Impacts of neighboring countries on military expenditures

Abstract

Using the latest spatial econometric techniques and data pertaining to 144 countries over the period [1993][1994][1995][1996][1997][1998][1999][2000][2001][2002][2003][2004][2005][2006][2007], this article tests and compares four frequently used spatial econometric models and eight matrices describing the mutual relationships among the countries, all within a common framework, which helps clarify the impact of neighboring countries on military expenditures. Furthermore, it utilizes two different data sources. Due to this setup, it provides one of the most thorough spatial analyses of military expenditures so far. Furthermore, it confirms but also challenges the results of several previous studies. Military spending measured as a ratio of GDP in one country indeed depends primarily on the spending of other countries, but in a limited number of cases, it also depends on control variables that can be observed in other countries, among which are the level of GDP, the occurrence of international wars, and the political regime. The most likely specification of the matrix describing the relationships among countries is the first-order binary contiguity matrix based on land or maritime borders, extended to include two-sided relationships among the five countries that are permanent members of the UN Security Council and one-sided relationships to all other countries. Finally, cross-sectional approaches are rejected in favor of dynamic spatial panel data approaches due to their controls for habit persistence, country, and time-period fixed effects.

Asia-Pacific Demand for Military Expenditure: Spatial Panel and SUR Estimates

“…The last two contiguity spatial weighted spillover expressions apply a weight of 1 to the ME of the United States and China, respectively, along with a weight of 1 applied to the ME of contiguous neighbors. As such, contiguity and power projection determine spillovers for our last two spatial spillover measures, consistent with George and Sandler (forthcoming), Skogstad (2016), and Yesilyurt and Elhorst (2017).…”

“…Some recent spatial panel estimates of defense demand take an inductive approach and use military burdens as the dependent variable. These studies compute spillovers in terms of a weighted sum of other countries' military defense burdens (Goldsmith 2007;Skogstad 2016;Yesilyurt and Elhorst 2017). This practice troubles us on three grounds.…”

“…There are weaker U.S. alliance linkages to Pakistan, the Philippines, and New Zealand (Correlates of War (COW) Project 2013). Spatial connectivity may stem from spatial propinquity (contiguity or nearness in distance) or power projection by dominant countries, such as China and the United States (Skogstad 2016;Yesilyurt and Elhorst 2017;George and Sandler forthcoming). 6 We assume an N-country scenario in which a unitary actor (e.g.…”

Spatial effects on local government efficiency

“…The Bayesian approach is used to select spatial models and spatial weight matrices as done by other scholars (Costa da Silva, Elhorst, & da Mota Silveira Neto, 2017;Rios et al, 2017;Yesilyurt & Elhorst, 2017). Table 2 displays the values of the Bayesian posterior-model probabilities computed across different definitions of W for each spatial panel data model.…”

“…Table 2 displays the values of the Bayesian posterior-model probabilities computed across different definitions of W for each spatial panel data model. 22 On comparing the probabilities of all 72 combinations, 23 the DSAR model with W d < 20km shows 22 The Matlab code developed by Yesilyurt and Elhorst (2017) is rearranged for this purpose. Their code is available at https://spatial-panels.com/software/.…”