While the first two indicators are similar, there may be merit in calculating, if not necessarily presenting, both. The first indicator has the benefit that it is a commonly used and understood metric. Meanwhile, from a presentational perspective there may be merit in the second indicator’s focus solely on taxes that are considered “good” for progressivity. This potentially contrasts with the negative connotation that may be created by the first indicator that indirect taxes are “bad” whereas direct taxes are “good”. Not only is that a simplistic interpretation from a distributional perspective, but it may cloud the fact that there are strong tax policy rationales for raising significant revenue from indirect taxes within a broader inclusive growth strategy. Additionally, the second indicator provides a more muted response to differences in revenue source (due to direct taxes also appearing in the denominator). Given the bluntness of the indicator, this muted nature may be preferable to a more highly responsive indicator. Finally, the second indicator provides additional consistency with the third indicator as they are based on an identical numerator.

It is proposed that these indicators would be calculated for the most recent years possible for IDA countries to enable monitoring over time. In most cases, this will involve a 1-2 year lag, though for some countries it will be greater. For countries where lags are significant, an alternative approach could be to present (top) statutory tax rates for major tax types including PIT, CIT and VAT. However, these would ideally need to be complemented with information on the breadth of these tax bases (such as the C- efficiency ratio for VAT),<sup>36</sup> and would require similar assumptions to be made regarding the incidence of the different taxes in order to draw implications regarding progressivity.

These are obviously blunt instruments, as no detail is provided on tax burdens borne by poor vs rich taxpayers. This is particularly important in the case of the PIT where, for example, a flat tax regime could be applied that raises the same total revenue as a highly progressive tax, but this would not be differentiated using the tax revenue data. The remaining indicators therefore provide greater detail on the PIT.

# 6.2 Effective progressivity indicators

To examine the effective progressivity and redistribution achieved by PIT systems in IDA countries, it is proposed that Kakwani and Reynolds-Smolensky effective progressivity (and redistribution) indicators be calculated for each IDA country. These indicators could be constructed from a full microsimulation model. However, building a full microsimulation model even for a single country is highly data and resource intensive.<sup>37</sup> In the context of producing progressivity assessments for more than 60 IDA countries, it is proposed that a simplified modelling approach is more appropriate.

36 The aggregate tax revenue figures effectively incorporate both the tax rate and breadth-of-base information into the one indicator.

37 The FPSG unit has constructed PIT microsimulation models for a number of developing countries as part of its general microsimulation capacity building work. However, no models have been developed as yet for IDA countries. Constructing these models requires a significant resource investment, and so their development is more suited to country-specific projects rather than for an analysis of 60+ countries. Full microsimulation models also have a broader use than just progressivity assessment. Specifically, they are constructed to provide Finance Ministries with the ability to assess both the revenue and distributional impacts of potential tax reform options. To maximize accuracy of revenue figures, they are typically based on tax return microdata rather than income survey data (and so do not account for the full income distribution – which is necessary for constructing progressivity measures). That said, work is underway to adapt the unit’s tax return data-based models to utilize income survey data, and to develop methodologies to address the limitations of different data sources.

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