北大经院工作坊第788场
The Economic Impact of Heritable Physical Traits: Hot Parents, Rich Kid?
经院-全健院
“健康与劳动经济学”工作坊
主讲人：张安文（Senior Lecturer in Economics at the University of Glasgow）
主持老师：（北大经院）秦雪征
参与老师：
（北大全健院）刘国恩、吕蓓妮、潘聿航、林昊翔、孙宇、杨佳楠
（北大经院）石菊、姚奕、王耀璟、袁野、梁远宁、庄晨、Kevin Devereux
时间：2023年12月11日（周一）10：00-11：30
地点：北京大学经济学院107会议室
主讲人简介：
Anwen Zhang is an applied microeconomist and currently Senior Lecturer in Economics at the University of Glasgow. His research focuses on policy-relevant issues related to health and education. His recent work aims to understand what matters for mental health. His publications appear in journals across multiple disciplines, including Journal of Political Economy, Proceedings of the National Academy of Sciences, The Lancet, and Social Sciences & Medicine. Anwen is affiliated with the Global Labor Organization as a GLO Fellow. He held a Visiting Fellowship at the London School of Economics and Political Science (LSE). Prior to joining the University of Glasgow, he held research positions at the LSE and University of Cambridge. He received his PhD in Economics from Lancaster University.
摘要：
Since the mapping of the human genome in 2004, biologists have demonstrated genetic links to the expression of several income-enhancing physical traits. To illustrate how heredity produces intergenerational economic effects, this study uses one trait, beauty, to infer the extent to which parents' physical characteristics transmit inequality across generations. Analyses of a large-scale longitudinal study in the U.S., and a much smaller data set of Chinese parents and children, show that a one standard-deviation increase in parents' looks is associated with a 0.4 standard-deviation increase in their child's looks. A large data set of U.S. siblings shows a correlation of their beauty consistent with the same expression of their genetic similarity, as does a small sample of billionaire siblings. Coupling this estimate with parameter estimates from the literatures describing the impact of beauty on earnings and the intergenerational elasticity of income suggests that one standard-deviation difference in parents' looks generates a 0.06 standard-deviation difference in their adult child's earnings, which amounts on average to additional annual earnings in the U.S. of about $2300.
《行业研究前沿》课程系列
2023年第8期
变局——百年、十年、三年
主讲人：张瑜（华创证券研究所副所长、宏观经济研究主管、首席宏观分析师）
主持人：锁凌燕（北京大学经济学院教授）
时间：2023年12月12日（周二）18：40-20：30
形式：
腾讯会议，会议号：559-192-645
主讲人简介：
张瑜，华创证券研究所副所长、宏观经济研究主管、首席宏观分析师。
兼任中国人民大学国际货币研究所研究员，中国人民大学财税研究所兼职研究员，澳门城市大学经济研究所特约研究员，中国金融四十人论坛（CF40）青年论坛会员，中国保险资产管理业协会专委会特邀研究员及资管百人，清华大学、中国人民大学、复旦大学、上海财经大学等EMBA与MBA客座讲师。
作为首席带队连续多年获得资本市场奖项。2019至2021年，连续多年获新财富最佳分析师、水晶球最佳分析师、新浪金麒麟最佳分析师、上证报最佳宏观经济分析师、金牛最具价值分析师、21世纪金牌分析师、Wind金牌分析师及路演领军人物等奖项；2022年最新获奖详情：新财富最佳分析师第三名、水晶球最佳分析师第二名、上证报最佳分析师第二、中证报最佳分析师第二、新浪金麒麟最佳分析师第三。
主办单位：
北京大学经济学院
北京大学中国保险与社会保障研究中心
北京大学中国金融研究中心
北大经院工作坊第789场
Robust Federated Learning of Causal Effects
经院-全健院
“健康与劳动经济学”工作坊
主讲人：Larry Han (Assistant Professor of Biostatistics and Health Sciences, Northeastern University)
主持老师：（北大全健院）潘聿航
参与老师：
（北大全健院）刘国恩、吕蓓妮、林昊翔、孙宇、杨佳楠
（北大经院）秦雪征、石菊、姚奕、王耀璟、袁野、梁远宁、庄晨、Kevin Devereux
时间：2023年12月13日（周三）10：00-11：30
形式：ZOOM会议
会议号：642 873 1122
密码：fEepb4
主讲人简介：
Larry Han is an Assistant Professor in the Department of Health Sciences at Northeastern University, as well as an Impact Engine Fellow of the Real-World Healthcare Navigator. His research focuses on developing novel statistical and machine learning methods to leverage real-world data to improve decision-making, including robust and efficient estimation and inference of treatment effects using large-scale data generated from electronic health records and clinical trial data. Active areas of research include causal inference, conformal prediction, data integration, federated and transfer learning, and sensitivity analysis.
