The research activities in the program will address the following four themes:
1. Industrial productivity in East Asia
We will update and improve—jointly with the Institute of Economic Research of Hitotsubashi University—the JIP Database, which is the primary resource for the analysis of changes in the industrial structure and productivity trends in Japan, and extrapolate these data to the period before 1993. This task will be performed in collaboration with the EU KLEMS and the World KLEMS projects to make international comparisons on issues such as the long-term economic stagnation, the worldwide slowdown of economic globalization, and the economic impacts of the COVID-19 pandemic. In addition, we will update and improve the CIP Database jointly with the Institute of Economic Research of Hitotsubashi University and the China Center for Economic Research of Peking University to analyze the Chinese economy, which is feared to be facing a significant slowdown in its economic growth. The program will endeavor to publish research publications that focus on the construction method used for the JIP Database, the results of its analysis, and the methods used for measuring and improving the productivity in services.
This program will conduct analyses using firm/business-level microdata. In particular, we will 1) construct and analyze a database (comparable to the NBER-CES Manufacturing Database in the United States) to measure the trends in TFP by classifying the manufacturing sector into approximately 300 sub-sectors, 2) construct a labor productivity database by industry (approximately 30 sectors) and commuting area (approximately 300 areas) using the raw data from the Economic Census for Business Activity 2012/2016 and analyze the aging population and its relationships with ICT and e-commerce using the database, and 3) conduct productivity dynamics analysis in collaboration with OECD's DynEmp project.
The current COVID-19 pandemic has revealed the vulnerability of the medical system in many developed countries; therefore, Japan and other developed countries need to redevelop their social infrastructure to mitigate the destructive decline in productivity caused by disasters and depressions and the risk of ensuing long-term stagnation. The latter will also be addressed during the analysis in this program.
2. Updating and analyzing the R-JIP Database
The existing R-JIP 2017 covers through to 2012; however, it does not correspond to the System of National Accounts (SNA) 2008. This program uses the 2008 SNA-comparable Prefectural Accounts (from 2006 to 2016) released in July 2019 by the Cabinet Office and other agencies as a starting point and aims to create and release an updated R-JIP Database for the period between 2006 and 2015.
In addition, we will create a 2011 table that corresponds to the 2005 Inter-prefectural Input-Output Table, which identified the interregional division of headquarters functions. We will expand the existing single-year estimate (for 2005) for the sectoral land input, which is important when measuring productivity in services, into yearly estimates.
3. Analysis of the improvement in resource distribution and productivity in the medical/education service sectors
For the medical and education industries, which are particularly important for Japan's future economy, we will measure the output and productivity based on the quality of services adjusted using detailed microdata and analyze the determinants of productivity. We will focus on the examination of causal relationships and contribute to policy formulation and evaluation. Our analysis of the medical sector will focus on issues concerning the distribution of resources and that of the education sector will focus on the measurement of the impacts of educational policies and practices.
4. Micro empirical analysis of enterprise and industrial growth
By designating the "Engine of Enterprise Growth" as the central research theme, we will conduct empirical analysis that explicitly considers the identification of causal relationships between exogenous shocks and enterprise growth and deduces implications which we can refer to during policy making and enterprise activities. In addition, innovative causal analysis techniques using highly granular data and machine learning techniques will be employed to identify the causes and outcomes of the growth mechanisms of firms and industries. In particular, we will create a collaboration system with private companies that retain unique confidential data (e.g., overseas transaction data of companies, map data, financial agreement (e.g., lease) data of companies, and insurance contract data of companies) and conduct empirical research using data that have not been used in existing research. Specifically, we will conduct causal analysis using exogenous shocks to analyze 1) the aging population and innovation, 2) globalization and the hollowing out of the domestic economy, 3) business succession in small- and medium-sized enterprises, 4) financial system/financial policy and resource allocation, 5) U.S.-China trade friction and productivity dynamics, and using machine-learning technics to analyze, 6) the statistical causal relationship regarding the determinants of enterprise growth and exit, 7) statistical causal relationships regarding the determinants of accounting frauds, 8) demand function estimation of financial agreements (e.g., lease) that consider the heterogeneity of firms and the application to the estimation of dynamic pricing, and 9) identification of the spillover process in growth factors via transaction networks.
