2021
DOI: 10.31235/osf.io/8dmqg
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New Estimates of Over 500 Years of Historic GDP and Population Data

Abstract: Gross Domestic Product (GDP), GDP per capita, and population are central to the study of politics and economics broadly, and conflict processes in particular. Despite the prominence of these variables in empirical research, existing data lack historical coverage and are assumed to be measured without error. We develop a latent variable modeling framework that expands data coverage (1500 A.D--2018 A.D) and, by making use of multiple indicators for each variable, provides a principled framework to estimate uncer… Show more

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Cited by 3 publications
(8 citation statements)
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“…The result for the interaction (pre-1939), plotted in Figure A4, is similar to the main result. Using the Fariss et al (2017) data on GDP per capita strengthens the conditional relationship for the earlier period in the base model (at p < 0.05), and it even becomes significant (at p < 0.1) with additional controls (Table A31).…”
Section: Robustness Testsmentioning
confidence: 83%
See 2 more Smart Citations
“…The result for the interaction (pre-1939), plotted in Figure A4, is similar to the main result. Using the Fariss et al (2017) data on GDP per capita strengthens the conditional relationship for the earlier period in the base model (at p < 0.05), and it even becomes significant (at p < 0.1) with additional controls (Table A31).…”
Section: Robustness Testsmentioning
confidence: 83%
“…Next, since we lose quite some observations with some of our controls, we run the same models with an imputed version of the most important control variable, GDP per capita (Tables A7-A9). We use the variable constructed by Fariss et al (2017), which is based on a range of sources, including the Maddison data. The results basically remain the same, although suffrage becomes insignificant in the base model for the pre-1939 period (Table A8, Model 3).…”
Section: Robustness Testsmentioning
confidence: 99%
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“…The GDP data are from (Fariss et al, 2017), who estimate (logged) income level by using a dynamic latent trait model and drawing on information from different GDP datasets. We use their estimates benchmarked in the long time series from the Maddison project (Bolt and van Zanden, 2013).…”
Section: Economic Crisesmentioning
confidence: 99%
“…We continue with a continuous growth measure and return to the t − 1 lag, but use GDP data from the Maddison project (Bolt and van Zanden, 2013) instead of Fariss et al (2017). This change reduces the number of observations from 18,243 country-years in Model 1, Table 1 to 12,331 in Model 1, Table 2.…”
Section: Robustness Testsmentioning
confidence: 99%