北大经院工作坊第1207场
House Prices, Land Finance, and the Coordination of Monetary and Fiscal Policies in China
（房价，土地财政和中国财政货币政策协调）
宏观经济学工作坊
主讲人：欧声亮 (上海交通大学安泰经济与管理学院副教授)
主持老师：（北大经院）韩晗
参与老师：
（北大国发院）赵波、余昌华、李明浩
（北大经院）陈仪、李博、李伦
时间：2025年12月11日（周四）14:00-15:30
地点：北京大学国家发展研究院承泽园229教室
主讲人简介：
欧声亮，上海交通大学安泰经济与管理学院副教授，博士毕业于庞培法布拉大学，主要研究领域为宏观经济学与货币经济学，研究成果发表在Journal of Monetary Economics, Journal of the European Economic Association, 《经济学（季刊）》等学术期刊，主持国家自然科学基金、上海市晨光计划等研究项目。
摘要：
China’s fiscal framework is characterized by land finance where local governments heavily depend on land sales revenue. Using a quantitative New Keynesian model that incorporates this salient institutional feature, we examine the impact of housing market shocks on the Chinese economy and assess the effectiveness of coordinated monetary and fiscal policies in mitigating these effects. Our findings reveal that policies aimed at inflating away debt can substantially dampen the effects of these shocks on GDP, house prices, and the debt-to-GDP ratio. These findings hinge on the interaction between China’s land-based fiscal framework and its monetary and fiscal policies.
北大经院工作坊第1208场
一、The Impact of Pollution on Intra-city Population Distribution—Based on Evidence of Government Relocation
二、财政压力下的地方政府行政处罚行为
国际经济学与实证产业组织工作坊
主讲人：
一、梁文泉（同济大学经济与管理学院副教授）
二、唐为（上海财经大学财税投资学院财政系副教授）
主持老师：（北大经院）莫家伟
参与老师：
（北大经院）杨汝岱、田巍、刘政文、吴群锋
（北大新结构）王歆、徐铭梽
（北大国发院）薛思帆
时间：2025年12月12日（周五）9：30-11：30
地点：北京大学经济学院305会议室
一、题目：The Impact of Pollution on Intra-city Population Distribution—Based on Evidence of Government Relocation
主讲人简介：
梁文泉，复旦大学经济学博士，现为同济大学经济与管理学院副教授。长期致力于研究劳动力空间分布的起因和影响，主要关注环境污染、人口流动、人力资本和城市发展。目前已在AEJ、IER、JEEM、JEBO、CER等国际期刊以及《中国社会科学》、《经济研究》、《管理世界》、《经济学（季刊）》等国内期刊发表论文。研究成果曾获《经济学（季刊）》年度最佳论文、上海青年经济学者优秀成果一等奖。主持国家自科基金青年和面上项目等多项省部级以上课题。
摘要：
Using the Chinese Industrial Enterprise Pollution Database and county-level statistical data from 1998 to 2014, this study examines the impact of pollution emissions and the distribution of pollution within cities on the population. To address endogeneity issues, the study also employs the relocation of city government offices from 2000 to 2016 as an exogenous shock to construct a staggered Difference-in-Differences (DID) model for identifying the effects of pollution on the population in upwind areas within cities. The results show that an increase in pollution emissions in a county significantly decreases its resident population, and the distribution of pollution within a city also affects the population in different counties within the city. After the relocation of government offices, pollution in upwind areas significantly increases while the population significantly decreases. Mechanism analysis suggests that after the relocation, firms in upwind areas reduce pollution facilities, increase pollution emissions, and experience an increase in land allocation for heavy industries. Further investigation reveals that young individuals with higher education levels are less tolerant of the spread of pollution in upwind areas within cities, leading them to choose to move away from cities with higher pollution emissions in these areas
二、题目：财政压力下的地方政府行政处罚行为
主讲人简介：
唐为，上海财经大学财税投资学院财政系副教授、系主任、博士生导师，研究方向为公共经济学、城市经济学，在《经济研究》、《经济学（季刊）》、Journal of Development Economics, Journal of Urban Economics, Regional Science and Urban Economics, China Economic Review, Journal of Real Estate Finance and Economics等中英文期刊发表论文多篇。主持国家自科基金面上与青年项目、教育部人文社科基金规划项目和青年项目等多项课题。入选或荣获上海市东方英才计划、上海市哲社优秀成果奖、洪银兴经济学奖、《经济学（季刊）》2021年度最佳论文等。
摘要：
