This thesis was conducted to study and analyze the fund allocation process adopted by different states in the United States to reduce the impact of the Covid-19 virus. Seven different states and their funding methodologies were compared against the case count within the state. The study also focused on development of a physical distancing index based on three significant attributes. This index was then compared to the expenditure and case counts to support decision making.
A regression model was developed to analyze and compare how different states case counts played out against the regression model and the risk index.

One of the most pressing questions in economics is “why are some countries richer than others?” One methodology designed to help answer the question is known as “Development Accounting,” a framework that organizes the determinants of income into two categories: differences in inputs and differences in efficiency. The objective of our work is to study to what extent differences in the levels of pollution can help explain income differences across countries. To do this, we adjusted a factor-only model to allow us to enter PM2.5, a measure of pollution that tracks the concentration of fine particulate matter in the air and looked to see if the model’s predictive power improved. We ultimately find that we can improve the model’s success in predicting GDP by .5 - 6%. Thus, pollution is unlikely to be a major force in understanding cross-country income differences, but it can be used with other economic factors to potentially magnify its impact with other additions in the future.