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Defence Spending and Economic Growth in South Africa: Evidence from Cointegration and Co-Feature Analysis

  • Charles Shaaba Saba ORCID logo EMAIL logo
Veröffentlicht/Copyright: 3. September 2021
Peace Economics, Peace Science and Public Policy
Aus der Zeitschrift Peace Economics, Peace Science and Public Policy Band 28 Heft 1

Abstract

The paper revisits the causality relationship between defence spending and economic growth for South Africa during the period 1960–2018. The results of our estimation show that defence spending and economic growth are cointegrated and that there is bidirectional Granger causality running between defence spending and economic growth in the long run. We then applied a Hodrick-Prescott filter to decompose the trend and the fluctuation components of the defence spending and economic growth series. The findings from the autoregressive distributed lag bounds test estimations show that in the long- and short-run, the trends and cyclicality of defence spending retard economic growth. The estimation results show that there is cointegration between the trends and the cyclical components of the two series, which suggests that the Granger causality possibly relates to the business cycle. This study suggests that investing more and reducing inefficiency spending in the defence sector during fluctuations can further stimulate economic growth in South Africa.

Keywords: defence spending; economic growth; Granger causality; Hodrick-Prescott (HP) filter; South Africa
JEL Classification: C32; H56; O47
Corresponding author: Charles Shaaba Saba, School of Economics, College of Business and Economics, University of Johannesburg, Auckland Park Kingsway Campus, PO Box 524 Auckland Park, Johannesburg, South Africa, E-mail:

Acknowledgements

We appreciate the editor(s), the editorial team members and the anonymous expert referees for their insightful comments and suggestions that helped improve the quality of this paper.

  1. Disclosure statement: No potential conflict of interest was reported by the author(s).

Appendix

Table A:

Brief review of the empirical literature on the defence spending-economic growth nexus.

Author(s) Sample/period Methodology Main finding(s)
Country-specific studies
Sezgin (2001) Turkey (1956–1994) 2SLS and 3SLS simultaneous equation method

  1. The study concludes that Turkey’s EG is stimulated by its defence sector while DEFS has no significant effect on savings and the balance of trade.
Chang et al. (2014) China and G7 countries (1988–2010) Bootstrap panel causality technique

  1. Causality from real GDP to DEFS is found for China

  2. The results find evidence of the neutrality hypothesis for Italy, France, and Germany, the military spending–growth detriment hypothesis for both Canada and the UK.

  3. A feedback causality between military spending and EG in both Japan and the USA.
Lai, Huang, and Yang (2005) Taiwan (1953–2000) Multivariate threshold regression

  1. Arms race is found between Taiwan and China.

  2. China’s DEFS changes lead EG in only one regime (when Taiwan’s spending growth is less than 5%).
Meng, Lucyshyn, and Li (2015) China (1989–2012)

  1. Engle and Granger (1987) two step cointegration

  2. Granger causality tests

  1. DEFS and income inequality are cointegrated

  2. Causality from DEFS changes to those of income inequality is found in the short run.
Dimitraki and Menla Ali (2015) China (1952–2010)

  1. Bartlett corrected trace test for cointegration

  2. Long-run weak exogeneity tests

  1. Cointegration is found between DEFS and real GDP along with some control variables.

  2. It is the economic development that drives increases in DEFS.
Gupta, Kabundi, and Ziramba (2010) USA (1976–2005) Factor augmented vector autoregressive (FAVAR) model

  1. Overall, the results of the study show that a positive shock to the growth rate of the real DEFS translates to a positive short-run effect on the growth rate of real GNP lasting up to 10 quarters, but the effect is significant only for two quarters.

  2. Beyond the 10th quarter, the effect becomes negative and shows signs of slow reversal at around the 17th quarter.
Feridun, Sawhney, and Shahbaz (2011) North Cyprus (1977–2007) ARDL bounds testing approach to cointegration and Granger causality tests

  1. The results suggest a long-run equilibrium relationship and that there exists a strong, positive unidirectional causality running from DEFS to EG.
Klein (2004) Peru (1970–1996) 3SLS, 2SLS and OLS

  1. Overall, DEFS has a negative effect on EG.
Shahbaz, Afza, and Shabbir (2013) for Pakistan (1972–2009) ARDL bounds testing approach

  1. The results suggest a stable cointegration relationship between DEFS and EG.

  2. An increase in DEFS reduces the pace of EG confirming the validity of Keynesian hypothesis.

  3. Unidirectional causality running from DEFS to EG.
Zhao, Zhao, and Chen (2017) China (1952–2012)

  1. Granger causality tests

  2. Generalised impulse response functions based on vector error correction models.

  1. Two long-run equilibrium relationships among the variables also show that DEFS inversely and unidirectionally Granger impacts EG.

