Historical GDP/GDPPC/Population

Summary

The historical GDP, population, and GDPPC data is pulled from the article "New Estimates of Over 500 Years of Historic GDP and Population Data" by Fariss et al. (2022). The article proposed a dynamic latent variable model to address three major issues in historical GDP, population, and GDP per capita data. Namely 1) missing data, 2) measurement uncertainty, 3) systematic bias across sources.

The authors built a dynamic latent variable model that combined multiple historical and contemporary datasets. The work was supported by the Security and Political Economy (SPEC) Lab at the University of Southern California.

For population and GDP per capita, the latent trait model parameters were constructed based on following formula:

(Farris et al., 2022)

Tables in IFs

Variable	Table	Definition	Last IFs Update	UsedInPreprocessor
HistoricalGDP%mean	SeriesHistoricalGDP%mean	Mean of historical GDP values extended with a Latent Variable Modeling Framework.	7/23/2025	0
HistoricalGDP%meanLog10	SeriesHistoricalGDP%meanLog10	Mean of log10 of historical GDP values extended with a Latent Variable Modeling Framework.	7/23/2025	0
HistoricalGDP%sd	SeriesHistoricalGDP%sd	SD of historical GDP values extended with a Latent Variable Modeling Framework.	7/23/2025	0
HistoricalGDP%sdlog10	SeriesHistoricalGDP%sdlog10	SD of log10 of historical GDP values extended with a Latent Variable Modeling Framework.	7/23/2025	0
HistoricalGDPPC%mean	SeriesHistoricalGDPPC%mean	Mean of historical GDP per capita values extended with a Latent Variable Modeling Framework.	7/23/2025	0
HistoricalGDPPC%meanlog10	SeriesHistoricalGDPPC%meanlog10	Mean of log10 of historical GDP per capita values extended with a Latent Variable Modeling Framework.	7/23/2025	0
HistoricalGDPPC%sd	SeriesHistoricalGDPPC%sd	SD of historical GDP per capita values extended with a Latent Variable Modeling Framework.	7/23/2025	0
HistoricalGDPPC%sdlog10	SeriesHistoricalGDPPC%sdlog10	SD of log10 of historical GDP per capita values extended with a Latent Variable Modeling Framework.	7/23/2025	0
HistoricalPOP%mean	SeriesHistoricalPOP%mean	Mean of historical population values extended with a Latent Variable Modeling Framework.	7/23/2025	0
HistoricalPOP%meanlog10	SeriesHistoricalPOP%meanlog10	Mean of log10 of historical population values extended with a Latent Variable Modeling Framework.	7/23/2025	0
Historicalpop%sd	SeriesHistoricalpop%sd	SD of historical population values extended with a Latent Variable Modeling Framework.	7/23/2025	0
HistoricalPOP%sdlog10	SeriesHistoricalPOP%sdlog10	SD of log10 of historical population values extended with a Latent Variable Modeling Framework.	7/23/2025	0

Data Pulling Instructions

Step 1:

To pull data from this paper, go to:

https://journals.sagepub.com/doi/full/10.1177/00220027211054432

In the Acknowledgements section, there is a link to the predicted data:

https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/FALCGS

Step 2:

Download the most updated datafiles (in this example, in 2024).

Data Notes

1.     The paper was published in 2022. In 2024, the author shared an updated version of the datafile.

2.     The data files are in .rds format, if using R, open the file and it will automatically import; if using python, use the read_r function in the pyreadr package.

3.     The datafile included predicted values as well as datasets used in the modeling, slice “latent_gdp”, “latent_gdppc”, “latent_pop” from the indicator column of corresponding datafiles for the predicted values.

4.     GWNO codes and corresponding country names were provided in the supplementary files of the original paper. However, the supplementary only provided corresponding codes for 217 countries/regions and the dataset had 225 countries/regions (missing: 89,99,327, 396,397, 563,564,711). For the additional gwno codes, use this link for corresponding country names: http://ksgleditsch.com/data-4.html.