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In my study, I have taken the linear filtering techniques which Lucas developed in 1980, and the recursive estimation method, as well as the chow test and F-test, and choose the data of the US, Britain, Japan, Germany, Euro area, BRICKs and some members of ASEAN, from 1960 to 2012, to study the relationship between annual rate of M2 growth and CPI inflation. The results show that in most sample developed and developing countries the positive correlation relationship between money supply and inflation began to weaken since the 1990s, and “the paradox of inflation” is now a common phenomenon.
In my paper, I attempt to provide a new explanation of “the paradox of inflation”. I conjecture that, in the past two decades, some advanced countries were becoming a “relatively wealthy society”, which means that commodity supply as well as money supply is abundant. I state that the US is a “relatively wealthy society” and try to determine what features could mark a “relatively wealthy society”.
I choose the credit growth rate of nonfinancial sectors and the ratio of dividends to investment to represent the production inclination of the business sector, and choose the income per capita and the GINI index to represent the consumption inclination of the resident sector. Then, through a semi parametric varying-coefficient regression model, I found that, in the US, when the credit growth of the business sector is under 5%, the ratio of dividends to investment is over 0.20, the per capita income is more than $30,000, and the GINI index is over 0.45, the country becomes a “relatively wealthy society”.
Base on this new explanation, I can conclude “in the relatively wealthy society, inflation is no longer a monetary phenomenon; it is a wealth allocation phenomenon”.
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.
Using the Development Accounting methodology specified in Caselli (2004), we investigate the potential of PM2.5, a measure of pollution, as an explanation of cross-country differences in GDP using available Macroeconomic data from the Penn World Table and the WHO. We find that the addition of PM2.5 makes improvements to the model within the expectations of the literature. This adjustment shows promise for use in cooperation with other, more potent economic factors.