This study aims to investigate if the level of economic freedom matters for how corruption affects per capita income in US states.
Using a new (and novel) index of corruption, which is based on Associated Press news wires, the author estimates the long-run cointegrating relationship between corruption, economic freedom and per capita income with fully modified ordinary least squares (FMOLS) following Pedroni (2000).
The author finds that there is a threshold level of economic freedom that determines if corruption reduces the per capita income in a state. According to the FMOLS estimations, the negative effects of corruption on income decrease as economic freedom increases, and they eventually disappear.
This is the first study investigating the intricate relationship between corruption, economic freedom and economic performance using data from US states. The study uses a news-based measure of corruption constructed by Dincer and Johnston (2017), which has several advantages over the convictions-based measure used in previous studies analyzing the relationship between corruption and growth using US data. The study takes into account the integration and cointegration properties of the data and estimates the relationship among the cointegrated variables using FMOLS following Pedroni (2000).
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