Government Capacity and the Acquisition, Implementation, and Impact of ARRA Funds

This dissertation examined transportation grants provided to states under the American Recovery and Reinvestment Act of 2009 (ARRA). Some states acquired more grants and utilized them in a timelier manner than others. This dissertation examined why this is the case, utilizing System Theory and Resource Based Theory as the intellectual framework. Human resource and financial resource capacities were viewed as the principal drivers of success and studying this managerially controllable variables underpin the analysis. Though many studies have examined ARRA since 2009, my dissertation is the first to simultaneously examine the three stages of the ARRA transportation grant process: acquisition, implementation, and impact. There are three research questions, aligned with the three stages: (1) what factors affect state governments in the acquisition of competitive grants? (2) what factors affect state governments in the implementation of competitive and formula grants? and (3) what factors affect state governments in expenditure recovery and transportation investment? Government Capacity consists of four components, namely human resources, financial resources, general management, and experience. I used three regression models (log-linear for the first, and panel corrected standard error for the last two) to test the impact of the government capacity on grant acquisition, implementation, and impact. Overall, the test results showed that three dimensions of government capacity played a significant role to varying extents with respect to ARRA: human resource, financial resource, and experience. States with higher government capacity [strength (S) of capacity] turned the threat (T) of the Great Recession into an opportunity (O) for the restoration and development of transportation, and compensated for their weakness (W). The dissertation concluded that specific aspects of Government Capacity were thus relevant predictors of the acquisition, implementation, and impact of ARRA grants. Findings also support prior research that quality, not quantity of personnel may of signal import to organizational capacity during times of fiscal stress

Analyzing Local Government Capacity and Performance: Implications for Sustainable Development

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

Measuring Efficiency and Effectiveness of Highway Management in Sustainability

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