Scott Abramson, Brandon Cooley and Bethany Lacina
Chair: Justin Esarey (Wake Forest University)
Co-Host: Md Mujahedul Islam (University of Toronto)
How Wide is the Ethnic Border?
Author(s): Scott Abramson, Brandon Cooley and Bethany Lacina
Discussant: Florian Hollenbach (Texas A&M University)
We explore the relationship between ethnic heterogeneity and within- and cross-country barriers to trade. We develop a spatial model of trade in which observable productivity shocks directly affect local prices. These local shocks propagate through the trading network differentially, depending on unobserved trading frictions. Coupling data describing monthly commodity prices in 227 cities across 42 African counties, remotely sensed weather data, and spatial data describing the locations of ethnic-group homelands, we estimate this model to quantify the costs traders incur when by crossing ethnic and national borders. We show that ethnic borders induce a friction approximately half the magnitude of national borders, indicating that ethnic heterogeneity is an impediment to the development of efficient national markets. Through counterfactual experiments, we quantify the effect of these frictions on consumer welfare and the extent to which colonial-era political borders have hindered African economic integration. In all, our paper suggests that trade impediments caused by ethnic heterogeneity are a substantial channel through which ethnic fractionization impacts development.
The Dynamics of Civil Wars: A Bayesian hidden Markov model applied to the pattern of conflict and the role of ceasefires (Cancelled)
Author(s): Jonathan P. Williams, Gudmund H. Hermansen, Håvard Mokleiv Nygård, Govinda Clayton and Siri Aas Rustad
Discussant: Bruce Desmarais (Penn State University)
Why (and when) do small conflicts become big wars? We develop a Bayesian hidden Markov modeling (HMM) framework for the studying the dynamics of violence in civil wars. The key feature of an HMM for studying such a process is that an it is defined on top of a latent state space constructed to represent the domain scientist intuition for the processes being studied. To learn a latent state space of varying intensity of conflict we use count data of weekly conflict related deaths over time in a nation as an emitted response variable, and construct an autoregressive model of order 1 to describe its evolution. Using event-level data for all civil wars from 1989 to the present, this framework allows us to study transitions in the latent intensity, e.g. from escalating conflicts to stable and/or deescalating conflicts. In particular, we examine the effect of declaring a ceasefire on the underlying dynamics of conflict. Accounting for the effects of covariates for the relative degree of democracy, GDP per capita, and population in a country, we are able to quantify the uncertainty for the underlying intensity of a conflict at any given point in time.
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