Replication data for: Explaining Rare Events in International Relations (doi:10.7910/DVN/RNSU7V)

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Part 1: Document Description
Part 2: Study Description
Part 3: Data Files Description
Part 4: Variable Description
Part 5: Other Study-Related Materials
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Replication data for: Explaining Rare Events in International Relations

doi:10.7910/DVN/RNSU7V

Harvard Dataverse

2007-11-28

4

King, Gary; Zeng, Langche, 2007, "Replication data for: Explaining Rare Events in International Relations", https://doi.org/10.7910/DVN/RNSU7V, Harvard Dataverse, V4, UNF:3:vyct3c8fMCdWOdp03NUhaA== [fileUNF]

Replication data for: Explaining Rare Events in International Relations

doi:10.7910/DVN/RNSU7V

King, Gary (Harvard University)

Zeng, Langche (UC San Diego)

2001

Harvard Dataverse

Harvard Dataverse

2006

2001

https://doi.org/10.7910/DVN/RNSU7V

Social Sciences

Some of the most important phenomena in international conflict are coded s "rare events data," binary dependent variables with dozens to thousands of times fewer events, such as wars, coups, etc., than "nonevents". Unfortunately, rare events data are difficult to explain and predict, a problem that seems to have at least two sources. First, and most importantly, the data collection strategies used in international conflict are grossly inefficient. The fear of collecting data with too few events has led to data collections with huge numbers of observations but relatively few, and poorly measured, explanatory variables. As it turns out, more efficient sampling designs exist for making valid inferences, such as sampling all available events (e.g., wars) and a tiny fraction of non-events (peace). This enables scholars to save as much as 99% of their (non-fixed) data collection costs, or to collect much more meaningful explanatory variables. Second, logistic regression, and other commonly used statistical procedures, can underestimate the probability of rare events. We introduce some corrections that outperform existing methods and change the estimates of absolute and relative risks by as much as some estimated effects reported in the literature. We also provide easy-to-use methods and software that link these two results, enabling both types of corrections to work simultaneously. <br /><br /> You may also be interested in the companion methods article to this one, <a href="http://gking.harvard.edu/files/gking/files/0s.pdf" target="_blank">Logistic Regression in Rare Events Data</a>, as well as related work, <a href="http://gking.harvard.edu/files/abs/1s-enc-abs.shtml" target="_blank">Inference in Case-Control Studies.</a> <br /><br /> See also: <a href="http://gking.harvard.edu/category/research-interests/applications/international-conflict" target="_blank">Internation al Conflict</a>, <a href="http://gking.harvard.edu/category/research-interests/methods/rare-events" target="_blank">Rare Events</a>

This dataset is made available without information on how it can be used. You should communicate with the Contact(s) specified before use.

King, Gary; Zeng, Langche, 2001, "Explaining Rare Events in International Relations," International Organization, 55, 3, 693-715: <a href="http://j.mp/iHyh68" target="_blank">Link to article</a>

King, Gary; Zeng, Langche, 2001, "Explaining Rare Events in International Relations," International Organization, 55, 3, 693-715: <a href="http://j.mp/iHyh68" target="_blank">Link to article</a>

• Number of cases: 303814

• No. of variables per record: 13

• Type of File: text/tab-separated-values

UNF:3:8p3E5xbsx0MQeREYNgGrjQ==

Replication of the full sample model in table 1

• Number of cases: 7190

• No. of variables per record: 16

• Type of File: text/tab-separated-values

UNF:3:WtZaHZxhnJA+1nAQCxEcLg==

Data for replication of results in the state failure data

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• YEAR
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• V5
• V6
• V7
• V8
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• V12
• V13
• V14
• V15
• V16

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Huth.dat

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File for replication of results in table 2, ASCII format

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Huth.out

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Output file for replication of results in table 2, Gauss format

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Huth1.prg

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File for replication of results in table 2, Gauss code

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LogisticRegressionArticle.pdf

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Article related to this study: Logistic Regression in Rare Events Data

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Model.do

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Output file for replication of the full sample model in table 1, Stata code

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model.dta

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Data file replicating the full sample model in table 1, in Stata format

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Model.Log

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output for replication of the full sample model in table 1

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Readme

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Detailed information on data and documentation files for this study

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SF.dat

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File for replication of results in the state failure data, ASCII format

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SF.out

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File for replication of results in the state failure data, Gauss output file

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SF.prg

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Program file for replication of results in the state failure data, Gauss code file

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