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Replication Data for: Prediction, Proxies, and Power

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title Replication Data for: Prediction, Proxies, and Power
 
Identifier https://doi.org/10.7910/DVN/FPYKTP
 
Creator Kenkel, Brenton
Carroll, Robert
 
Publisher Harvard Dataverse
 
Description Many enduring questions in international relations theory focus on power relations, so it is important that scholars have a good measure of relative power. The standard measure of relative military power, the capability ratio, is barely better than random guessing at pre- dicting militarized dispute outcomes. We use machine learning to build a superior proxy, the Dispute Outcome Expectations score, from the same underlying data. Our measure is an order of magnitude better than the capability ratio at predicting dispute outcomes. We replicate Reed et al. (2008) and find, contrary to the original conclusions, that the probability of conflict is always highest when the state with the least benefits has a preponderance of power. In replications of 18 other dyadic analyses that use power as a control, we find that replacing the standard measure with DOE scores usually improves both in-sample and out-of-sample goodness of fit.



Note:This analysis involves many layers of computation: multiple imputation of
the underlying data, creation of an ensemble of machine learning models on
the imputed datasets, predictions from that ensemble, and replications of
previous studies using those predictions.


Our replication code sets seeds in any script where random numbers are
drawn, and runs in a Docker environment to ensure identical package versions
across machines. Nevertheless, because of differences in machine precision
and floating point computations across CPUs, the replication code may not
produce results identical to those in the paper. Any differences should be
small in magnitude and should not affect any substantive conclusions of the
analysis.
 
Subject Social Sciences
International relations
Machine learning
Measurement
 
Contributor Kenkel, Brenton
 
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