The visualization below is from a larger project I am working on in Python related to consumer behavior on Twitter. You can follow the full project as it is being finalized here. The visualization highlights the variation in consumer profiles across businesses and how they vary within each business (The bars represent medians, whereas the coloration represents mean. Incongruities between the two represent a skewed distribution of consumer characteristics, which may indicate bots, the presence of super-users, or something else as yet undetermined)
The collapse of Bretton Woods in 1971 (a result of Nixon decoupling the US dollar from gold) tested our understanding of the role of nominal exchange rate volatility on trade and the real economy. Although the IMF concluded in the 1980s that it was unlikely that increased volatility would have a significant negative impact on trade volume, it was less clear how volatility (and potential manipulation) would affect resource allocation and bilateral trade imbalances.
With trade and currency manipulation back in the news, I was interested in visually exploring the relationships between exports, exchange rates, and purchasing power parity (through The Economist's Big Mac Index). Using data from the US Census, WTO, and The Economist, I looked at the changes in these areas from 2006 through 2017.
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