Each of these three progressivity concepts, and the metrics that can be adopted under each, are examined in more detail in the next three sections of the paper. Effective progressivity is considered first as it is the most commonly adopted approach.

# 2 Measuring effective tax progressivity

The most common and widely applicable type of progressivity measurement is effective progressivity measurement. Effective progressivity metrics consider the actual tax liabilities of individuals or households rather than just the tax schedules. Subject to data availability, effective progressivity metrics can be calculated for the PIT, SSC, VAT as well as various excise taxes (such as those on fuels used for heating and transport, electricity, and on alcohol, tobacco and sugar-sweetened beverages).

This section first discusses data requirements and key methodological issues before presenting the most commonly used global measures of structural progressivity. It then presents examples of their use, focusing where possible on IDA countries, and finally summarizes the strengths and weaknesses of effective progressivity metrics.

## 2.1 Data, welfare metric, and unit of analysis

To calculate effective progressivity metrics, data is needed that provides an appropriate welfare metric to rank individuals or households (or at least of a set of disaggregated groupings of individuals or households) from poorest to richest, and enables the tax liabilities of each individual or household (or group) to be ascertained. Ideally, microdata is used, although (simplified) calculations can also be made with semiaggregated data, such as average income levels across percentiles or deciles.

To examine the progressivity of the PIT, income survey data will typically be used, which can be weighted from the representative sample to reflect the population. Income and household characteristics are included to enable the actual tax liabilities of taxpayers to be modelled, including non-standard reliefs where the required information is included in the survey. To examine the VAT or excise taxes, household budget survey data can be used as these surveys typically provide a breakdown of total consumption across a large number of consumption categories, enabling differentiation between consumption subject to different VAT rates and different excise tax rates.

Before calculating progressivity or other metrics, some adjustments (in addition to basic data cleaning) may be made to the microdata to adjust for limitations associated with that data. For example, a common problem with income survey data is that incomes at the top end are often underreported, and various methods can be applied to adjust for this (see, e.g., Blanchet et al., 2022; Ruiz and Woloszko, 2016). Meanwhile, in the absence of income survey data, if consumption survey data is being used to model the PIT, adjustments based on savings patterns can be made to proxy better for income data. A parallel paper examines such techniques (including greater use of tax return microdata) in detail. Further discussion of these techniques is left to that paper.

Additional adjustments may also be made to specify the welfare metric and the unit of analysis. For PIT analysis, where income data is available it will be the preferred starting point in calculating a welfare 8