No abstract

Local infrastructure development is a crucial goal for sustainable development, for which local governments take charge of developmental policies. This implies that the capacity of the local government determines the performance of the developmental policies—local infrastructure development. In this sense, this study investigated the impact of local government capacity, measured via the quantity and the quality of human and financial resource factors, on its performance. Moreover, the study examined which of the multidimensional government capacity components affect performance, controlling a competition effect or spillover effect among localities. The study analyzed panel data containing six years (2013–2018) of information on 152 local bodies in Korea, employing the spatial autoregressive model, which is useful for controlling geographical spatial effects. The data show that, unlike the quality factors, the quantity of government capacity does not have a significant effect on its performance. Furthermore, the data also indicated that there are competition effects in relation to the performance of local development. The results imply that local governments need to improve the quality of managerial government capacity in order to increase their sustainable development performance.

When determining their redistributive budgets, states must strike a subtle balance—to provide for their needy residents without becoming a “welfare magnet” and attracting poor individuals from neighboring states. We examine the competing incentives that state politicians face in federal systems and their effects on program accessibility and redistributive spending across U.S. states between 2005 and 2011. Comparing two redistributive programs under state control—Medicaid and Temporary Assistance for Needy Families (TANF)—we find strong evidence of interstate competition in the case of cash assistance programs, but less evidence in the case of health care. Yet our data show that states do not alter their policies in response to rising inequality, that is, when the median voter becomes poorer than the average voter. Moreover, the Great Recession had a greater impact on TANF than Medicaid. We attribute these differential effects to different funding mechanisms used by the federal government to finance the two state-administered programs.

A critical issue in CWB studies is how to measure Community Wellbeing (CWB). While prior studies have focused on subjective and objective indicators for CWB measurements, this study introduces the intersubjective metric, identifying its necessity, importance, and usefulness. To this end, we collected objective data, subjective, and intersubjective data from Korea City Stat and CWB survey, and measured community wellbeing for twenty-seven communities in Korea from these three perspectives. We then compared and analyzed the three measurements (i.e., objective, subjective, and intersubjective approaches) using distance analysis, mean comparison analysis, and correlation analysis through the standardization and regularization process. The results of analyses demonstrated that the intersubjective CWB indicators are useful in themselves. Based on these findings, we hope that in future community well-being studies, the intersubjective indicators will be used as indicators for local development beyond simple indicators dealing with objective conditions and subjective satisfaction.

This study analyses efficiency and effectiveness of highway management at the state level in the United States. While the current literature on highway management has contributed to understanding infrastructure budget and finance, the relationship between efficiency and effectiveness measurements has not been sufficiently discussed in the context of sustainability. To fill this gap, this study was systemically designed to test the relationship by controlling the states' political factors, fiscal capacity, median voter, and economic conditions. Data envelopment and principal component analysis with panel data covering 11-year time waves were used to measure both efficiency and effectiveness. The results of the fixed effects model and the spatial autoregressive panel model show a statistically strong relationship between efficiency and effectiveness which are respectively measured by two analysis approaches.