@article{WucherpfennigHunzikerCederman, author = {Wucherpfennig, Julian and Hunziker, Philipp and Cederman, Lars-Erik}, title = {Who Inherits the State? Colonial Rule and Post-Colonial Conflict}, series = {American Journal of Political Science}, volume = {60}, journal = {American Journal of Political Science}, number = {4}, issn = {0092-5853}, doi = {10.1111/ajps.12236}, pages = {882 -- 898}, abstract = {Postulating grievance-based mechanisms, several recent studies show that politically excluded ethnic groups are more likely to experience civil conflict. However, critics argue that endogeneity may undermine this finding since governments' decisions to include or exclude could be motivated by the anticipation of conflict. We counter this threat to inference by articulating a causal pathway that explains ethnic groups' access to power independently of conflict. Focusing on postcolonial states, we exploit differences in colonial empires' strategies of rule to model which ethnic groups were represented in government at the time of independence. This identification strategy allows estimating the exogenous effect of inclusiveness on conflict. We find that previous studies have tended to understate the conflict-dampening impact of political inclusion. This finding suggests that grievances have been prematurely dismissed from conventional explanations of conflict, and that policy makers should consider conflict resolution methods based on power sharing and group rights.}, language = {en} } @article{WucherpfennigKachiBormannetal., author = {Wucherpfennig, Julian and Kachi, Aya and Bormann, Nils-Christian and Hunziker, Philipp}, title = {A Fast Estimator for Binary Choice Models with Spatial, Temporal, and Spatio-Temporal Interdependence}, series = {Political Analysis}, volume = {29(4)}, journal = {Political Analysis}, doi = {10.1017/pan.2020.54}, pages = {570 -- 576}, abstract = {Binary outcome models are frequently used in the social sciences and economics. However, such models are difficult to estimate with interdependent data structures, including spatial, temporal, and spatio-temporal autocorrelation because jointly determined error terms in the reduced-form specification are generally analytically intractable. To deal with this problem, simulation-based approaches have been proposed. However, these approaches (i) are computationally intensive and impractical for sizable datasets commonly used in contemporary research, and (ii) rarely address temporal interdependence. As a way forward, we demonstrate how to reduce the computational burden significantly by (i) introducing analytically-tractable pseudo maximum likelihood estimators for latent binary choice models that exhibit interdependence across space and time and by (ii) proposing an implementation strategy that increases computational efficiency considerably. Monte Carlo experiments show that our estimators recover the parameter values as good as commonly used estimation alternatives and require only a fraction of the computational cost.}, language = {en} }