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This article conducts a meta-analysis of the effect of military expenditures on growth within a structured analytic framework. We extend the pioneering study of Aynur Alptekin and Paul Levine, by using a much larger sample of studies. Like them we confine our attention to studies that use the share of military expenditure in GDP, the military burden, as the independent variable, but unlike them we include not just those that use the military burden directly, what we call the core sample, but also those that use other functions of it, such as logarithms, differences, etc., which we call the remaining sample. We also consider an overall sample which pools all results. The t-statistic on the coefficient of military burden is used as the dependent variable. Our null hypothesis is that military expenditure has no significant effect on growth and we explain why this is plausible. The estimates are sensitive to the sample and type of data used, estimation method adopted, and the controls included. Overall, the results are consistent with the hypothesis of no effect: the average effect across all studies is close to zero. Certain study characteristics appear significant determinants of the effect of military expenditure on growth, but there does not appear to be a simple pattern and different characteristics were significant in the three samples. This might be a result of data mining to produce a significant result. However, there does not appear to be strong evidence of publication bias towards positive or negative results, perhaps because there is no strong a priori belief in the direction of the effect.

Using the latest spatial econometric techniques and data pertaining to 144 countries over the period 1993–2007, 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.

The Solow residual has presented an opportunity to researchers who have been attempting to explain the unexplained share of output. In pursuing this goal, the literature has relied on different models, estimators, and data sets. One such application is spatial models to estimate growth, but it remains rare in the literature. Such models allow us to determine whether the interaction among countries is significant. Additionally, it is possible to observe efforts to mimic different variables among countries thanks to indirect (spillover) effects. Therefore, using spatial models and data sets on founding OECD countries for the period 1996–2019, this article tests alternative weight matrices to clarify the mutual relationship among countries. The findings reveal that spatial models contribute to estimations by improving parametric results. Empirical evidence found that there are spatial interactions among countries. The spillover effect of technology growth is insignificant, while the direct effect is significantly positive. Investment growth is significantly positive except spillover effect. Human capital growth is significantly positive in any sense.

Even though there are a range of useful sources on defence economics data, only some of them are used widely. The main sources of export, import and output data sets are Stockholm International Peace Research Institute (SIPRI), World Military Expenditures and Arms Transfers-US Department of State (WMEAT), European Statistical Office (EUROSTAT) and United Nations Industrial Development Organization (UNIDO) while SIPRI and WMEAT are for military expenditures. This paper discusses the main features and similarities of data sets from the various sources. Like other industrial/sector data sets, the main problem for arms data sets is revisions of industrial classifications, which prevent the creation of long time series.

Using the latest spatial econometric techniques and data pertaining to 144 countries over the period 1993–2007, 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.

The structure of the data set has a great impact on the estimation results. Especially the methods, which are affected by outliers like Ordinary Least Squares (OLS), will lead to biased results. For this reason robust estimation techniques are required. To investigate this structure, 237 stocks in Borsa Istanbul (BIST) is estimated using OLS and Least Median Squares (LMS) method between the years of 2001-2004. Beta coefficients are computed based on OLS and LMS methods using market model. It was found that LMS produce robust results in the presence of multivariate outliers. Especially, in case of the volatile stocks, LMS is one of the appropriate techniques to get robust results.

Bu çalışmanın temel amacı illerin eğitim düzeyini açık ve geniş bir şekilde ölçmek ve analiz etmektedir. Bu yapı çerçevesinde hem il hem de cinsiyete göre zaman içerisinde Ortalama Okullaşma Yılı (OOY) ve Beklenen Okullaşma Yılı (BOY) hesaplanmıştır. Üstelik bu değişkenlerin zaman boyutundaki (2010-2015) gelişimi de belirlenmiş ve çıkarımlar yapılmıştır. Bu değişkenler bu yapıda ilk defa hesaplandığı için araştırmacı, ilgili yöneticiler ve politika yapıcılar için yararlı olacağı düşünülmektedir. Bunun en önemli nedeni, bu değişkenlerin eğitim yatırımlarının cinsiyete dayalı katkısının sonuçlarını ve nedenlerini belirlemede önemli bir gösterge oluşturmasıdır.