Previous studies are limited in identifying the effectiveness of each country to seek sectoral support rather than integrated aid. However, it is hypothesized that sector-specific aid by Official Development Assistance (ODA) may be more effective than total aid. This study aims to identify the determinants of economic growth and the living standard levels in 15 Asian countries, focusing particularly on the effects of Official Development Assistance (ODA). In order to explore this research question, we have used two indexes: (1) the annual ODA grants to Korea, with aid type as the key independent variable; and (2) the human development index (HDI), to measure dependent variables from 2006 to 2016, across the 15 Asian countries. Special attention has been paid to understand which is more significant on human development, the effects of each type of aid program individually, and the whole amount of ODA assistance. We have constructed a panel model and a fuzzy set ideal type model to account in the data for qualitative attributes by recipient countries. We have found that the economic and social impacts of ODA on the basis of panel data are significant and that our instrumental variable (IV) method illustrates a statistically significant impact of the total ODA on the HDI of the recipient countries. By separating the total amount of ODA into economic and social sectors, we have found that specific programs of public service, medical care, and welfare are more likely to directly affect HDI. While the total amount of ODA still has a positive impact on HDI, education, health, and the public service field, aids also have significant effects on HDI. Although the effect of sector-specific aid in the water and sanitation field grant-aid is not significant in panel regression results, our fuzzy set method shows that, even if education aid is low, HDI is estimated to increase if the level of health and public service aid is high. Our empirical findings suggest that (1) sector-specific aid may be more effective than total aid with ineffective sub-aid programs and that (2) an optimally specific combination of various sub-programs in ODA may exist for each developing country.

Conventional studies on policy demand identification that are anchored in big data on urban residents are limited in that they mostly involve the top-down and government-oriented use of such data. It restricts treatment to specific issues (e.g., public safety and disaster management), even from the beginning of data collection. Scant research has emphasized the general use of data on civil complaints—which are independent of areas of application—in the examination of sustainable cities. In this work, we hypothesized that the analyses of civil complaint data and big data effectively identify what urban residents want from local governments with respect to a broad range of issues. We investigated policy demand using big data analytics in examining unstructured civil complaint data on safety and disaster management. We extracted major keywords associated with safety and disaster management via text mining to inquire into the relevant matters raised in the civil complaints. We also conducted a panel analysis to explore the effects exerted by the characteristics of 16 locally governed towns on residents’ policy demands regarding safety and disaster management-related complaints. The results suggest that policy needs vary according to local sociocultural characteristics such as the age, gender, and economic status of residents as well as the proportion of migrants in these localities, so that, city governments need to provide customized services. This research contributes to extend with more advanced big data analysis techniques such as text mining, and data fusion and integration. The technique allows the government to identify more specifically citizens’ policy needs.