LI WENCONG

National University of Singapore

+65 9877 5176 |Bli wenc@nus.edu.sg |Personal Website

EMPLOYMENT

National University of Singapore Singapore

Research Fellow, NUS Behavioral Lab Manager 2024 – Present

EDUCATION

National University of Singapore (NUS) Singapore

Ph.D. in Economics 2019 – 2024

The University of Nottingham Ningbo China (UNNC) Ningbo, China

B.S. in International Economics & Trade (First Class) 2015 – 2019

University of Nottingham Nottingham, United Kingdom

Student Exchange Program (GPA: A+) 2018

JOB MARKET PAPER

“Who Shares More Fake News? Moral Judgment Explains Partisan Asymmetries in the Spread of

Misinformation”. (working paper, with Alessandro del Ponte and Noah Lim)

•We aim to examine whether individuals knowingly choose to share fake news, and how this choice

is shaped by political identity and ideological congruence.

•Abstract:

This study investigates how political partisanship shapes the sharing of misinformation within

a polarized media environment. Using a preregistered, incentive-compatible experiment with a

nationally representative quota sample of Americans (N = 3,065), we exposed participants to

political news headlines varying in veracity and party beneﬁt in a 2 (veracity: true or false) by 2

(party beneﬁt: help or hurt) between-subject design. Participants assigned to the role of Senders

decided whether to share these headlines with Receivers. Subsequently, Receivers played a Dictator

Game with Senders where they chose whether to send a reward. All participants expressed their

moral judgments about sending the news and answered questions about their partisanship, ideology,

and demographics. We ﬁnd that Republicans were signiﬁcantly more likely than Democrats to share

fake news, regardless of whether it would help or hurt their party. A deeper analysis reveals that

strong partisans were the most likely to share fake and helpful news, whereas weak partisans were

the most likely to share true and hurtful news. Critically, we ﬁnd that these behavioral differences

are underpinned by distinct moral judgments. Democrats consistently expressed stronger moral

condemnation of misinformation, while Republicans exhibited heightened moral condemnation of

true information that challenges their in-group. This ﬁnding suggests that moral judgment is a key

mechanism behind partisan asymmetries in information sharing, with partisan strength amplifying

these differences. These ﬁndings enhance our understanding of how political identity and moral

concerns inﬂuence the spread of misinformation and its impact on civic discourse.

RESEARCH PAPER

“Perceived Correlations in Risk Attitudes”. (working paper, with Jingcheng Fu and Songfa Zhong)

•We study people’s capacity to “proﬁle” others’ risky choices and examine how well people can pre-

dict the inter-correlation between decisions.

•Abstract:

This study explores the perception of correlations in risky choices within and across domains

with different probabilities and outcomes. In an experimental setting, participants make risky

choices and subsequently estimate the correlation between these choices among their peers. We

ﬁnd that participants generally perceive within-domain correlations as positive, albeit signiﬁcantly

overestimated compared to actual correlations, and cross-domain correlations to be closer to reality

but exhibit considerable variability. Utilizing these empirical observations alongside a theoretical

framework, we show that individuals tend to underestimate the size of noise in within-domain

correlations and inadequately account for it in cross-domain evaluations. Our results support the

notion that individuals use simpliﬁed mental models that overlook the role of noise in predicting

choice behavior.

“Loss Aversion: Loss Looms Larger or Gain Looms Smaller?”. (working paper, with Songfa Zhong)

•This project reﬁnes our understanding of loss aversion’s boundary in people’s decisions under risk.

•Abstract:

Loss aversion is a critical contribution of Prospect Theory to the study of decision-making. However,

previous research on the construct of loss aversion in de- cisions under risk unveils mixed results.

Some studies provide evidence for loss aver- sion, whereas others show a tendency for gains to

be the same impactful as losses. We employ two representations, high-outcome-ﬁrst framed and

low-outcome-ﬁrst framed questions, to explore this discrepancy. We ﬁnd that the reﬂections are con-

sistent with the fourfold pattern of risk attitudes in both treatment groups. That is, the outcome

framing does not affect the persistence of the fourfold pattern. We further note that the extent of

loss aversion is indeed greater when negative outcomes are presented ﬁrst in the mixed prospects

as opposed to when positive outcomes take precedence, though to a modest extent. Our study

contributes valu- able insights to the ongoing discourse on whether loss aversion is contingent upon

contextual factors.

“Information Resilience”. (joint with Tan Kai Lin Lynn and Noah Lim)

•Book-chapter, forthcoming at the Oxford Handbook of Misinformation and Disinformation

TEACHING EXPERIENCE

National University of Singapore

EC3101 Microeconomic Analysis II Singapore

Teaching Assistant, Undergraduate Level 3 Fall 2019 & Spring 2021

•Assisted professor with providing undergraduates with a thorough understanding of the core con-

cepts and methods of microeconomics;

•Introduced a set of mathematical and analytical tools to students in tutorials, as a foundation for

more advanced undergraduate electives which require a microeconomics background;

•Offered physical ofﬁce hours to support student learning outside of regular sessions.

EC3303 Econometrics I Singapore

Teaching Assistant, Undergraduate Level 3 Fall 2021 - Spring 2024

•Deeply explained to students about the theoretical underpinnings, practical implementation and

interpretation of multiple regression model results in cross-sectional data settings;

•Instructed undergraduate students in the utilization of programming, speciﬁcally Stata, for the pur-

pose of data analysis;

•Developed and shared digital resources, including tutorial slides and interactive materials;

•Offered both online and physical ofﬁce hours to support student learning outside of regular ses-

sions.

EC4394 Behavioral Economics Singapore

Teaching Assistant, Undergraduate Level 4 Fall 2020

•Aided in teaching students how to integrate psychology into economic behavior and apply behav-

ioral economics principles to a range of economic and business contexts;

•Aided in the development of in-class surveys (via Qualtrics online survey platform) and conducted

an analysis of undergraduate students’ risk attitudes based on those data;

•Actively engaged in grading assignments, examinations, and worked as student presentation ref-

eree.

EC6884 Behavioral and Experimental Economics Singapore

Teaching Assistant, Senior Graduate Level Spring 2022

•Supported graduate students in maintaining detailed records of their progress and provided timely

feedback;

•Actively engaged in grading assignments and worked as student presentation referee;

•Collaborated closely with professor in the planning and execution of class activities.

RESEARCH EXPERIENCE

Institution for New Structural Economics, Peking University

& Centre for Inclusive Finance, Ningbo, China

& Ningbo Development and Planning Institute Ningbo, China

Undergraduate Research Assistant June 2018 –July 2018

•Proﬁciently conducted ﬁeldwork, collected data, and performed basic archive analysis as an Investi-

gator.

AWARDS & ACHIEVEMENTS

Graduate Teaching Fellowship (GTF): Awarded to outstanding PhD candidates of NUS.

Tuition Fee Allowance (TFA): Awarded to outstanding PhD candidates of NUS.

PhD Research Incentive Award: Awarded to outstanding PhD economics candidates of NUS.

Dean’s Excellent Scholarship: Conferred upon undergraduate students who have achieved a ranking

within the top 10% of Economics at UNNC.

SKILLS

Programming: Stata, LaTeX, Qualtrics

Languages: English (Professional), Mandarin Chinese (Native)

Software: Microsoft (Word, Excel, PowerPoint, Outlook, et al.)