北大经院工作坊第1116场
Estimation and Inference in Boundary Discontinuity Designs
计量、金融和大数据分析工作坊
主讲人：Matias D. Cattaneo（Princeton）
主持老师：（北大经院）王一鸣、巩爱博
参与老师：（北大经院）刘蕴霆、王法、王熙
时间：2025年5月30日（周五）10:00-11:30
地点（线下）：北京大学经济学院606会议室
主讲人简介：
Matias D. Cattaneo is a Professor of Operations Research and Financial Engineering (ORFE) at Princeton University, where he is also an Associated Faculty in the School of Public and International Affairs(SPIA), the Department of Economics, and the Program in Latin American Studies(PLAS), and an Affiliated Faculty in the Data-Driven Social Science (DDSS) initiative, the AI at Princeton initiative, and the Center for Statistics and Machine Learning (CSML). His research spans econometrics, statistics, data science, and decision science, with applications to program evaluation and causal inference. His work is interdisciplinary, and often motivated by quantitative problems in the social, behavioral, and biomedical sciences. His research often integrates nonparametric, semiparametric, high-dimensional, and machine learning methods to develop robust estimation and inference techniques.Matias is an elected Fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the International Association for Applied Econometrics. He serves on the editorial boards of leading journals, including Journal of the American Statistical Association, Econometrica, Operations Research, Statistical Science, the Econometrics Journal, the Journal of Econometrics, Econometric Theory, and the Journal of Causal Inference. He is also an Amazon Scholar and has advised numerous governmental, multilateral, non-profit, and private organizations around the world.
报告摘要：
Boundary Discontinuity Designs are used to learn about treatment effects along a continuous boundary that splits units into control and treatment groups according to a bivariate score variable. These research designs are also called Multi-Score Regression Discontinuity Designs, a leading special case being Geographic Regression Discontinuity Designs. We study the statistical properties of commonly used local polynomial treatment effects estimators along the continuous treatment assignment boundary. We consider two distinct approaches: one based explicitly on the bivariate score variable for each unit, and the other based on their univariate distance to the boundary. For each approach, we present pointwise and uniform estimation and inference methods for the treatment effect function over the assignment boundary. Notably, we show that methods based on univariate distance to the boundary exhibit an irreducible large misspecification bias when the assignment boundary has kinks or other irregularities, making the distance-based approach unsuitable for empirical work in those settings. In contrast, methods based on the bivariate score variable do not suffer from that drawback. We illustrate our methods with an empirical application. Companion general-purpose software is provided.
北大经院工作坊第1117场
Good Data and Bad Data:The Welfare Effects of Price Discrimination
微观理论经济学工作坊
主讲人：Nima Haghpanah（Associate Professor of Economics at Penn State）
主持老师：
（北大经院）吴泽南、石凡奇
（北大国发院）胡岠
参与老师：
（北大经院）胡涛
（北大国发院）汪浩、邢亦青
（北大光华）翁翕、刘烁
时间：2025年5月29日（周四）10:30-12:00
地点：北京大学经济学院302会议室
主讲人简介：
Nima Haghpanah is an associate professor of Economics at Penn State. Before joining Penn State in 2016, he received his PhD in Computer Science from Northwestern University in 2014 and was a postdoctoral associate at MIT from 2014 to 2016. He studies mechanism design, information design, and price discrimination.
摘要：
We ask when additional data collection by a monopolist to engage in price discrimination monotonically increases or decreases weighted surplus. To answer this question, we develop a model to study endogenous market segmentation subject to residual uncertainty. We give a complete characterization of when data collection is good or bad for surplus, which consists of a reduction of the problem to one with only two demand curves, and a condition for the two-demand-curves case that highlights three distinct effects of information on welfare. These results provide insights into when data collection and usage for price discrimination should be allowed.
北大经院工作坊第1118场
The Rise of Platform and The Fall of Traditional Economy
数字经济工作坊
主讲人：古定威（复旦大学管理学院副教授）
主持老师：（北大经院）曹光宇
时间：2025年5月30日（周五） 10:00-11:30
地点：北京大学经济学院302会议室
主讲人简介：
Dingwei Gu is an associate professor of economics at the School of Management, Fudan University. His research interests focus on industrial organization and corporate finance, covering platform economics, digital economics, vertically related industries, and antitrust. His works have been published in the RAND Journal of Economics, Journal of Industrial Economics, and Journal of Corporate Finance.
摘要：
Platforms have become increasingly important to the global economy. In this paper, we study the impacts of a platform on the traditional economy where the merchants and consumers can conduct business even without the platform. By working as an additional valuable channel, the platform benefits consumers and society, but hurts merchants; more so if the platform becomes more valuable. The proportion of on-platform merchants is decreasing in platform value and merchant competition. In the traditional economy with platform, a more intense merchant competition can be harmful to consumers and society.
