Annex 1: Country Sample – 31 Countries 13
Caribbean small states (13): Antigua and Barbuda, Barbados, Belize, Dominica, Grenada, Guyana, Jamaica, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Suriname, The Bahamas, Trinidad and Tobago
Other small states (18): Botswana, Cabo Verde, Comoros, Djibouti, Equatorial Guinea, Gabon, Guinea-Bissau, Kiribati, Lesotho, Maldives, Mauritius, Micronesia, Namibia, São Tomé and Principe, Seychelles, Swaziland, The Gambia, Vanuatu
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Leandro Medina & Friedrich Schneider, 2018. “Shadow Economies Around the World: What Did We Learn Over the Last 20 Years?” IMF Working Papers 2018/017, International Monetary Fund
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There are other potential determinants of income inequality, which we cannot include due to the lack of data.
Government Effectiveness captures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government’s commitment to such policies. Estimates give a country’s score on the aggregate indicator, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5.
Due to the lack of long historical time series data and adequate instruments at cross country level, we do not attempt to identify causes of inequality. Instead, we report and interpret our results as statistically significant relationships.
The list of countries included in the regression is reported in Annex 1.
We also explored the potential association between inequality and redistribution with crime, gender gaps and natural disaster indicators. They were not included in any of our final specifications because none of them showed statistically significant results.
The Chinn-Ito Financial Openness index is a normalized measure, so the coefficient in the regression represents the marginal effect of a change in one standard deviation of the world sample.
As related IMF research suggests (see Grigoli and Robles, 2017; and Grigoli et al., 2016) the empirical relationship between inequality and economic development is inconclusive. If income is not highly concentrated, an increase in inequality can provide incentives for countries to be more productive. If highly concentrated, that same increase can lead to rent-seeking behaviors. Thus, the relationship between inequality and economic development is likely to be non-linear. The impact of income inequality on economic development is positive for values of a net Gini below a certain threshold but that impact becomes negative for values above it, and null elsewhere.
However, if we consider redistribution levels in advanced economies, this tentative explanation is at least controversial. Income redistribution policies in most advanced European countries are much larger, indeed, on average, they represent a reduction in the Gini coefficient of around 20 points (the difference between Gini at market income and Gini at disposable income). As stated in previous sections, redistribution levels in small states represent on average about 3 points of their Gini inequality index.
Note that the interpretation of the coefficients in Table 3 is not straightforward. Redistribution is not measured in terms of changes in income but instead in terms of the change of Gini coefficient due to changes in income connected to payments of direct taxes and receipts of direct transfers (i.e. redistribution through fiscal policies).
According to Medina and Schneider (2018), the size of the informal economy in small states could be as much as twice the observed in advanced economies.
Without the Caribbean dummy, the model’s goodness of fit (R-Squared) would reduce to 0.55, while only including it, the R-Squared would already be almost 0.8.
Initially, we followed the IMFs criteria for small developing states, that is for states with populations under 1.5 million. However, data availability was an issue and some countries had to be omitted. To compensate for this “similar small states” were added to the sample with populations around 2 million.