He obtained his PhD in Biostatistics at Harvard University, advised by Professor Tianxi Cai and Professor Lorenzo Trippa. He completed a postdoctoral fellowship in Health Care Policy at Harvard Medical School, advised by Professor Sharon-Lise Normand. He received an AM in Biostatistics from Harvard, an MPhil in Healthcare Operations from the University of Cambridge, an MA in Global Affairs from Tsinghua University, and a BS in Public Health and Biostatistics from UNC - Chapel Hill.
摘要：
Federated or multi-site studies have distinct advantages over single-site studies, including increased generalizability, the ability to study underrepresented populations, and the opportunity to study rare exposures and outcomes. However, these studies are challenging due to the need to preserve the privacy of each individual's data and the heterogeneity in their covariate distributions. We propose a novel federated approach to derive valid causal inferences for a target population using multi-site data. We adjust for covariate shift and covariate mismatch between sites by developing multiply-robust and privacy-preserving nuisance function estimation. Our methodology incorporates transfer learning to estimate ensemble weights to combine information from source sites. We show that these learned weights are efficient and optimal under different scenarios. We showcase the finite sample advantages of our approach in terms of efficiency and robustness compared to existing approaches. Finally, we showcase the utility of our methodology for estimating COVID-19 vaccine efficacy (Moderna vs. Pfizer) across geographic regions, and variations in congenital heart surgery quality across racial/ethnic groups. Our findings have implications for the efficient allocation of scarce resources.
Paper 1: Multiply Robust Federated Estimation of Targeted Average Treatment Effects (2023), NeurIPS. https://arxiv.org/pdf/2309.12600.pdf
Paper 2: Privacy-Preserving, Communication-Efficient, and Target-Flexible Hospital Quality Measurement (2023+), Annals of Applied Statistics. https://arxiv.org/pdf/2203.00768.pdf
Paper 3: Federated Adaptive Causal Estimation (FACE) of Target Treatment Effects (2023+), Invited revision at JASA. https://arxiv.org/pdf/2112.09313.pdf
北大经院工作坊第790场
Incomplete Property Rights and Farm Size: Evidence from Haiti
经济史工作坊
主讲人：Craig Palsson (Utah State University)
主持老师：（北大经院）赵一泠、Mark Hup
参与老师：
（北大经院）郝煜、管汉晖、周建波
（北大光华）颜色
（北大国发院）席天扬、于航
时间：2023年12月13日（周三）16:00-17:00
地点：北京大学经济学院305会议室
主讲人简介：
Craig Palsson is an Assistant Professor of Economics in the Huntsman School of Business at Utah State University. He is interested in how historical political economy affects modern underdevelopment, with a special interest in the economic history of Haiti. He has published in journals such as the Journal of Public Economics, the Journal of Urban Economics, and the Journal of Economic History. He is also the creator of the popular Market Power channel on YouTube.
摘要：
We investigate the connection between incomplete property rights and plot size. Incomplete property rights create transaction costs in the land market, which should have two effects on plot size: transaction costs (1) decrease plot size and (2) increase the variation in plot sizes. We test these hypotheses using newly collected archival data from Haiti. We measure incomplete property rights using Haiti’s tradition of families jointly owning land and find that the patterns in the data are consistent with these property rights creating large transaction costs in the land market. These results inform the discussion of small farms around the developing world and of Haiti’s economic development.
供稿：科研与博士后办公室
美编：初夏
责编：度量、雨禾、雨田