北大经院工作坊第1119&1120场
The Epistemology of Risk Sciences: Cross-Disciplinary Definitions and Methodologies
风险、保险与不确定性经济学工作坊
主讲人：
冯润桓（清华大学经济管理学院）
贾若（北京大学经济学院）
李建平（中国科学院大学经济与管理学院）
栾胜华（中国科学院心理研究所）
庞珣（北京大学国际关系学院）
魏玖长（中国科学技术大学公共事务学院）
徐建华（北京大学环境科学与工程学院）
朱建明（中国科学院应急管理科学与工程学院）
主持老师：
（清华经管）冯润桓
（北大经院）贾若
（人大财金）陈泽
参与老师：
（人大财金）魏丽
（人大财金）许荣
（北大经院）郑伟
时间：2025年5月30日（周五）15:00-18:00
地点：清华大学经济管理学院李华楼B502教室
内容：
本次研讨会特邀来自8个不同学科的专家，研讨各自领域研究框架、核心观点、方法论，随后开展讨论与问答。本讨论会旨在促进跨学科交流，分享不同领域关于风险科学研究的基本思想。同时，希望通过本讨论会推动合作研究，拟在讨论会内容基础上，共同撰写一篇跨学科综述论文，为不同背景学者提供风险科学研究的"全景式导引"。
北大经院工作坊第1121场
Cross-border Spillover Effects of Dams along International Rivers
发展与公共财政工作坊
主讲人：雷宇翔（香港科技大学助理教授）
参与老师：
（北大经院）刘冲、吴群锋、曹光宇、年永威
（北大国发院）李力行、席天扬、徐化愚、于航、王轩、易君健、黄清扬
（北大光华管理学院）张晓波、仇心诚
时间：2025年5月28日（周三）10:30-12:00
地点：北京大学国家发展研究院承泽园245教室
主讲人简介：
Yu-Hsiang Lei is an Assistant Professor of Economics at the Hong Kong University of Science and Technology. He obtained his PhD in Economics from the London School of Economics and received undergraduate education from National Taiwan University. He is a development economist, and his research interests also span the fields of political economy and public economics, with a special regional emphasis on China. His work mainly focuses on producing empirically grounded evidence to address critical challenges faced by developing countries and to provide insights to understand broader development and governance issues.
摘要：
Hydropower dams on international rivers often trigger disputes due to neglecting externalities in downstream countries. Focusing on Chinese dams on the upper Mekong River, this paper examines the cross-border impacts of dams. Employing machine learning and difference-in-difference methods, I find that dam spillover effects depend on climate conditions. Under normal conditions, dams smooth water levels across seasons, boost agricultural productivity, and reduce large-scale fires. However, during extreme weather, dams' operations conflict with downstream interests, turning externalities negative and exacerbating damages in downstream countries. With the increasing frequency of extreme weather events, the negative cross-border impact may dominate.
北大经院工作坊第1122场
Deceptive Online Networks in the US 2020 Elections
发展与公共财政工作坊
主讲人：Jennifer Pan（Sir Robert Ho Tung Professor of Chinese Studies, Professor of Communication, Stanford University）
参与老师：
（北大经院）刘冲、吴群锋、曹光宇、年永威
（北大国发院）李力行、席天扬、徐化愚、于航、王轩、易君健、黄清扬
（北大光华管理学院）张晓波、仇心诚
时间：2025年5月28日（周三）14:00-15:30
地点：北京大学国家发展研究院承泽园249教室
主讲人简介：
Jennifer Pan is a political scientist whose research focuses on political communication and digital media. She is the Sir Robert Ho Tung Professor of Chinese Studies, Professor of Communication and a Senior Fellow at the Freeman Spogli Institute at Stanford University. Dr. Pan's research uses experimental and computational methods with large-scale datasets to answer questions about the role of digital media in politics. Her work has appeared in peer-reviewed publications such as the American Political Science Review, American Journal of Political Science, Journal of Politics, Science, and Nature. She graduated from Princeton University, summa cum laude, and received her Ph.D. from Harvard University’s Department of Government.
摘要：
Deceptive online networks are coordinated efforts that use identity deception to pursue strategic political or financial goals. During the US 2020 elections, these networks reached at least 37 million Facebook and 3 million Instagram users, representing 15% and 2% of the platforms' active US adult users, respectively. Only 3 networks out of 49--1 network with explicitly political aims and 2 that appeared to use politics as a lure for profit---were responsible for over 70% of users reached. Notably, accounts unaffiliated with the networks played a significant role in facilitating this reach by resharing content the three networks produced. Deceptive networks, regardless of whether their goals were political or financial, reached users who were older, more conservative, more frequently exposed to content from untrustworthy sources, and spent more time on Facebook.
供稿单位：科研与博士后办公室
