2019, Vol.15, No.3

Crisisonomy 2023 KCI Impact Factor : 0.64

pISSN : 2466-1198 / eISSN : 2466-1201
https://journal.kci.go.kr/cemtp

In this study, I used a multi-level analysis method to analyze the factors affecting individual’s perception of corruption, considering both individual level variables and national level ones. As a result of the analysis, it is found that both perception of social and public corruption level are influencing evenly by individual factors and national environmental factors. It also suggests that the perceived levels of corruption in the society as a whole and the factors influencing the perception of corruption by public institutions are differentiated. These results suggest that the perception of corruption may also constitute social risks and crisis situations and that government policy efforts to mitigate the perception of corruption of the public are needed.

This study aims to describe the situations of the vulnerable based on the philosophical discussion of the Sara Ruddick’s “Maternal Thinking” and emerging issue on the safety right along with the overview of their situation in South Korea and other countries. Subsequently, we proposed a few ways to resolve their situations based on safety rights and Sara Ruddick’s maternal thinking. The results offer examples suggesting that the government should be more active rather than passive. This study strengthens previous studies’ weak legal and philosophical foundation for future studies on the vulnerable populations affected by the disaster.

In this study, we propose a regression model for estimating damage amounts of natural disasters based on the quantitative data provided by public institutions. Based on quantitative data for 225 primary local governments during the years of 2001 to 2014, we define a regression model to have 14 independent variables by applying variable selection methods to the variables from 32 sub-indexes used in Natural Disaster Risk Index(NDRI). For verifying the proposed regression model, we compare the estimates from the model with NDRI, Regional Safety Grades(RSG), and actual amounts of damages from natural disasters. From the analysis results, we can find the regression model give better estimates for the amounts of damages than NDRI and RSG. If we utilize the proposed model, we can estimate the future changes in the amounts of damages using the changes in natural disasters and improve the performance of NDRI using the standard regression coefficients from the regression analysis.