The dataset developed by Michail Moatsos (2021) provides a comprehensive long-term series on global extreme poverty from 1820 to 2018. It is constructed by combining and harmonizing a wide array of historical sources and methodologies to ensure consistency over time.

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Estimating historical poverty levels, particularly before the 20th century, requires synthesizing various forms of data, including records on economic output, population, and inequality. The data for 1820-2018 is based on the work of Michail Moatsos (2021), which combines multiple historical sources and methodologies to construct a consistent long-term series on global extreme poverty.

Data Sources for Historical Poverty Estimates

Economic output data, such as historical Gross Domestic Product (GDP) or national income accounts, is a fundamental component for estimating historical poverty. This data helps establish the overall economic resources available within a society at a given time.

Economic Output Data for Historical Poverty Estimates

Accurate historical population counts are essential for calculating poverty rates. This data, often derived from censuses, administrative records, or demographic reconstructions, is used to determine the size of the population living below the poverty line.

Population Data for Historical Poverty Estimates

Data on inequality, such as income or wealth distribution, is crucial for historical poverty estimation. It allows researchers to understand how economic output is distributed across the population, which is necessary to estimate the number of people falling below a specific poverty threshold.

Inequality Data for Historical Poverty Estimates

Michail Moatsos (2021) Global Extreme Poverty Dataset

This figure illustrates the proportion of the global population that lived in extreme poverty over the period from 1820 to 2018.

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