  2. A trade-off relationship between DEFS and public expenditures in China.
Su et al. (2020) China (1952–2014) Rolling Granger causality test

  1. Positive bidirectional causal relationships between DEFS and EG.
Dimitraki and Win (2020). Jordan (1970–2015)

  1. Gregory-Hansen cointegration technique

  2. ARDL bound test

  1. The results reveal positive short- and long-run relationships between DEFS and EG.
Gyimah-Brempong (1989) Thirty nine Sub-Saharan African countries (1973–1983) Three stage least squares

  1. Positive relationship between DEFS and EG.
Yildirim, Sezgin, and Öcal (2005) Middle Eastern countries and Turkey (1989–1999) The fixed effects panel analysis and the GMM method.

  1. Empirical analysis indicates that DEFS enhances EG in the Middle Eastern countries and Turkey as a whole.
Global/regional studies
Stroup and Heckelman (2001) Fourty four countries in Africa and Latin America (1975–1989) Fixed-effects approach

  1. The findings show that the effect of DEFS on EG is non-linear, with low levels of DEFS increasing EG but higher levels of DEFS decreasing EG.

  2. The study also finds that the influence of military labour use on growth is non-linear, and exhibits a greater drag on EG in those countries with relatively higher levels of adult male education attainment.
d’Agostino, Dunne, and Pieroni (2019) One hundred and nine non-high-income countries (1998–2012) Structural panel IV model

  1. Negative effect of military spending on growth
Desli and Gkoulgkoutsika (2020) World 1960–2017 Dynamic common correlated effects estimator

  1. Overall, the effect of DEFS on EG appears to be negative; this originates from the cold war and early post-cold war era.

  2. For the post-cold war era, a neutral effect (i.e. no statistical significance) is apparent for the majority of the countries.
Saba and Ngepah (2020a). SSA, MENA, and LAC countries (1990–2018)

  1. Panel Vector Autoregression (PVAR)

  2. Dumitrescu and Hurlin (2012) causality test

  1. The causality results reveal that there is feedback causality between defence spending, economic growth and development.

  2. Mixed impact of DEFS on EG in the three regions.
Töngür and Elveren (2016) Turkey (1963–2008) Augmented Solow growth model

  1. DEFS have no significant effect on EG
Yolcu Karadam, Yildirim, and Öcal (2017) Middle Eastern countries and Turkey (1988–2012) Panel smooth transition regression (PSTR) model

  1. The results indicate that although the effect of DEFS on EG is positive for low values of transition variables, negative effects are observed for high values of them.
Cevik and Ricco (2018) Advanced and developing countries (1984–2014) GMM approach

  1. Overall, empirical results documented in the study suggest that DEFS have no significant effect on EG.

  1. DEFS and EG denote defence spending and economic growth, respectively. SSA, MENA and LAC represent Sub-Saharan Africa, the Middle East and North Africa, and Latin America and the Caribbean, respectively.

Table B:

Descriptive statistics results.

Statistics LDEFS LRGDP TLRGDP TLDEFS CLRGDP CLDEFS
Mean −3.845 26.087 26.087 −3.845 −2.44E-13 2.96E-14
Median −3.741 26.110 26.100 −3.779 −0.002 0.006
Maximum −2.944 26.786 26.806 −3.220 0.048 0.324
Minimum −4.655 25.094 25.095 −4.587 −0.037 −0.409
Std. dev. 0.513 0.459 0.458 0.484 0.019 0.135
Skewness −0.016 −0.301 −0.295 −0.107 0.305 −0.142
Kurtosis 1.604 2.360 2.335 1.488 2.840 3.593
Jarque-Bera 4.795 1.897 1.945 5.730 0.978 1.061
Probability 0.091 0.387 0.378 0.057 0.613 0.588
Observations 59 59 59 59 59 59

  1. Source: Author’s computations using data from SIPRI and WDI.

Table 15:

Chow test results for the trend and cyclical components.

