Following our successful publication of our Global Living Wage Dataset in 2020, we decided to update and improve the dataset for the reference year 2022 (now updated to the 2023 version, click on the link above). We believe that the lack of availability of data and the cost barrier to accessing good quality data is slowing down the efforts of many organizations on the topic of living wage. Our objective is to fill this gap and allow any organization to get started on the topic using our dataset.
We don't intend to replace other living wage datasets, which would anyway be a good source of data should you need additional data (e.g. at sub-national level, or for a specific context) to continue deployment your wages strategy.
This Global Living Wage Dataset dataset relies on public cost of living data drawn from Numbeo from which we built a living wage model. We completed the dataset on countries for which direct cost of living data was not available by developing a linear regression model based on purchasing power parity data (World Bank 2021). This approach allows us to publish:
Living wage estimates for 217 countries and territories (98 with primary data from Numbeo, clearly identified in the dataset)
4 types of living wages to chose from: typical family, standard family, single individual, single working parent family
Fully transparent methodology provided in the dataset file (XLS), considering Anker&Anker 2017 as the international standard
The following figures are analysis output from the Global Living Wage Dataset, to support its use and interpretation.
The next figures shows the Global Living Wage Dataset, its high coverage and an idea of the range of living wage (typical family) for each country.
The choice of living wage type (among the four types made available in the dataset) influences greatly the living wage value, although regional characteristics are even more important at determining the living wage value.
The type of economy and income group classification of countries (using the World Bank classification) is a good predictor of the value of the living wage. In general, cost of living is relatively correlated to price and income levels.
As can be see in the next graph, the correlation between price levels (PPP) and living wages exists, although it is not perfect (R2 = 0.65). Additional factors (social organizations, economics, employment and household composition, etc) influence the final value of living wage.
Finally, we provide a comparison of the previous dataset published in 2020 by Valuing Impact and this new dataset valid for 2022. The new dataset (2022) is represented on the Y-axis.
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