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Nexus between defence spending, economic growth and development: evidence from a disaggregated panel data analysis

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Abstract

This study investigates the causal effect relationship between defence spending, economic growth and development in three regions for the period 1990–2018. It further estimates the trivariate impacts of defence spending, growth and development in three regions. To measure growth, we used real gross domestic product as the proxy, while economic development was measured by the Human Development Index. The empirical results suggest the existence of a bidirectional long-run relationship between defence spending, economic growth and development in the entire sample and the three regions of sub-Saharan Africa, the Middle East and North Africa, and Latin America and the Caribbean countries. The causality results reveal that there is feedback causality between defence spending, economic growth and development. There is thus a need to promote holistic policies to reduce the defence spending necessary for the enhancement of inclusive economic growth and development in these three regions. A decrease in defence spending in the three regions will encourage aggregate output and the standard of living to increase, and move simultaneously in the same direction.

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Fig. 1
Fig. 2
Fig. 3

Note: We computed the IRFs using the PVAR coefficients. In order to take into account the standard errors of these coefficients we used the 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‰ from the resulting distribution were then used to generate the lower and upper bounds of the impulse response functions

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Notes

  1. Several earlier studies have noted controversies surrounding the nature of the relationship between defence spending and economic growth. Some of these studies include Al-Yousif (2002), Abu-Bader and Abu-Qarn (2003), Yildirim et al. (2005), Ahmed (2012), Pan et al. (2015), Saba and Ngepah (2018, 2019a, b), among others. However, most of the studies applied different econometric techniques in investigating the causality between defence spending and economic growth.

  2. Previous studies failed to measure economic development.

  3. Stockholm International Peace Research Institute.

  4. We measured economic development by using the human development index as it comprises key dimensions of social and economic dimensions of countries (Haq 1995; Sen 1999; United Nations Development Programme 2010).

  5. These regions include Sub-Saharan Africa (SSA), Middle East and North Africa (MENA) and the Latin America & Caribbean (LAC) countries.

  6. The countries were further disaggregated into three regions.

  7. The results of the study show that military spending positively affects economic growth when the level of military spending is low, while the effect turns to negative for high levels of military spending.

  8. Defence spending has positive and significant effects for 39 countries, while being negative and significant for eight countries, and insignificant on economic growth for the remaining 38 countries.

  9. No significant impact of DEF on growth, as a result of income distribution (i.e., income inequality).

  10. The study finds a nonlinear relationship between military spending and economic growth in Turkey and the Middle Eastern countries.

  11. Interested readers are referred to Levin et al. (2002), Im et al. (2003), Breitung (2001), and Breitung and Das (2005) methodological framework for more details.

  12. The details of this methodology have been extensively discussed in the literature, and warrant detailed discussion here. However, for details and steps involved in the use of this methodology, consult Pesaran (2007).

  13. Interested readers are referred to Westerlund (2007) methodological framework for more details.

  14. The group-mean panel dynamic OLS (DOLS) estimator uses the lead and lagged differences of the regressors to correct for endogeneity, while the group-mean FMOLS estimator applies a semi-parametric correction to the OLS estimator to eliminate serial correlation and endogeneity.

  15. Interested readers are referred to the Dumitrescu and Hurlin (2012) methodological framework for more details.

  16. This study used the latest STATA pvar programs made available to other researchers by Abrigo and Love (2015). It was first used by Love and Zicchino (2006) and later updated with additional functionality, including optimal moment and model selection, following Andrews and Lu (2001) etc.

  17. Interested readers are referred to Abrigo and Love (2015) for more details. We refer readers to this paper to save space.

  18. See, for instance, Lutkepohl (2005) for details applied in time-series VAR.

  19. We test the null hypothesis (H0) of non-stationarity of all series against the alternative hypothesis (H1), which supports their stationarity. To accept/reject the null hypothesis for the entire sample and the three regions, we compare the p value to a threshold level of 1%. We may accept H0 if the p value is greater than 1% and reject it if the p value is less than 1%. The results reported in Table 3 indicate that all the variables have a unit root problem at the level. However, at first difference, we reject the null hypothesis and confirm the stationarity of all the variables. This shows that all the variables have a unique order of integration, i.e., I(1).

  20. We note that the Pesaran (2007) panel unit root test assumes the existence of one common factor that affects all countries in the same way. This restrictive assumption should be considered when interpreting the results.

  21. We find that the null hypothesis of no cointegration is rejected at 1% and 5% levels, respectively, which confirms the existence of cointegration between the variables for entire sample and the three regions. This further confirms that empirical results are robust and reliable.

  22. The results of the model and moment selection criteria and the Hansen test statistics are not reported to save space, but are available upon request.

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Table 12 Optimum lag length selection results
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Saba, C.S., Ngepah, N. Nexus between defence spending, economic growth and development: evidence from a disaggregated panel data analysis. Econ Change Restruct 55, 109–151 (2022). https://doi.org/10.1007/s10644-020-09311-7

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