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Empirical Economics

Erschienen in:

21.12.2017

Military spending, economic growth and investment: a disaggregated analysis by income group

verfasst von: Christos Kollias, Suzanna-Maria Paleologou

Erschienen in: Empirical Economics | Ausgabe 3/2019

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Abstract

Employing a panel vector autoregression (PVAR) methodology, the paper addresses the nexus between military expenditures and two key macroeconomic variables, namely growth rates and investment spending using SIPRI’s new consistent time series dataset. Given data constraints, the sample chosen was a compromise between T and N for the empirical tests conducted. It consists of 65 countries and covers the period 1971–2014 that allows for a total of 2730 observations. The PVAR model is estimated for the entire sample as well as three income group sub-samples. Findings reported herein are not uniformed, and noteworthy differences between the three income groups were unearthed by the empirical tests conducted.

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Data on economic growth rates and gross capital formation as a share of GDP are drawn from the World Bank’s World Development Indicators database.

The countries included in each group and panel estimation are shown in Appendix. The World Bank groups countries into four categories: high income, Upper middle income, Lower middle income and low income. Due to data availability constraints, it was decided to join the two middle-income groups into a single middle-income category; otherwise, the two original middle-income groups would have contained a small number of countries each with the concomitant potential effects on the empirical estimations.

The picture is slightly different when it comes to the absolute level of military spending globally. A broadly upward trend in the Cold War, a sharp decline during the first post-bipolarity decade, a sharp reversal into an upward trend following 9/11 that was halted following the recent economic crisis [for a detailed discussion see Sandler and George (2016)].

The first paper that used panel VAR in Stata was Love and Zicchino (2006), who also made the programs available informally to other researchers. We use the STATA pvar programs by Abrigo and Love (2015) which give an updated package of programs with additional functionality, including sub-routines to implement Granger (1969) causality tests, and optimal moment and model selection following Andrews and Lu (2001).

See Hamilton (1994) for a discussion on IRFs and derivations.

The coefficients are estimated by the GMM, which, in our case, is “just identified”, i.e., the number of regressors equals the number of instruments. Hence, it is equivalent to 2SLS.

For a survey on homogeneity in panel analysis see Eberhardt and Teal (2011).

For reasons of brevity, results are not reported here but are available upon request.

The IRFs are computed using the estimated Panel VAR coefficients. The standard errors of these coefficients need to be taken into account. This is done with Monte Carlo simulation, in which the parameters of the model are re-calculated 200 times using the estimated coefficients and their variance-covariance matrices as underlying distribution. The 5th and 95th percentiles from the resulting distribution are then used to generate the lower and upper bounds of the impulse response functions.

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Titel: Military spending, economic growth and investment: a disaggregated analysis by income group
verfasst von: Christos Kollias
Suzanna-Maria Paleologou
Publikationsdatum: 21.12.2017
Verlag: Springer Berlin Heidelberg
Erschienen in: Empirical Economics / Ausgabe 3/2019
Print ISSN: 0377-7332
Elektronische ISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-017-1379-2

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