Abstract：(Tentative) We draw on new granular data from cities around the world to study how the spatial distribution of income within cities varies with development. We document that in less-developed countries, average incomes of urban residents decline monotonically in distance to the city center, whereas income-distance gradients are flat or increasing in developed economies. We also show that urban neighborhoods with natural amenities – in hills and near rivers – are poorer than average in lessdeveloped countries and richer than average in developed ones. We hypothesize that these patterns arise due to the differences in the provision of residential and transportation infrastructure within cites. Using a quantitative urban model, we show that observed differences in residential and transportation infrastructure help explain a significant fraction of how the spatial income distribution within cities varies with income per capita.

Abstract: In one-shot delegation the principal optimally imposes an upper bound on actions when the agent is possibly upward-biased. In repeated delegation a biased type has greater incentive to signal her honesty in the early period than an honest type to induce the principal to relax the upper bound in the later period. Both types are locked in a signaling race and pool at downward-distorted actions in equilibrium. The optimal delegation set in the early period imposes a binding action lower bound despite the agent’s upward bias. The optimal action upper bound is less restrictive than that in a one-shot game.

発表内容：
　　『Academist における学術と社会をつなぐ多様なチャレンジ』
　　講演者：柴藤亮介 株式会社アカデミスト 代表取締役 CEO
　　『京都大学 iPS研究所におけるファンドレージングの歴史と展望』
　　講演者：渡邉文隆 公益財団法人京都大学 iPS細胞研究財団特命専門業務職員
　　（前同財団社会連携室長）

RLiu-short bio-112024

Abstract:
Advanced data acquisition technology nowadays has often made inferences from diverse data sources easily accessible. Fusion learning refers to fusing inferences from multiple sources or studies to make more effective overall inference. We focus on the tasks: 1) Whether/When to combine inferences? 2) How to combine inferences efficiently? 3) How to combine inference to enhance the inference for a target study? We present a general framework for nonparametric and efficient fusion learning. The main tool underlying this framework is the new notion of depth confidence distribution (depth-CD), developed by combining data depth, bootstrap and confidence distributions. We show that a depth-CD is an omnibus form of confidence regions, whose contours of level sets shrink toward the true parameter value, and thus an all-encompassing inferential tool. The approach is efficient, general and robust, and readily applies to heterogeneous studies with a broad range of complex settings. The approach is demonstrated with an aviation safety analysis application in tracking aircraft landing performance.

This is joint work with Dungang Liu (U. Cincinnati) and Minge Xie (Rutgers University).

Abstract：The Asia and Pacific region has enjoyed rapid economic and human development gains in the past three decades. Though it has benefited from demographic tailwinds, investment and productivity growth are the key to these gains. The critical role of structural transformation, that is, workers moving out of agriculture into other, higher-productivity sectors in achieving productivity growth, is often underappreciated. Movement into manufacturing in particular, helped by rapid international trade integration, has been a hallmark of the structural transformation in the region. However, services have played a bigger role across the region in the past three decades. Looking ahead, enabling continued transformation will be critical. As per capita incomes have risen, the move into services will likely become even more prominent. Ensuring a shift toward more productive services will require investment in education and training to supply the needed skills, especially to allow workers to adapt to the wave of new technologies, including AI. Continued international integration in services would be key, with an eye on boosting tradability and competition in services. In many economies, enhancing agricultural productivity will still be important for promoting transformation and growth, along with lowering barriers to workers and resources moving across sectors. Policies to raise labor force participation, especially among elderly workers and women, will be critical for mitigating the impact of population aging and decline in much of the region.