The shape of things to come? Expanding the inequality and grievance model for civil war forecasts with event data

Chiba, Daina; Gleditsch, Kristian Skrede

doi:10.1177/0022343316684192

Cited by 46 publications

(45 citation statements)

References 43 publications

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“…33 In this way, elections may appear to be more violent, when in reality, a greater number of media sources for the coder to intake may artificially inflate the true number of violent events. To account for this possible source of bias, we have included in the dataset a weight that users can employ to weight the event data by a per capita measure of media coverage.…”

Section: Electoral Violence Data and Coding Proceduresmentioning

confidence: 99%

The Dataset of Countries at Risk of Electoral Violence

Birch

Muchlinski

2017

Terrorism and Political Violence

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Electoral violence is increasingly affecting elections around the world, yet researchers have been limited by a paucity of granular data on this phenomenon. This paper introduces and describes a new dataset of electoral violence-the Dataset of Countries at Risk of Electoral Violence (CREV)-that provides measures of 10 different types of electoral violence across 642 elections held around the globe between 1995 and 2013. The paper provides a detailed account of how and why the dataset was constructed, together with a replication of previous research on electoral violence. We introduce this dataset by demonstrating that the CREV, while measuring the same underlying phenomena as other datasets on electoral violence, provides researchers with the ability to draw more nuanced conclusions about the causes and consequences of violence that occurs in connection with the electoral process. We also present and analyze descriptive data from the CREV dataset.

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Section: Electoral Violence Data and Coding Proceduresmentioning

confidence: 99%

The Dataset of Countries at Risk of Electoral Violence

Birch

Muchlinski

2017

Terrorism and Political Violence

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“…Since the model’s performance has declined significantly in the past decade, those using the model should update their empirical and mental models accordingly. Of course, this is already being done, including by researchers affiliated with the PITF (Chiba and Gleditsch 2017; Nyseth Brehm 2017; Ward and Beger 2017). But many of these advances have been motivated by a desire to probe the validity of the theoretical model of instability that underpins the GEA model or interrogate specific types of instability in further detail, rather than the simple fact that the model appears to have stopped working – at least for the most recent decade.…”

Section: Why Does This Matter?mentioning

confidence: 99%

The Future is a Moving Target: Predicting Political Instability

Bowlsby

Chenoweth

Hendrix

et al. 2019

Brit. J. Polit. Sci.

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Previous research by Goldstone et al. (2010) generated a highly accurate predictive model of state-level political instability. Notably, this model identifies political institutions – and partial democracy with factionalism, specifically – as the most compelling factors explaining when and where instability events are likely to occur. This article reassesses the model’s explanatory power and makes three related points: (1) the model’s predictive power varies substantially over time; (2) its predictive power peaked in the period used for out-of-sample validation (1995–2004) in the original study and (3) the model performs relatively poorly in the more recent period. The authors find that this decline is not simply due to the Arab Uprisings, instability events that occurred in autocracies. Similar issues are found with attempts to predict nonviolent uprisings (Chenoweth and Ulfelder 2017) and armed conflict onset and continuation (Hegre et al. 2013). These results inform two conclusions: (1) the drivers of instability are not constant over time and (2) care must be exercised in interpreting prediction exercises as evidence in favor or dispositive of theoretical mechanisms.

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“…A model that uses news sources as predictor for the same set of outcomes is better calibrated. Chiba & Gleditsch (2017) explore whether dynamic information about mobilization and the behavior of actors from event data can help improve an existing forecast model of civil war that relies on relatively static measures of horizontal inequality (Buhaug, Cederman & Gleditsch, 2014). While their findings suggest some support, the contribution of events data to improving predictive power is somewhat limited.…”

Section: The Contributionsmentioning

confidence: 99%

Introduction

Hegre

Metternich

Nygård

et al. 2017

Journal of Peace Research

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Prediction and forecasting have now fully reached peace and conflict research. We define forecasting as predictions about unrealized outcomes given model estimates from realized data, and predictions more generally as the assignment of probability distributions to realized or unrealized outcomes. Increasingly, scholars present within-and outof-sample prediction results in their publications and sometimes even forecasts for unrealized, future outcomes. The articles in this special issue demonstrate the ability of current approaches to forecast events of interest and contributes to the formulation of best practices for forecasting within peace research. We highlight the role of forecasting for theory evaluation and as a bridge between academics and policymakers, summarize the contributions in the special issue, and provide some thoughts on how research on forecasting in peace research should proceed. We suggest some best practices, noting the importance of theory development, interpretability of models, replicability of results, and data collection.Keywords forecasting, out of sample evaluation, peace research, prediction, theory testingNo matter how I turn it over in my mind, the number one task of peace research always turns out to be that of prediction [...] (J David Singer, 1973)

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The shape of things to come? Expanding the inequality and grievance model for civil war forecasts with event data

Cited by 46 publications

References 43 publications

The Dataset of Countries at Risk of Electoral Violence

The Dataset of Countries at Risk of Electoral Violence

The Future is a Moving Target: Predicting Political Instability

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