## 6.3 Progressive capacity indicators

It is proposed also to complement the effective progressivity indicators with progressive capacity indicators. These two approaches are asking essentially two separate questions and there is consequently merit in producing both: while effective progressivity metrics assess the progressivity of the taxes actually paid by households, progressive capacity metrics assess the progressivity of the tax structure itself.

The case to also calculate progressive capacity metrics is strengthened by the fact that the proposed simplified approach to modelling effective progressivity can be leveraged to produce progressive capacity metrics at low additional resource cost. This is because: (1) the same information on underlying tax rules in IDA countries will need to be collected for either metric; and (2) a simplified tax modelling approach is adopted in each case – meaning that the percentile-based model that will be constructed for the effective progressivity metrics can easily be adapted for the progressive capacity metric.

The proposed methodology would be based on the simplified approach of Gerber et al. (2020), as outlined in section 5. This approach is based on average tax rate calculations made at 500 individual income points (from 1%-500% of GDP per capita). This provides a simple option to produce a progressive capacity metric, enabling changes in the progressivity of the tax schedules of an IDA country to be identified in isolation from their interaction with the underlying income distribution. Kakwani and Reynolds-Smolensky indices would be calculated based on this methodology for the tax rules applicable in each year from 2023-25 to enable monitoring.

As noted in section 5, Gerber et al. (2020) undertook their study for OECD countries, and it is possible that the methodology may not apply as effectively for IDA countries. This is because restricting the upper bound of the income distribution to 500% of GDP per capita could exclude significant parts of the progressive PIT schedule in some IDA countries. This will become clear as data on IDA countries PIT schedules is collected, and the methodology may be adjusted accordingly, if necessary. An alternative option that could be progressed is the simplified “transplant and compare” approach of Vellutini and Benítez (2021). While slightly more complicated to implement, this approach does not impose any ceiling on the income distribution and reduces the likelihood of country rankings being affected by the underlying ‘base’ distribution.

# 7 Conclusion

Measurement of tax progressivity is a challenging issue for several reasons. Most significantly, data limitations have a large impact on what taxes can be examined, what measurement approaches can be adopted to examine those taxes, and how reliable those approaches are likely to be. This is particularly important for IDA countries where data limitations are typically most significant. Additionally, there is no single definition of progressivity, or single method for measuring the degree of progressivity of a tax, thereby necessitating a range of decisions and value judgements to be made in any analysis. Contingent upon the definition chosen, there are several approaches that can then be adopted to measure the degree of progressivity of a tax, each with benefits and drawbacks. Finally, measurement of tax progressivity – particularly when based on microdata – can be extremely resource intensive, and this must be taken into account, particularly in the current context where the progressivity of a large number of countries needs to be assessed.

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