Comparing the German and Japanese nursing home sectors: Implications of demographic and policy differences

This research provides a comparative study of the Japanese and German nursing home sectors. Faced with aging populations, both countries share similar long-term care policies based on social insurance. However, descriptive statistics indicate significant differences in the outcomes and costs in their respective nursing home sectors. This research aims to identify the reasons for this state of affairs by examining demographic and policy differences between the two countries. To shed light on the subject from multiple angles, we conduct three types of empirical analysis—regression, the Blinder-Oaxaca decomposition, and data envelopment analysis—on regional data from the past decade. Our findings indicate that the different outcomes are driven by both demographic and policy differences where policy relates to long-term care as well as to additional welfare aid. In terms of policy, a key difference is found in the designs of the welfare programs for low-income elders. In Germany, our results are consistent with moral hazard due to the generous design of the welfare program, while in Japan, our results do not indicate moral hazard, which may be due to strict nursing home admission rules for welfare recipients

A nonparametric Bayesian approach for counterfactual prediction with an application to the Japanese private nursing home market

This paper proposes a new inferential framework for structural econometric models using a nonparametric Bayesian approach. Although estimation methods based on moment conditions can employ a flexible estimation without distributional assumptions, they have difficulty conducting a prediction analysis. I propose a nonparametric Bayesian methodology for an estimation and prediction analysis. My methodology is applied to an empirical analysis of the Japanese private nursing home market. This market has a sticky economic circumstance, and my prediction simulates an intervention that removes this circumstance. The prediction result implies that the outdated circumstance in this market is harmful for consumers today

A nonparametric Bayesian approach for counterfactual prediction with an application to the Japanese private nursing home market

This paper proposes a new inferential framework for structural econometric models using a nonparametric Bayesian approach. Although estimation methods based on moment conditions can employ a flexible estimation without distributional assumptions, they have difficulty conducting a prediction analysis. I propose a nonparametric Bayesian methodology for an estimation and prediction analysis. My methodology is applied to an empirical analysis of the Japanese private nursing home market. This market has a sticky economic circumstance, and my prediction simulates an intervention that removes this circumstance. The prediction result implies that the outdated circumstance in this market is harmful for consumers today

Accuracy improvement of small defect detection for ultrasonic inspection by using scientific visual analysis

When we diagnose the structural integrity of a steel member in service and evaluate its remaining life time, we need to improve analyzing method to get the accurate information of a internal defect of a member. And by the recent demand for high level quality control of welding of steel joint the accuracy improvement of defect detection in the image display became essential in nondestructive evaluation (NDE)[1][2][3]. So far the defect information obtained by an ultrasonic test is displayed in several ways and A-scan and B-scan displays are commonly used in a field inspection and C-scan display is used in laboratory test of the steel structural member[4][5]