Keynotes

Title: Information Experiment: What Does Empirical Microeconomics Tell Us?
Abstract:

This talk explores the field of empirical microeconomics, focusing on the importance of causal inference and its application in policy-making. I discuss the concept of evidence in empirical studies, emphasizing the distinction between causality and correlation. The talk highlights various methods of causal inference, including Randomized Controlled Trials (RCTs) and Natural Experiments, and their importance in Evidence-Based Policy Making (EBPM). Through a series of empirical studies, I examine how information experiments can influence decision-making and behavior. Key examples include addressing gender wage gaps, altering social norms regarding female labor force participation, and the impact of role models in educational and labor market outcomes. The talk also discusses the challenges in proving discrimination based on gender and race, and the effectiveness of interventions in mitigating these biases. By presenting these findings, the talk aims to underscore the critical role of information and causal inference in developing effective policies and understanding human behavior. The discussion will also touch upon the limitations and external validity of information experiments, advocating for the accumulation of evidence to support robust policy decisions.

   Momoe Makino is a senior research fellow at the Institute of Developing Economies, Japan External Trade Organization (IDE-JETRO). She is also a research fellow at Population Council, New York. She received Bachelor of Law at University of Tokyo in 1999, Master of International Relations at the Fletcher School, Tufts University in 2002, Ph.D. in Economics at the University of Washington in 2021. Her majors are development microeconomics, population economics, and household economics. Her research published in peer-reviewed journals includes “Dowry in the Absence of the Legal Protection of Women’s Inheritance Rights,” Review of Economics of the Household 17, issue 1 (2019): 287-321, “Marriage, Dowry, and Women’s Status in Rural Punjab, Pakistan,” Journal of Population Economics 32, issue 3 (2019): 769-797, “Labor Market Information and Parental Attitudes toward Women Working Outside the Home: Experimental Evidence from Rural Pakistan,” Economic Development and Cultural Change 32, issue 3 (2024): 1041-1067, “Heterogeneous Impacts of Interventions Aiming to Delay Girls’ Marriage and Pregnancy across Girls’ Backgrounds and Social Contexts” (joint with Thoai Ngo, Stephanie Psaki, Sajeda Amin, Karen Austrian), Journal of Adolescent Health 69, issue 6 (2021): S39-S45, among others.

Title: From Data Platforms to Knowledge Infrastructure
Abstract:

Modern society is facing pressing issues, including environmental challenges, inequality, and regional conflicts. To resolve these complex societal problems, the concept of “open science” is essential, as emphasized at last year’s G7 meeting. In Japan, starting in 2025, all scientific papers resulting from publicly funded research, along with the associated data, will be required to be immediately accessible through open access. The National Institute of Informatics (NII) has been at the forefront of advancing Japan’s academic information infrastructure for many years. In 2017, NII embarked on the development of the NII Research Data Cloud―a platform for the publication, discovery, and management of academic information―which became operational in 2021. By 2022, the project evolved into a research data ecosystem, built in collaboration with numerous universities and research institutions. This initiative aims to create a comprehensive environment where papers, data, and computational resources are readily accessible across all fields of research. Recognizing the significant impact of generative AI on society and the need for a hub in Japan where large-scale language models (LLMs) can be developed and studied, NII spearheaded the formation of the LLM-jp study group in May 2023. The group, founded on principles of openness, began with approximately 30 researchers specializing in natural language processing and has since grown to over 1,800 participants from industry, government, and academia. In April 2024, NII further advanced this initiative by establishing the LLM R&D Center. By September 2024, the center had developed and released the world’s largest fully open LLM, featuring 172 billion parameters―on a scale similar to GPT-3.5. The center's ongoing work also focuses on ensuring the reliability and transparency of these models. To address the complex societal challenges mentioned above, it is crucial not only to deepen academic research but also to foster collaboration across various disciplines, creating new cross-disciplinary knowledge. LLMs can play a pivotal role in these processes by interpreting data, interconnecting and systematizing knowledge, and laying the groundwork for a robust knowledge infrastructure.

   Sadao Kurohashi received a PhD in Electrical Engineering from Kyoto University in 1994. He is currently the Director-General of the National Institute of Informatics, Japan, and a specially appointed professor at the Graduate School of Informatics at Kyoto University. His research interests include natural language processing, knowledge infrastructure, and open science. He received the 10th and 20th anniversary best paper awards from Journal of Natural Language Processing in 2004 and 2014, respectively, 2009 IBM faculty award, and the 2010 NTT DOCOMO mobile science award, and the 2017 Commendation for Science and Technology by the Minister of Education.