This page contains information about some of my working papers and my published research.
Those who would revolt against an autocrat often face a dilemma caused by uncertainty: they would like to revolt if the ruler would respond with democratization, but they would prefer to concede if the ruler would choose instead to violently suppress the revolution. Consequently, the autocrat must decide how to best signal his willingness to use violence in hope of deterring revolt. Using a simple signaling model, we find that rulers cannot meaningfully convey their type by transferring wealth to the citizenry. However, they can convey their type through shows of force, as long as the strong type of autocrat – who would use violent repression in the case of revolution – has a competitive advantage in displaying his strength. We additionally demonstrate that rulers favor shows of force when their willingness to suppress revolution is questioned and that citizens at times prefer to pay the direct cost of shows of force to learn about the ruler’s type, rather than to remain uninformed. The results illustrate a more general result in costly signaling models: information transmission is only possible when the cost of the signal is smaller for the type that wants to distinguish himself.
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 predicting militarized dispute outcomes. We use machine learning to build a superior proxy, the Dispute Outcome Expectations (DOE) 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.
Using Item Response Theory to Improve Measurement in Strategic Management Research: An Application to Corporate Social Responsibility (with Dave Primo and Brian Richter)
This article uses item response theory (IRT) to advance strategic management research, focusing on an application to corporate social responsibility (CSR). IRT explicitly models firms' and individuals' observable actions in order to measure unobserved, latent characteristics. IRT models have helped researchers improve measures in numerous disciplines. To demonstrate their potential in strategic management, we show how the method improves on a key measure of corporate social responsibility and corporate social performance (CSP), the KLD Index, by creating what we term D‐SOCIAL‐KLD scores, and associated estimates of their accuracy, from the underlying data. We show, for instance, that firms such as Apple may not be as “good” as previously thought, while firms such as Walmart may perform better than typically believed. We also show that the D‐SOCIAL‐KLD measure outperforms the KLD Index and factor analysis in predicting new CSR‐related activity.