Alliances as Contiguity in Spatial Models of Military Expenditures

Flores, Alejandro Quiroz

doi:10.1177/0738894211413064

Cited by 30 publications

(21 citation statements)

References 59 publications

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“…With our third robustness check, we note that such differences in emphasis also occur when researchers carry out separate analyses for countries located in Europe, Asia, America, and Asia (Gleditsch, 2002;Goldsmith, 2006Goldsmith, , 2007Quiroz Flores, 2011), in these cases based on the COW dataset. 8 Since the choice of model and of W need to be made simultaneously, we repeated the Bayesian comparison approach and selected the most likely spatial econometric model and the most likely spatial weight matrix for each continent.…”

Section: The Dominance Of the United Kingdom Andmentioning

confidence: 99%

“…First, an endogenous spatial lag measures whether the defense burden of country i depends on the defense burden of other countries j (j 6 ¼ i), or vice versa, resulting in the spatial autoregressive (SAR) model that appears in several studies. For example, Quiroz Flores (2011) uses crosssection data from the World Bank (WB) to explain the defense burden indicator across 168 countries in the year 2000. Goldsmith (2007) also uses cross-section data, in this case from 129 countries in 1991 extracted from the Correlates of War (COW) project, but also anticipates that the indicator depends on the value observed in the previous year, which helps control for habit persistence.…”

Section: Spatial Lags and Weightsmentioning

confidence: 99%

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Impacts of neighboring countries on military expenditures

Yeşilyurt

Elhorst

2017

Journal of Peace Research

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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.

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Section: The Dominance Of the United Kingdom Andmentioning

confidence: 99%

Section: Spatial Lags and Weightsmentioning

confidence: 99%

Impacts of neighboring countries on military expenditures

Yeşilyurt

Elhorst

2017

Journal of Peace Research

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“…Hence, we apply weighting matrices, where w ikt = 1 when the distance between capital cities of countries i and k falls below a certain cut-off level, and w ikt = 0 otherwise. Given our list of sample countries, we use 2000 and 4000 km as the cut-off distances following the lead of Flores (2011).…”

Section: Empirical Methodologymentioning

confidence: 99%

“…10. Flores (2011) also use 8000 km as cut-off distances for his global sample. However, since we use a much smaller set of sample countries for our analysis, employing such a cut-off point means a very sparse weight matrix leading to estimation problems.…”

Section: Notesmentioning

confidence: 99%

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

George

Hou

Sandler

2018

Defence and Peace Economics

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This article investigates the demand for military expenditure for a sample of key Asia-Pacific countries. Spatial panel demand estimates are presented for three joined spatial units using a fixed-coefficient spatial lag model based on a two-step efficient GMM estimator. Spatial autoregression estimates are next presented for 1991-2015, founded on alternative kinds of country connectivities, such as contiguity, inverse distance, discrete distance, and power-projection considerations. Finally, 11 select countries' demands for defense equations are estimated using seemingly unrelated regressions. From alternative perspectives, these estimated models indicate how AsiaPacific countries respond to the defense spending of other countries. In the spatial runs, free riding is prevalent despite the growing military might of China, which apparently is not generally viewed as a threat. For the sample period, the projection of Chinese or American power is a relevant spatial factor. The main threat is reflected in non-U.S. allies' reaction to U.S. allies' defense spending during 1991-2015 and to Chinese defense spending after 2002.

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“…The second-order binary contiguity matrix, denoted by : the influence of a country might go beyond its immediate neighbours, as implied by the inverse distance matrix (Goldsmith, 2007) and the different cut-off points (Florez, 2011).…”

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confidence: 99%

Entrepreneurship and Income Inequality: A Dynamic Spatial Panel Data Analysis

Ragoubi

Harbi

2019

Tékhne

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This paper extends the empirical debate of Ragoubi and El Harbi (2018) on the dynamic relationship between entrepreneurship and income inequality. Using a dynamic spatial panel data analysis for both 33 high-income countries and 39 middle-income and low-income countries over the period 2004–2014, the main empirical findings are summarised as follows. First, the results indicate that entrepreneurship is a spatial and persistent phenomenon. Second, there is strong support for the existence of an inverted U-shaped relationship between entrepreneurship and income inequality espoused by the Kuznets Curve hypothesis for middle-income and low-income countries. Third, the interaction between income inequality and income per capita has a significant negative effect on the entrepreneurial activity for middle-income and low-income countries. Fourth, a significant positive association is found between the interaction variable and entrepreneurship for high-income countries. Fifth, the findings show evidence of significant positive and negative short-run direct effects of income inequality on the entrepreneurial activity for middle-income and low-income countries. Finally, there are significant negative short-run spillover effects of income inequality on the entrepreneurial activity for middle-income and low-income countries.

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Alliances as Contiguity in Spatial Models of Military Expenditures

Cited by 30 publications

References 59 publications

Impacts of neighboring countries on military expenditures

Impacts of neighboring countries on military expenditures

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

Entrepreneurship and Income Inequality: A Dynamic Spatial Panel Data Analysis

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