New Estimates of Over 500 Years of Historic GDP and Population Data

• Published date: 01 April 2022
• Date: 01 April 2022
• Author: Jonathan N. Markowitz,Therese Anders,Christopher J. Fariss,Miriam Barnum
• DOI: 10.1177/00220027211054432
• Subject Matter: Data Set Feature

Data Set Feature

Journal of Conf‌lict Resolution

2022, Vol. 66(3) 553–591

© The Author(s) 2022

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DOI: 10.1177/00220027211054432

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New Estimates of Over

500 Years of Historic GDP

and Population Data

Christopher J. Fariss

1

, Therese Anders

2

,

Jonathan N. Markowitz

2

, and Miriam Barnum

2

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 AD–2018 AD) and, by making

use of multiple indicators for each variable, provides a principled framework to es-

timate uncertainty for values for all country-year variables relative to one another.

Expanded temporal coverage of estimates provides new insights about the relationship

between development and democracy, conf‌lict, repression, and health. We also

demonstrate how to incorporate uncertainty in observational models. Results show

that the relationship between repression and development is weaker than models that

do not incorporate uncertainty suggest. Future extensions of the latent variable model

can address other forms of systematic measurement error with new data, new

measurement theory, or both.

Keywords

gross domestic product, population, GDP per capita, latent variables, measurement,

construct validity

1

Department of Political Science, University of Michigan, Ann Arbor, MI, USA

2

School of International Relations, University of Southern California, Los Angeles, CA, USA

Corresponding Author:

Christopher J. Fariss, Department of Political Science, University of Michigan, 426 Thompson Street, Ann

Arbor, MI 48109-1382, USA.

Email: cjf0006@gmail.com

Introduction

Gross domestic product (GDP), GDP per capita (GDPPC), and population play a vital

role in empirical social science. Moreover, they are key variables in the study of

international and domestic conf‌lict processes in particular. Despite the prominence of

these variables, existing data used to operationalize them suffers from three problems:

(1) measurement coverage (missingness), (2) measurement uncertainty, and (3)

measurement bias. First, there is a lack of economic cross-country data coverage prior

to 1950 (Gleditsch 2002), or data are available with very limited temporal coverage

even though many other datasets of interest cover variables beginning in the 1800s.

1

This leaves researchers unable to consistently estimate the relationship between key

variables such as economic development and democratization, conf‌lict, repression, or

health prior to this date. Second, measurement error arises because of imprecision or

disagreement in available data, which can mask real relationships or exacerbate false

ones. Though scholars are aware that data suffers from measurement error, existing

estimates of these variables provide only point estimates and offer no method for

quantifying uncertainty (measurement error). Third, existing models for estimating

GDP,populatio n, and GDP per capita offer no way to correct for any systematic bias in

the data generating process. As previous research has demonstrated, failing to correct

for measurement bias leads scholars to draw incorrect inferences about the relationships

in their data. We develop a latent variable model of GDP, GDP per capita, and

population that provides remedies for each of these issues.

2

First, the latent variable model generates estimates of cross-national data coverage

back in time by several centuries to 1500. These estimates are important not only for

scholars assessingexisting explanationsof outcomes in earlier periods of history, but also

scholars interested in comparing inferences generated from data across time periods.

Given that most of the major interstate wars occurred before 1950, these data will be

particularly usefulto scholars studying the causes of war, as well as the debate about its

relative decline over time (e.g., Fazal 2014;Lacina, Gleditsch and Russett 2006). Until

now, researchers have been unable to estimate these long-term relationships.

For example, existing research argues that economic development reduces the risk

of conf‌lict both by contributing to the democratization process of states in the in-

ternational system and by making war more costly (Hegre 2000). As Souva and Prins

state, “democratic regimes are about 37% less likely to initiate fatal militarized disputes

given an average level of GDP per capita”(Souva and Prins 2006, 194). However, other

research argues that the relationship with economic development is curvilinear, for both

democratization (Treisman 2020) and conf‌lict (Boehmer and Sobek 2005;Gartzke

2012). To date, the relationships between economic development and democratization,

repression, or conf‌lict have not been estimated with complete cross-national data prior

to 1950. Scholars have instead relied on proxy-measures such as energy consumption

per capita (e.g., Markowitz, McMahon and Fariss 2019), shipping and rail costs (e.g.,

Lake 2009;Markowitz and Fariss 2013), or simple linear interpolation of GDP per

capita (e.g., Treisman 2020) to account for long-term economic variation. The new

554 Journal of Conf‌lict Resolution 66(3)

estimates we present in this article allow researchers to evaluate whether these em-

pirical relationships are limited to the post-1950 period, or whether they generalize to

earlier time periods (using more precise data with better coverage). We show that many

correlational patterns between GDP per capita and these other variables vary con-

siderably over time, which means that relationships for the post-1950 period do not

necessarily generalize to periods prior to 1950. Critically, this also suggests that these

relationships might change in the future.

Second, our new latent variable model provides estimates of the relative level of

uncertaintyfor each country-year unit by accountingfor variation in the level of coverage

within and disagreement between componentindicators. This is useful because it allows

researchers to evaluate whether measurement error in the data (expressed as the level of

uncertainty withwhich each country-year unit is accuratelymeasured) is large enough to

alter the size, or even the direction, of the effect of a given explanatory variable.

For illustration,we demonstrate that measurementuncertainty may be large enough to

change the estimated magnitude of the effect of development (GDP per capita) on

repression. If models do not incorporate uncertainty, then the researcher cannot rule out

the possibilitythat the statistical associations of GDP, GDP per capita,or population with

some othervariable are not a false-negativeresult (Type 2 erroror attenuation bias), orthe

related possibility that the relationship of these variables are a false-positiveresult (Type

1 error). In modelswith multiple indicators (i.e.,multiple-variable regression),bias due to

measurement error is not always attenuating if un-modeled, higher-order interactions

exist. Incorporating measurement uncertainty of variables addresses these diff‌icult-to-

model issues and can be further explored with non-parametric regression techniques. In

sum, incorporatinguncertainty into a regressionmodel provides evidencethat effect sizes

are probabilistically distinguishable from zero even when we cannot measure a right-

hand side variableperfectly (an assumptionof standard regression models).Though some

existing models that include these variables may have under-estimated an effect size,

others may have over-estimated effect size or even incorrectly reversed the direction of

the effect. The implications are potentially profound given the wide usage of these

variables across the social sciences.

Third, the measurement model provides a framework for incorporating theoretical

knowledge about the data generating process for information used in the measurement

of each variable that can correct for potential bias in the existing data. We describe how

the latent variable model we develop in this article can be further developed to address

measurement bias and point to other areas of scholarship that have successfully built

and extended latent variable models to address measurement bias.

3

The new estimates provide ample opportunities for future scholarship to re-visit

existing debates and investigate new research questions relating to war, peace, eco-

nomic development, and health and well-being, as well as the information necessary to

reduce bias due to measurement uncertainty in GDP, GDP per capita, or population

variables. The key output from the model is predicted intervals of the original source

variables in the original unit-of-measurement, in addition to the relative level of

uncertainty for each country-year estimate in the form of a standard deviation. The level

Fariss et al. 555