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 estimate uncertai… Show more
“…The controls comprise economic, social, and institutional variables that could be common causes of MM and regime transformations. With regards to economic factors, we include gross domestic product (GDP) per capita to measure economic development, the GDP growth rate (Fariss et al, 2022) and gas and oil production (Ross & Mahdavi, 2015) to capture a country's resource wealth. In addition, we add information on the average years of education (Barro & Lee, 2013) and population size (Fariss et al, 2022).…”
Section: Mobilization and The Quality Of Democracymentioning
confidence: 99%
“…With regards to economic factors, we include gross domestic product (GDP) per capita to measure economic development, the GDP growth rate (Fariss et al, 2022) and gas and oil production (Ross & Mahdavi, 2015) to capture a country’s resource wealth. In addition, we add information on the average years of education (Barro & Lee, 2013) and population size (Fariss et al, 2022). Since societies with ongoing conflicts are more likely to experience MM and are often governed by unstable regimes, we include binary indicators for civil conflict from UCDP (Gleditsch et al, 2002; Pettersson et al, 2019) as well as an index of ethnic fractionalization (Dražanová, 2020).…”
Section: Mobilization and The Quality Of Democracymentioning
Mass mobilization (MM) is an important driver of political change. While some citizens organize in favor of more democratic institutions, others take to the streets to support an authoritarian status quo. This article introduces measures of pro-democratic and pro-autocratic MM using expert assessments for 179 polities from 1900–2021. The data allow us to trace patterns in MM over time, across regions and regime types. We use this new data to systematically analyze the relationship between both types of mobilization and regime change. We confirm the findings of the literature on contentious democratic politics, and our analysis of autocratic mobilization allows us to make sense of the controversy in the literature on “bad actors” in civil society. We show that MM in favor of autocracy negatively affects democracy, making a case for specifying the goals of the actors involved in contentious politics to more precisely understand their impact.
“…The controls comprise economic, social, and institutional variables that could be common causes of MM and regime transformations. With regards to economic factors, we include gross domestic product (GDP) per capita to measure economic development, the GDP growth rate (Fariss et al, 2022) and gas and oil production (Ross & Mahdavi, 2015) to capture a country's resource wealth. In addition, we add information on the average years of education (Barro & Lee, 2013) and population size (Fariss et al, 2022).…”
Section: Mobilization and The Quality Of Democracymentioning
confidence: 99%
“…With regards to economic factors, we include gross domestic product (GDP) per capita to measure economic development, the GDP growth rate (Fariss et al, 2022) and gas and oil production (Ross & Mahdavi, 2015) to capture a country’s resource wealth. In addition, we add information on the average years of education (Barro & Lee, 2013) and population size (Fariss et al, 2022). Since societies with ongoing conflicts are more likely to experience MM and are often governed by unstable regimes, we include binary indicators for civil conflict from UCDP (Gleditsch et al, 2002; Pettersson et al, 2019) as well as an index of ethnic fractionalization (Dražanová, 2020).…”
Section: Mobilization and The Quality Of Democracymentioning
Mass mobilization (MM) is an important driver of political change. While some citizens organize in favor of more democratic institutions, others take to the streets to support an authoritarian status quo. This article introduces measures of pro-democratic and pro-autocratic MM using expert assessments for 179 polities from 1900–2021. The data allow us to trace patterns in MM over time, across regions and regime types. We use this new data to systematically analyze the relationship between both types of mobilization and regime change. We confirm the findings of the literature on contentious democratic politics, and our analysis of autocratic mobilization allows us to make sense of the controversy in the literature on “bad actors” in civil society. We show that MM in favor of autocracy negatively affects democracy, making a case for specifying the goals of the actors involved in contentious politics to more precisely understand their impact.
“…However, data vintaging calls into question the robustness of existing findings, since many vintages provide conflicting information. With few exceptions (Boehmer et al, 2011;Hollyer et al, 2014;Inklaar and Prasada Rao, 2017;Fariss et al, 2022), researchers did not examine these issues until recently Figure 2.…”
Section: The Logic Behind Data Vintagingmentioning
confidence: 99%
“…However, data vintaging calls into question the robustness of existing findings, since many vintages provide conflicting information. With few exceptions (Boehmer et al, 2011; Hollyer et al, 2014; Inklaar and Prasada Rao, 2017; Fariss et al, 2022), researchers did not examine these issues until recently Figure 2.Figure 2.Available observations by country, 1980–1999.These maps show the number of complete observations available by country for the variables included in de Soysa and Neumayer’s analysis. Thirty countries (including Angola and Sudan) are included in the original data, but not in the 2022 WDI; conversely, the 2022 WDI includes Israel, Laos, Oman, and the United States, all of which missing from the original data.…”
Section: The Logic Behind Data Vintagingmentioning
Macroeconomic variables like unemployment, inflation, trade, or GDP are not set in stone: they are preliminary estimates that are constantly revised by statistical agencies. These data revisions, or data vintages, often provide conflicting information about the size of a country’s economy or its level of development, reducing our confidence in established findings. Would researchers come to different conclusions if they used different vintages? To answer this question, I survey all articles published in a top political science journal between 2005 and 2020. I replicate three prominent articles and find that the use of different vintages can lead to different statistical results, calling into question the robustness of otherwise rigorous empirical research. These findings have two practical implications. First, researchers should always be transparent about their data sources and vintages. Second, researchers should be more modest about the precision and accuracy of their point estimates, since these estimates can mask large measurement errors.
“…Third, state power in the international system is both a reflection of and reflects upon the decision to dispatch diplomatic envoys. To get broader coverage than the conventional Composite Index of National Capability (CINC) scores from COW, particularly for the historical sample, I have proxied for power through the size of a country's economy, as measured by the log of its gross domestic product (GDP) (with data from Fariss et al, 2022). 7 Fourth, to control for regime affinity, I also include the degree of polyarchy (Dahl, 1971) in both parties to the dyad, indicating the extent to which multiparty elections with universal suffrage are held, in combination with freedoms of organization and expression, again drawing on the V-Dem data (Teorell et al, 2019).…”
The aim of this article is to explore the establishment of diplomatic representation as a measure of de facto recognition by other state units and to explain its causes in the “long 19th century” (1817–1914) and the post–World War II (WWII) era (1950–2000). Drawing on the Correlates of War diplomatic exchange data, the article explores the underlying drivers of dyadic acts of recognition in two series of logistic regression analyses, one for each time period. The results indicate that, also when taking alternative explanations into account, recognition of other states in the international system was in the 19th century at least based on one general principle: that of recognizing other de facto states. In the post-WWII era, contrary to expectations, this principle was still in effect. De facto statehood can thus be argued to constitute a rather stable norm for recognition in the international system.
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