM, while those who performed well under CM tend to do worse under DBM.

Teaching Certificate and Award

• Outstanding Undergraduate Course Teaching Assistant, 2023-2024, UCSB
  

• The Certificate in College and University Teaching, 2024, UCSB
  

Teaching Experience

• Co-Instructor, at USC
  Principals of Microeconomics, Spring 2025
  

• Instructor of Record, at UCSB
  Corporation Finance, Winter 2020, Spring 2020
  

• Head Teaching Assistant, at UCSB
  Intermediate Microeconomics, Spring 2023
  

• Teaching Assistant, at UCSB
  Corporate Finance, 3 quarters
  Intermediate Microeconomics, 3 quarters
  Introduction to Game Theory, 3 quarters
  Principles for Microeconomics, 3 quarters
  Principles for Macroeconomics, 1 quarter