Panel (A): trend component
Chow breakpoint test: 1983
Equation sample: 1960–2018
F-statistic 615.0929*** Prob. F(2,55) 0.0000
Log likelihood ratio 185.9282*** Prob. chi-square (2) 0.0000
Wald statistic 1230.186*** Prob. chi-square (2) 0.0000
Panel (B): cyclical component
Chow breakpoint test: 2007
Equation sample: 1960–2018
F-statistic 1.9196*** Prob. F(1,57) 0.0013
Log likelihood ratio 1.9542*** Prob. chi-square (1) 0.0021
Wald statistic 1.9196*** Prob. chi-square (1) 0.0059

  1. *, **, and ***denote significance levels at 1, 5 and 10%, respectively. Source: Author’s computations using data from SIPRI and WDI.

Table 16:

Optimum lag length selection results for the unfiltered data.

Variables: Defence spending and growth
Unfiltered data structural breaks uncontrolled
Lag AIC SIC HQIC
0 1.7479 1.8208 1.7761
1 −6.7674 −6.5484* −6.6827
2 −6.8996 −6.5347 −6.7585*
3 −6.9075* −6.3965 −6.7099
4 −6.8769 −6.2199 −6.6229
Unfiltered data controlling for structural breaks
0 1.4489 1.5584 1.4913
1 −7.9750 −7.5371* −7.8057*
2 −7.9820 −7.2156 −7.6856
3 −7.9839* −6.8890 −7.5605
4 −7.8083 −6.3849 −7.2579

  1. *indicates lag order selected by the criterion. AIC is Akaike information criterion; SIC is Schwarz information criterion; HQIC is Hannan-Quinn information criterion. Source: Author’s computations using data from SIPRI and WDI.

Table 17:

Optimum lag length selection results for the filtered data.

Panel A: trend component
Variables: defence spending and real GDP
Filtered data structural breaks uncontrolled (trend)
Lag AIC SIC HQIC
0 1.6389 1.7119 1.6672
1 −11.9178 −11.6988 −11.8331
2 −18.7516 −18.3866 −18.6104
3 −23.7154 −23.2044 −23.5178
4 −26.8364* −26.1795* −26.5824*
Filtered data controlling for structural breaks (trend)
0 2.0389 2.1485 2.0813
1 −13.7663 −13.3283 −13.5969
2 −19.8453 −19.0789 −19.5489
3 −25.5521 −24.4572 −25.1287
4 −28.1727* −26.7493* −27.6223*
Panel (B): cyclical component
Filtered data structural breaks uncontrolled (cyclical)
0 −6.4213 −6.3483 −6.3930
1 −7.4767 −7.2578 −7.3921
2 −7.7834 −7.4184 −7.6422
3 −7.9754* −7.4644* −7.7778*
4 −7.9177 −7.2608 −7.6637
Filtered data controlling for structural breaks (cyclical)
0 −4.9577 −4.8482 −4.9153
1 −8.6321 −8.1941* −8.4627
2 −8.8125 −8.0461 −8.5161*
3 −8.8488* −7.7539 −8.4254
4 −8.7466 −7.3232 −8.1962

  1. *indicates lag order selected by the criterion. AIC is Akaike information criterion; SIC is Schwarz information criterion; HQIC is Hannan-Quinn information criterion. Source: Author’s computations using data from SIPRI and WDI.

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Received: 2021-04-21
Accepted: 2021-08-19
Published Online: 2021-09-03

© 2021 Walter de Gruyter GmbH, Berlin/Boston

Artikel in diesem Heft

  1. Frontmatter
  2. Editorial
  3. Fifth Walter Isard Annual Award for the Best Article in Peace Economics Peace Science and Public Policy
  4. Letters and Proceedings
  5. The Effect of the Spanish Civil War on City Shares
  6. Research Articles
  7. How Much are Iranian Men Willing to Pay for Exemption from Military Service?
  8. Spillovers Between Russia’s and Turkey’s Geopolitical Risk During the 2000–2021 Putin Administration
  9. Defence Spending and Economic Growth in South Africa: Evidence from Cointegration and Co-Feature Analysis
https://doi.org/10.1515/peps-2021-0017
Schlagwörter für diesen Artikel
defence spending; economic growth; Granger causality; Hodrick-Prescott (HP) filter; South Africa

Artikel in diesem Heft

  1. Frontmatter
  2. Editorial
  3. Fifth Walter Isard Annual Award for the Best Article in Peace Economics Peace Science and Public Policy
  4. Letters and Proceedings
  5. The Effect of the Spanish Civil War on City Shares
  6. Research Articles
  7. How Much are Iranian Men Willing to Pay for Exemption from Military Service?
  8. Spillovers Between Russia’s and Turkey’s Geopolitical Risk During the 2000–2021 Putin Administration
  9. Defence Spending and Economic Growth in South Africa: Evidence from Cointegration and Co-Feature Analysis
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