group of countries. EMDEs are defined by the level of development while commodity exporters by the export share of commodities, but there is heterogeneity across countries within each of the groups. Not all commodity exporters export the same commodities, and the nature of different commodities makes their production structures very different. We, therefore, introduce two variables to account for the heterogeneity across countries. First, we introduce (the log of) GDP per capita to better capture the level of development for individual countries. The addition of GDP per capita does not change the magnitude or the significance of the coefficient of either of the two dummy variables. In fact, GDP per capita is not significant in column (2) although it is significant in column (5). This suggests that EMDEs are not different simply because their GDP per capita is lower than that of advanced economies, and that there are other features of fiscal policy that differ across EMDEs.

Second, we add a variable (“resource rents”) that measures the rents coming from natural resources as a share of GDP. Higher rents coming from natural resources are likely correlated with the percentage of government revenues that come from the commodities sector.<sup>16</sup> The objective here is to allow our explanatory variable to have a much finer distinction depending on the actual revenues coming from natural resources rather than relying on a dummy variable that treats all countries with significant income coming from natural resources as equal. The introduction of this variable (Columns (3) and (6)) makes the dummy for commodity exporters become insignificant and even negative. This should not be a surprise as it reinforces the idea that economies with large commodities sectors display more volatility in fiscal policy. In other words, it is not just the presence of an important commodity sector, but the size of the sector also matters.

The introduction of this new variable also reduces the size of the EMDE dummy as some of these countries have substantial rents coming from natural resources and it, therefore, absorbs some of the variation in fiscal policy volatility. Note that GDP per capita becomes significant once this variable is introduced, suggesting that higher levels of GDP per capita are associated with lower fiscal policy volatility. Therefore, taken together these results suggest that both the level of development and the degree of commodity dependence help explain fiscal volatility. Finally, the fact that a limited set of variables explains about 45-50 percent of the cross-country variation in fiscal policy suggests that our measure of volatility is not simply capturing noise from the initial regression in equation (1).

Next, we exploit the richness of our diverse sample of commodity exporters by separating them into three groups—agriculture, metals, and energy exporters—depending on the main type of commodity exported to examine whether there are differences in fiscal behavior across different types of commodity exporters. Table 3 presents the results. We find evidence of additional volatility for all three groups, but it is for the energy exporters where the coefficient is always

> 16 We chose to use ‘resource rents’ instead of resource revenues due to data availability issues. The ‘Natural Resource Governance Institute’ provides data on resource revenues (as percent of GDP) complied from the Extractive Industries Transparency Initiative’s (EITI), IMF, and the International Centre for Tax and Development (ICTD). The database provides data for about 90 countries from the 1980s onwards although data availability varies across countries. The correlation between our measure of total resource rents and the resource revenues from this dataset is about 69 percent.

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