The dynamics between financial market development, taxation propensity, and economic growth: a study of OECD and non-OECD countries

OECD countries comprise Australia, Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Japan, Korea Republic, Latvia, Lithuania, Luxembourg, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom and United States. The non-OECD countries are: Andorra, Antigua and Barbuda, Aruba, the Bahamas, Bahrain, Barbados, Bermuda, British Virgin Islands, Brunei Darussalam, Cayman Islands, Channel Islands, Croatia, Curacao, Cyprus, Faroe Islands, French Polynesia, Gibraltar, Greenland, Guam, Hong Kong, Isle of Man, Kuwait, Liechtenstein, Malta, Monaco, New Caledonia, Northern Mariana Islands, Oman, Palau, Panama, Puerto Rico, Qatar, San Marino, Saudi Arabia, Seychelles, Singapore, St. Kitts and Nevis, St. Martin, Taiwan, Trinidad and Tobago, Turks and Caicos Islands, United Arab Emirates, Uruguay, and Virgin Islands. Note that not all of the non-OECD countries were included due to data unavailability for some variables over our study period.

In Table 1 and all the subsequent tables referred to in the text, we have erred on the side of stating the results for CFM under Sample 3 and refer to it as Case 6. Careful readers will note that results for CFM would be best described under what we could have called Sample 4. However, to save space, we instead place the CFM results under Sample 3 and refer to it as ‘Case 6’.

We also performed unit root and cointegration tests for both of these sub-samples. The results from these tests were the same as for the pooled sample. We have not reported these results here due to space constraints.

FMOLS is a non-parametric estimation approach, which takes into account the possible correlation between the error term and the first differences of regressor, as well as the presence of a constant term, to deal with corrections for serial correlation (Pedroni 2000; Maeso-Fernandez, et al. 2006).

DOLS is a parametric estimation approach that adjusts the errors by augmenting the static regression with leads, lags, and contemporaneous values of the regressor in first differences.