Many previous studies on "Potential Economic Growth" and "Growth Potential" of the Japanese economy have been undertaken. But the Semi-Macroeconomic Analysis at the prefectural level is limited due to data limitations. In this paper, therefore, we attempt to estimate the potential GDP of each of 47 prefectures based on our original calculation of capital stock data. Furthermore, using the estimated values, I set some hypothesis from the viewpoint of "long-term stagnation theory" that solves the factors of long-term stagnation in the market economy in recent years, and try to verify the regional economic sphere as an analysis target. Through empirical analysis on the potential growth rate and the movement of the GDP gap, it is clear that many prefectures in Japan are "long-term stagnation due to a shortage of demand" and that especially the situation is same for the Tokyo metropolitan area centered on Tokyo, but that other local areas are "long-term stagnation due to a supply shortage".

As the super-aging society at Japan will increase medical expenses, we are focusing on studies using Real World Data, which is expected to have high external validity, to derive the evidence that can contribute to healthcare policy. Under Act on Anonymized Medical Data That Are Meant to Contribute to Research and Development in the Medical Field, the first Certified Producers of Anonymized Medical Data was just certified at the end of 2019 and the stakeholders are currently exploring methodologies for the secure management of personal information, anonymization methods, and data provision. On the other hand, Finland has already established the system for the systematic collection of comprehensive and thorough medical information with the Personal Identification Number from medical institutions across the country with the opt-out policy. The collected medical information is registered in the respective registers according to the respective rationale law. In addition, anonymized data are provided for secondary uses such as research. These activities are precisely the role that the certified producer is trying to fulfill. It is useful to know the current situation in this country in order to consider the direction of the future policy on the secondary use of medical information in Japan. Based on the interview and literature review, we introduce the Finnish health care register, institutional environment, and guidelines for anonymized processing of health care information, and present recent trends.

In Japan, the legislation called, Anonymously Processed Medical Information to Contribute to Medical Research and Development, came into force on 11 May 2018. Under the terms of the new law, a number of authorised service providers could be permitted to collect healthcare records from various medical and nursery-care institutions and to provide anonymously processed information to those who utilise medical statistics for their research. Prior to this Japanese programme, England has successfully implemented a similar system, following failure with "Care dot Data" (2013–6) due to a loss of public trust. This study hence explores the current noteworthy English fruitful endeavour to develop an institutional system for the secondary usage of healthcare information for more comprehensive and accurate medical statistics in healthcare research. It aims to discuss in what ways Japan should establish its counterpart. Drawing on the findings from Beck (1998), Giddens (1993), Luhmamn (1990), our research takes account of the social role of specialists and experts' expertise that includes the importance of medical statistics and statistical insight in our modern society because it takes an essential part of establishing contemporary social and public order vis-à-vis public trust on experts and the social system.

A number of European countries have established `register-based' statistical systems that cover administrative data Among them, countries including Denmark and the Netherlands have developed systems that enable the use of administrative data for research purposes, including systems which provide access to medical and health data. Based on the examples of Denmark and the Netherlands, this paper examines trends in the secondary use of medical and health data. This paper focuses on systems that allow researchers access to deidentified medical and health data. It also details the use of pseudonymization as a methodology for linking multiple administrative datasets via common identifiers. It is believed that these approaches can provide insights to further develop the use of administrative data in Japan.

In the field of official statistics, National Statistical Organizations in various countries are increasingly focusing on 'differential privacy' (an approach originally developed in computer science) as a method to prevent the disclosure of identifiable information by adding noise to the data. A motivating factor for this interest is the risk of `differencing attack' by which individuals can be potentially identified via a combination of several statistical tables taken from the outputs of small area statistics. This paper first introduces an empirical study that the U.S. Census Bureau conducted on differential privacy based on the 2010 Population Census data in the United States. This paper also examines the security and usability of statistical tables for which Laplace noise was introduced based on the methodology of differential privacy. Data used for this are grid squared statistics from the 2010 Japanese Population Census. Results show that the methodology of differential privacy is potentially applicable for Japanese Population Census data including small area statistics.

The decrease in mortality rates still ongoing, with the associated increase of life expectancy, longevity risk has emerged in many countries across the world. To deal with the dynamic variation of mortality rates and its effects, a set of statistical models, known as the stochastic mortality models, have been utilized. In this paper, we explain a few leading stochastic mortality models and apply them to the valuation of longevity risk from a Bayesian perspective.

Time series are sampled at various frequencies.In the classical literature of multivariate time series analysis, target variables are aggregated to the common lowest frequency. The temporal aggregation causes the loss of information and consequently lowers the accuracy of statistical inference. To address this issue, a new approach called Mixed Data Sampling (MIDAS) has been developed rapidly. MIDAS allows target variables to have different sampling frequencies. Recently, the MIDAS approach was adopted to vector autoregression and Granger causality tests. This article reviews these advances, and analyzes the dynamic interdependence between monthly inflation and quarterly economic growth in the United States. Significant Granger causality from economic growth to inflation is detected. The significant causality vanishes when inflation is aggregated to the quarterly level, suggesting spurious non-causality. These empirical results highlight the practical use of MIDAS.