Welcome to Euncheol Shin's webpage
BIO: I am an assistant professor of economics at KAIST College of Business in South Korea. I am interested in microeconomics, behavioral & experimental economics, industrial organization, political economy, and social networks. I received my B.S. in mathematics and B.A. in economics from Yonsei University in South Korea. In 2016, I received my Ph.D. in the social sciences (equiv. Ph.D. in economics) from the California Institute of Technology, under the advisement of Professor Leeat Yariv. Subsequently, I was hired as an assistant professor of economics at Kyung Hee University in South Korea, and I have been at KAIST College of Business since 2018.
Address: 85 Hoegiro KAIST S210, Dongdaemun-gu, Seoul 02455, South Korea.
Email: eshin.econ [AT] kaist.ac.kr.
Abstract: We study a pandemic disease control problem in which an infectious disease like COVID-19 spreads over multiple communities and non-pharmaceutical interventions are employed to control the spread of the disease. Drawing upon a canonical susceptible-infected-recovered model, we formulate the problem as a discrete time, finite horizon, dynamic program. As a base model, we first develop an epidemic model in which one benevolent social planner uses non-pharmaceutical interventions to control the spread of disease within one community. We extend this base epidemic model to a more complex pandemic model that additionally considers the interactions between multiple communities. We derive the optimal policies for both epidemic and pandemic models and characterize their structural properties using analytical and computational approaches. For the pandemic model, we propose three different principles under which a social planner enforces intervention orders. We compare the optimal policies for the three different principles and discuss related managerial implications.
Abstract: We examine the optimal intervention of an influence designer in the presence of social learning in a network. Before learning begins, a designer implants opinions into the network to make agents' ultimate opinions as close as to target opinions. By decomposing the influence matrix, which summarizes the learning structure, we transform the designer's problem into one with an orthogonal basis: implanted opinions on one cluster of agents influence only another cluster's opinions with a multiplier effect. This transformation allows us to characterize the optimal intervention under both complete and incomplete information on the network structure.
Abstract: Promoting social capital has long been an important issue in social sciences. This paper argues that teaching practices can stimulate social capital at both the individual and the classroom levels by evaluating the impact of a student-centered teaching pedagogy program. We measure changes in students’ friendship network and directed altruism with comprehensive friendship surveys and incentivized dictator game experiments conducted before and after the intervention. We find that the project-based learning program positively affects social capital by expanding students’ friendship networks and being more generous toward their peers, especially those not in direct friendship and without homophilous traits. Moreover, structural estimations suggest that the program also reduces friendship formation costs among the students, especially for those studying in the same classroom. Our results support the idea that teaching practice focusing on student-centered learning can be considered an effective educational policy to support social capital formation among students.
Abstract: We evaluate a program wherein an adaptive learning technology and an increased amount of teacher engagement were applied to students’ learning processes. A quasi-experimental design was utilized to analyze the effectiveness of a semester-long program implemented during regular mathematics classes for Grade 7 students in Vietnam. The intervention resulted in overall positive and significant learning gains, along with moderate improvement in the students’ perception of teacher efficacy and classroom interaction. The learning gains were greater for students who lagged behind. These results point to a feasible and effective solution for achieving quality education.
Abstract: Game theory experiments sometimes neglect to provide normative information that is socially focal in non-Western cultures. In South Korea, perceptions of fairness and other social judgments typically depend upon age hierarchies. Using one-shot Stag Hunt and Prisoner’s Dilemma games, we test whether providing age information can increase coordination and cooperation among Korean participants – since knowing who is older can shift norms and expectations. We find that making relative ages common knowledge increased the probability of choosing the riskier but coordinating action “Stag” in the Stag Hunt by 15% (9 percentage points); it also increased the probability of choosing “Cooperate” in the Prisoner’s Dilemma by 16% (7 percentage points). In both games, age effects were largest when paired with someone older, and when conditioning on expecting a partner to play Stag or Cooperate; this is consistent with norms of reciprocity to older players. Revealing age in identical experiments in the US yielded 0% (<1 percentage point) changes. In addition, although the US had higher baseline rates of “Stag” and “Cooperate,” this difference lessened (in Stag Hunt) or disappeared altogether (in Prisoner’s Dilemma) with age information. Since outcomes without ages have low external validity in Korea, our results suggest that comparisons between the US and Korea that ignore age information may misestimate cultural differences in social choices. More broadly, researchers should exercise caution when copying experimental protocols across participant pools for between-culture comparisons of social preferences; these protocols may not be equally appropriate for proxying how each culture makes social preference evaluations.
Abstract: We investigate the effects of individuals' strategic sophistication on collective action behaviors in the context of social distancing during the early stage of the COVID-19 pandemic. First, we build a 2x2 game that models strategic but boundedly rational social-distancing behavior under the assumption that each player believes that the opponent's strategic sophistication level (level-k type) is lower than her. The game is a weakest-link public goods game with a private cost of social-distancing action. We find that under a reasonable assumption, a player is more likely to play the action representing social-distancing behavior as the player's level-k type is higher. Then, we empirically test this hypothesis using large-scale nationally representative survey data, including participants' level-k types measured by a common economic experiment. Our empirical findings provide robust evidence that is consistent with the theoretical prediction. This study contributes to the literature by shedding new light on the role of the level-k theory in understanding real-world collective action problems.
7. Network Bottleneck and Speed of Learning (joint with Jin Huang and Kyu-Min Lee). [In Preparation]
Abstract: We propose a technique to analyze the speed of learning in networks. We consider a learning model in which each agent updates his belief by taking an average of his and his neighbors' beliefs. The learning speed is defined as the time length required for agents' beliefs to be close enough to the long-run beliefs. Three results arise from our analysis. First, we find that the speed of learning can be bound by the bottleneck ratio of the underlying network, which is a geometric feature. Second, we analyze and compare learning speed in deterministic and random networks. Third, although the bottleneck ratio is not a tight bound, a series of numerical simulations confirm our theoretical results.
Abstract: Many countries have implemented public funding to help citizens participate in an election. We build a citizen-candidate model in which citizens can enter an electoral competition race for office as a political candidate. We consider a public funding program: The entrance is costly, but an entering candidate receives a subsidy that fully compensates the entrance cost if her vote share is higher than a qualification threshold. On the one hand, due to the full compensation, some citizens may enter the race even if they lose for sure in the election. On the other hand, competition becomes more intense because of those surely-losing candidates' presence. Consequently, electoral outcomes such as the winner's position substantially change. We characterize equilibrium under the consideration of public funding. Furthermore, we analyze how the public funding program affects the number of running candidates, their policy diversity, the winner's position, and voters' welfare.