This thesis sets out to explain the relationship between fiscal spending and economic growth. The relationship is established using a panel vector autoregression model estimated by GMM, using GDP growth and government spending on a panel of 30 OECD countries. The model used is tested with slight variations in specification which are concluded to be important in the finalized results. By altering the specification used in the model this thesis produces relatively different sizes on the multiplier effect both in the short run and in the long run effect. The size of the multiplier effect produced by this thesis is varying between 0.437 on the low side and 2.224 on the high side depending on a few alterations in model specification. Similarly, the long run multiplier effect is measured as 1.873 on the low side and 8.263 on the high side. The mean duration of the multiplier effect is estimated to be approximately 3 years.

The big-box retail stores such as Wal-Mart and Target have become the focus of many studies researching their impacts on local economic outcomes. This dissertation studies three related topics: (i) the dynamic interrelationship among the presence of the big-box stores, retail wage, and employment, (ii) the impact of the big-box retailers on personal income growth, and (iii) the dynamic interrelationship between the presence of big-box retailers and personal income growth. The research draws important insights with potential implications for regional developers and policy makers. The first essay analyzes the dynamic relationship among the presence of the big-box retailers, retail wage, and employment at the county level for 1986-2005. A vector autoregression model is applied on panel data. Impulse response functions and variance decompositions are also presented. Results suggest that the presence of big-box stores decreases retail wages and increases retail employment. Retail employment has a higher impact on the retailers’ location decision than retail wage. The results also show that the presence of Wal-Mart drives the above-mentioned effects, while the presence of Target is insignificant. The second essay investigates the impact from the presence of big-box retailers on personal income growth in U.S. counties between 2000 and 2005 - based on neoclassical growth models of cross-country income convergence. Results suggest that counties having both Wal-Mart and Target stores experienced slower growth in personal income. After controlling for spatial autocorrelation, similar to the first essay, the effect of Wal-Mart’s presence on personal income growth is dominant in terms of statistical significance relative to Target’s. The third essay expands the second essay and investigates the dynamic interaction between the presence of big-box retailers and personal income growth over time at the county level for the period 1987-2005, using a panel vector autoregression model. For this analysis, the earning shares of natural resources and manufacturing sectors are included - assuming that all the variables are endogenous to one another. The findings indicate that big-box retailers negatively affect personal income growth, which is consistent with the second essay. However, personal income growth has an insignificant effect on the big-box retailers’ location decision.

In this thesis we examine the macroeconomic interactions of unconventional monetary policy introduced by European Central bank during crisis by estimating a panel vector autoregression. We study impact of such policies using monthly data from 13 Central and Eastern European countries within seven-year period from 2008 to 2014. We find a positive reactions of output and prices to expansionary unconventional monetary policy shock. Our results provide evidence that decrease in shadow policy rate of ECB leads to rise in output as well as temporary rise in inflation, however, the effect on inflation is weaker and less persistent. We also find that unconventional monetary policy positively influences market uncertainty, but we do not find any significant effect on exchange rates. Individual country estimates suggest that the reaction of exchange rates to non-standard monetary policy shock significantly vary across countries.

Corruption is a social evil that resonates far and deep in societies, eroding trust in governance, weakening the rule of law, impairing economic development, and exacerbating poverty, social tension, and inequality. It is a multidimensional and complex societal malady that occurs in various forms and contexts. As such, any effort to combat corruption must be accompanied by a thorough examination of the attributes that might play a key role in exacerbating or mitigating corrupt environments. This dissertation identifies a number of attributes that influence corruption, using machine learning techniques, neural network analysis, and time series causal relationship analysis and aggregated data from 113 countries from 2007 to 2017. The results suggest that improvements in technological readiness, human development index, and e-governance index have the most profound impacts on corruption reduction. This dissertation discusses corruption at each phase of infrastructure systems development and engineering ethics that serve as a foundation for corruption mitigation. The dissertation then applies novel analytical efficiency measurement methods to measure infrastructure inefficiencies, and to rank infrastructure administrative jurisdictions at the state level. An efficiency frontier is developed using optimization and the highest performing jurisdictions are identified. The dissertation’s framework could serve as a starting point for governmental and non-governmental oversight agencies to study forms and contexts of corruption and inefficiencies, and to propose influential methods for reducing the instances. Moreover, the framework can help oversight agencies to promote the overall accountability of infrastructure agencies by establishing a clearer connection between infrastructure investment and performance, and by carrying out comparative assessments of infrastructure performance across the jurisdictions under their oversight or supervision.