A dynamic panel analysis using SIPRI’s extended military expenditure data: The case of Middle Power nations

Mohamed Douch, Binyam Solomon


This study employs SIPRI’s extended military expenditure dataset to estimate a dynamic panel analysis of Middle Powers’ defense posture. The dynamic approach, particularly the Auto Regressive Distributed Lag (ARDL) approach, permits simultaneous, but separate, assessment of short- and long-run effects of a particular variable on military expenditure. We verify the robustness of earlier findings on Middle Power nations’ defense posture. In particular, their military expenditure tends to an income elasticity of greater than one indicating that military power is, at least in part, a status good. In addition, Middle Powers react to threat variables that proxy global instability, such as nuclear power proliferation, and they use foreign aid as a complementary policy tool. Competing demands for funds lead to significant tradeoffs between military and nonmilitary government spending.


Threat; nuclear arsenal; demand for military expenditure; Middle Powers nations; ARDL panel data

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Arellano, M. and S. Bond. 1991. “Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations.” Review of Economic Studies. Vol. 58, No. 2, pp. 277-297. http://dx.doi.org/10.2307/2297968

Arellano, M. and O. Bover. 1995. “Another Look at the Instrumental Variable Estimation of Error-Components Models.” Journal of Econometrics. Vol. 68, No. 1, pp. 29-51. http://dx.doi.org/10.1016/0304-4076(94)01642-D

Bentzen, I. and T. Engsted. 2001. “A Revival of the Autoregressive Distributed Lag Model in Estimating Energy Demand Relationship.” Energy. Vol. 26, No. 1, pp. 45-55. http://dx.doi.org/10.1016/S0360-5442(00)00052-9

Breitung, J. and H. Pesaran. 2008. “Unit Roots and Cointegration in Panels,” pp. 279-322 in L. Matyas and P. Sevestre, eds. The Econometrics of Panel Data: Fundamentals and Recent Developments in Theory and Practice. 3rd ed. Dortrecht: Kluwer. http://dx.doi.org/10.1007/978-3-540-75892-1_9

Eberhardt, M. 2012. “Estimating Panel Time-Series Models with Heterogeneous Slopes.” Stata Journal. Vol. 12, No. 1, pp. 61-71.

Douch, M. and B. Solomon. 2014. “Middle Powers and the Demand for Military Expenditures.” Defence and Peace Economics. Vol. 25, No. 6, pp. 605-618. http://dx.doi.org/10.1080/10242694.2013.861652

Murdoch, J.C. and T. Sandler. 1982. “A Theoretical and Empirical Analysis of NATO.” Journal of Conflict Resolution. Vol. 26, No. 2, pp. 237-263. http://dx.doi.org/10.1177/0022002782026002003

Pesaran, H. 1997. “The Role of Econometric Theory in Modeling the Long Run.” Economic Journal. Vol. 107, No. 440, pp. 178–191. http://dx.doi.org/10.1111/1468-0297.00151

Pesaran, H. and Y. Shin. 1999. “An Autoregressive Distributed Lag Modeling Approach to Cointegration in Econometrics and Economic Theory in the 20th Century,” pp. 371-413 in S. Strom, ed. The Ragnar Frisch Centennial Symposium. New York: Cambridge University Press.

Pesaran, H., Y. Shin, and R. Smith. 2001. “Bounds Testing Approaches to the Analysis of Level Relationships.” Journal of Applied Econometrics. Vol. 16, No. 3, pp. 289-326. http://dx.doi.org/10.1002/jae.616

Samargandi, N., J. Fidrmuc, and S. Ghosh. 2015. “Is the Relationship between Financial Development and Economic Growth Monotonic? Evidence from a Sample of Middle-Income Countries.” World Development. Vol. 68, No. 1, pp. 66-81. http://dx.doi.org/10.1016/j.worlddev.2014.11.010

Smith, R. 1989. “Models of Military Expenditure.” Journal of Applied Econometrics. Vol. 4, No. 4, pp. 345-359. http://dx.doi.org/10.1002/jae.3950040404

Smith, R. 1995. “The Demand for Military Expenditure,” pp. 69-87 in K. Hartley and T. Sandler, eds. Handbook of Defense Economics. Vol. 1. Amsterdam: North Holland.

DOI: http://dx.doi.org/10.15355/epsj.11.2.45


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