7 revenue) of around 65-75 percent, that country could probably be expected to raise between _5_ and 6 percent of GDP in actual VAT revenues, when the VAT becomes fully functiotial".

Extending the linear univariate model by including the base variable improves slightly the goodness of fit (by 4 percentage points), the RATE variable is still strongly significant, and the BASE coefficient has the anticipated positive sign, although it is only weakly significant at 10 percent level (see table 1). This is probably the result of the inability of the dummy variable to captule fully the diversity of tax bases in the sample.

# B. **Full Sample of 34 Countries**

The regression results from the full sample of 34 countries (see first column in Table 1) give i VAT rate coetficient of 0.389, indicating that the VAT generates revenues of some 0.4 percent of GDP for every percentage point of the ratet<sup>4</sup> . As expected, VAT revenue as a percent of GDP rises with increases in coverage and size of the tax base: the estimated equation shows a positive and significant oase coefficient at the 6 percent level. The low significance can be attributed to the inadequacy of the available data as a measure of the true base.

The base dummy used here has the right sign but does not fully capture the diversity of VAT tax, hence its low significance. For instance, the BASE data in the appendix fail to distinguish between the Israel and UK VAT regimes. Both countries have a single rate of 15 percent and both have a (G+S) VAT tax base. However, Israel enjoys a higher revenue of 9.8 percent of GDP compared to 6 percent in UK (also see Figure 1). The difference, however, does not reflect lower _collection_ efficiency in the U.K vis-a-vis Israel. The difference in revenue is explained by the difference in the size of the bases: Israel is an exception in that its VAT base includes the financial 13/ In fact, there are several subcategories of tax "gaps" including: the gap between potential and declared taxes, between declared taxes and paid taxes, and between taxes paid and those that are received by the treasury. Examination of the nature and sources of these tax gaps is identified as a fruitful area of further research (see: Bird and Casanegra, 1992, p. 1I).

14/ The RATE variable for multmle rate countries uses the standard rate in all regressions. The weighted average rate for these countries would be clearly preferable, but was unavailable at the time of writing. A subsequent comparison of (unpublished) weighted average rates collected by Silvani and the standard rates for ten multiple rates countries reveals that weighted rates are, mainly, lower than the standard rates. This means that the results of oi.r regressions may underestimate somewhat the rate coefficient in the full sample model, and more so in the only multiple rate models, since lower weighted rates generating sarme revenues imply higher revenue productivity. However, it is not clear whether this bias affects the results significantly in the full sarnple model, which is dominated by the larger number of single rate countries (20 as opposed to 14 multiple rate countries). We are grateful to Carlos A. Silvani (International Monetary Fund) for these observations and for sharing his data, which suggested an obvious line of further research. Specifically, we plan to reestimate the above models as more weighted rates data become available and to explore, with Silvani, the -nultiple rate models and tax gap models in greater detail.