本文以2016年“营改增”全面推行为财政压力的外生冲击，基于 2014—2021年行政处罚微观数据，系统考察财政压力对地方政府行政处罚行为的影响及其经济后果。“营改增”导致地方在增值税与营业税上的可支配收入下降，在财力收缩背景下，地方政府更倾向以增加罚没收入来弥补收入缺口；同时，财政压力下针对企业的处罚在合法性与合理性方面均出现偏离。异质性分析显示，在执法公开性较低、信息透明度不足、偿债压力较高且舆论监督薄弱的城市，财政压力对企业高额处罚的推动效应更为突出。压力驱动下的行政处罚虽可在短期内补充地方财力，但会提高企业退出、抑制跨地区资本流入，造成当地营商环境的恶化。
北大经院工作坊第1209场
Prediction when factors are weak
计量、金融和大数据分析工作坊
主讲人：张大可（上海交通大学安泰经济与管理学院助理教授）
主持老师：（北大经院）王一鸣、王法
参与老师：（北大经院）王熙、刘蕴霆、巩爱博
时间：2025年12月12日（周五）10:00-11:30
地点：北京大学经济学院219会议室
主讲人简介：
Dake Zhang is an Assistant Professor at the Antai College of Economics and Management, Shanghai Jiao Tong University. His research focuses on asset pricing, factor models, and reinforcement learning, with related work published in the Journal of Finance. His research has also been presented at major conferences such as the AFA, WFA, and the NBER Time Series Conference. At SJTU, he teaches courses including Financial Risk Management and Blockchain and Digital Currency. Dake earned his Ph.D. in Econometrics and Statistics from the University of Chicago Booth School of Business, and holds a Master’s degree in Statistics from the University of Chicago and a Bachelor’s degree in Mathematics from Tsinghua University.
摘要：
In economic forecasting, principal component analysis (PCA) has been the most prevalent approach to the recovery of factors, which summarize information in a large set of predictors. Nevertheless, the theoretical justification of this approach often relies on a convenient and critical assumption that factors are pervasive. To incorporate information from weaker factors, we propose a new prediction procedure based on supervised PCA, which iterates over selection, PCA, and projection. The selection step finds a subset of predictors most correlated with the prediction target, whereas the projection step permits multiple weak factors of distinct strength. We justify our procedure in an asymptotic scheme where both the sample size and the cross-sectional dimension increase at potentially different rates. Our empirical analysis highlights the role of weak factors in predicting inflation, industrial production growth, and changes in unemployment.
北大经院学术午餐会
第208期
Behavioral Personalization and Algorithmic Explainability: Evidence from a Field Experiment on Robo-Advising
主讲人：江嘉骏（复旦大学经济学院副教授）
主持老师：高明（北京大学经济学院长聘副教授）
时间：2025年12月12日（周五）12:30-14:00
地点：北京大学经济学院107会议室
主讲人简介：
江嘉骏，复旦大学经济学院副教授。北京大学光华管理学院金融学博士，数学与应用数学学士。主要研究领域为投资者行为偏误、智能投顾、机器学习与资产定价等。主持国家自然科学基金、上海市哲学社会科学规划课题等研究项目，研究成果在《经济研究》《金融研究》《管理科学学报》和Journal of Economic Behavior & Organization、Journal of Money, Credit and Banking等国内外权威期刊发表。
摘要：
Algorithmic financial advice has the potential to improve investment performance but often suffers from low adoption. We conduct a large-scale field experiment in partnership with a major commercial bank in China to provide the first causal evidence on how behaviorally informed personalization and algorithmic explainability affect investor take-up of robo-advice. We find that a simple rule-based algorithm that tailors recommendations based on elicited behavioral traits including risk and loss aversion significantly increases the likelihood of investing in the recommended asset category by 8.11% relative to a standard Sharpe ratio–maximizing baseline. Providing investors with feedback on their elicited preferences, along with an explanation of how these inform the recommendation, also increases uptake by 14.24%. Similar significant increases are observed in both the amount held in the recommended asset type and its share in the overall portfolio. These effects are shown to be persistent over time. Our findings highlight the value of incorporating behavioral preferences and explainability in promoting more inclusive adoption of robot advice.
供稿：科研与博士后办公室
美编：初夏
责编：度量、雨禾、雨田
