Religion, ethnicity, and politics are typical explanatory variables of violent conflicts. From an economic point of view, economic growth reduces the risk of civil war, yet the economic determinants of conflict have been little studied. In this article, we empirically study the impact of regional macroeconomic conditions on the number of violent conflicts in Indonesia, a country with potential risks of communal conflict because of the plurality of its society. We use panel data consisting of observations on 16 Indonesian regions from 2004 to 2013 to assess the impact of economic factors on conflict, reevaluating the religion effect using dynamic models (SYS GMM estimator). Our findings suggest that only the inflation rate predicts the conflict growth rate. Economic growth, economic development, poverty, and even religion, do not significantly affect the number of regional conflicts.
The article focuses on the ethnic and confessional diversity of Indonesia, as well as mechanisms of supporting it in the framework of the country’s rapid economic development and active involvement into globalization processes.
In recent years, relations between Russia and the Association of Southeast Asian Nations (ASEAN) have seen positive trends, but no qualitative shift to a new level of cooperation. Nevertheless, Moscow’s increasing economic and diplomatic reorientation towards Asia, coupled with a confluence between their priorities in regional politics and security, have the potential to make Russia–ASEAN cooperation more versatile and multidimensional.
We address the external effects on public sector efficiency measures acquired using Data Envelopment Analysis. We use the health care system in Russian regions in 2011 to evaluate modern approaches to accounting for external effects. We propose a promising method of correcting DEA efficiency measures. Despite the multiple advantages DEA offers, the usage of this approach carries with it a number of methodological difficulties. Accounting for multiple factors of efficiency calls for more complex methods, among which the most promising are DMU clustering and calculating local production possibility frontiers. Using regression models for estimate correction requires further study due to possible systematic errors during estimation. A mixture of data correction and DMU clustering together with multi-stage DEA seems most promising at the moment. Analyzing several stages of transforming society’s resources into social welfare will allow for picking out the weak points in a state agency’s work.