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Income inequality: Palma ratio (after tax)

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PovertyEconomic Inequality

What you should know about this indicator

  • Income is ‘post-tax’ — measured after taxes have been paid and most government benefits have been received.
  • Income has been equivalized – adjusted to account for the fact that people in the same household can share costs like rent and heating.
Income inequality: Palma ratio (after tax)
The Palma ratio is a measure of inequality that divides the share received by the richest 10% by the share of the poorest 40%. Higher values indicate higher inequality.
Source
Luxembourg Income Study (2024) – with major processing by Our World in Data
Last updated
June 13, 2024
Next expected update
June 2025
Date range
1963–2022

Sources and processing

This data is based on the following sources

The Luxembourg Income Study Database (LIS) is the largest available income database of harmonized microdata collected from about 50 countries in Europe, North America, Latin America, Africa, Asia, and Australasia spanning five decades.

Harmonized into a common framework, LIS datasets contain household- and person-level data on labor income, capital income, pensions, public social benefits (excl. pensions) and private transfers, as well as taxes and contributions, demography, employment, and expenditures.

Retrieved on
June 19, 2024
Retrieved from
https://www.lisdatacenter.org/our-data/lis-database/
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Luxembourg Income Study (LIS) Database, http://www.lisdatacenter.org (multiple countries; June 2024). Luxembourg: LIS.

How we process data at Our World in Data

All data and visualizations on Our World in Data rely on data sourced from one or several original data providers. Preparing this original data involves several processing steps. Depending on the data, this can include standardizing country names and world region definitions, converting units, calculating derived indicators such as per capita measures, as well as adding or adapting metadata such as the name or the description given to an indicator.

At the link below you can find a detailed description of the structure of our data pipeline, including links to all the code used to prepare data across Our World in Data.

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Notes on our processing step for this indicator

We create the Luxembourg Income Study data from standardized household survey microdata available in their LISSY platform. The estimations follow the methodology available in LIS, Key Figures and DART platform.

We obtain after tax income by using the disposable household income variable (dhi).

We estimate before tax income by calculating the sum of income from labor and capital (variable hifactor), cash transfers and in-kind goods and services from privates (hiprivate) and private pensions (hi33). We do this only for surveys where tax and contributions are fully captured, collected or imputed.

We obtain after tax income (cash) by using the disposable household cash income variable (dhci).

We convert income data from local currency into international-$ by dividing by the LIS PPP factor, available as an additional database in the LISSY platform.

We top and bottom-code incomes by replacing negative values with zeros and setting boundaries for extreme values of log income: at the top Q3 plus 3 times the interquartile range (Q3-Q1), and at the bottom Q1 minus 3 times the interquartile range.

We equivalize incomes by dividing each household observation by the square root of the number of household members (nhhmem). Per capita estimates are calculated by dividing incomes by the number of household members.

We obtain Gini coefficients by using Stata’s ineqdec0 function. We set weights as the product between the number of household members (nhhmem) and the normalized household weight (hwgt). We also calculate mean and median values from this function.

Reuse this work

  • All data produced by third-party providers and made available by Our World in Data are subject to the license terms from the original providers. Our work would not be possible without the data providers we rely on, so we ask you to always cite them appropriately (see below). This is crucial to allow data providers to continue doing their work, enhancing, maintaining and updating valuable data.
  • All data, visualizations, and code produced by Our World in Data are completely open access under the Creative Commons BY license. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.

Citations

How to cite this page

To cite this page overall, including any descriptions, FAQs or explanations of the data authored by Our World in Data, please use the following citation:

“Data Page: Income inequality: Palma ratio (after tax)”, part of the following publication: Joe Hasell, Max Roser, Esteban Ortiz-Ospina and Pablo Arriagada (2022) - “Poverty”. Data adapted from Luxembourg Income Study. Retrieved from https://ourworldindata.org/grapher/palma-ratio-after-tax-lis [online resource]
How to cite this data

In-line citationIf you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:

Luxembourg Income Study (2024) – with major processing by Our World in Data

Full citation

Luxembourg Income Study (2024) – with major processing by Our World in Data. “Income inequality: Palma ratio (after tax)” [dataset]. Luxembourg Income Study, “Luxembourg Income Study (LIS)” [original data]. Retrieved July 26, 2024 from https://ourworldindata.org/grapher/palma-ratio-after-tax-lis