The nexus between military spending and economic growth has been discussed in literature extensively. Now researchers have diverted their attention to examine the impact of military spending on external and internal debt. This is new exploration in literature to make contribution using cross-country or country case study. Our study is also an effort to contribute in literature by investigating the effect of military spending, economic growth and investment on external debt in case of Pakistan using time series data for the period of 1973-2009. In doing so, ARDL bounds testing approach to cointegration was used which confirmed the existence of cointegration between military spending, economic growth, investment and external debt.
The empirical analysis reveals that external debt in current period is positively influenced by debt in previous period while rise in military spending has positive and significant effect on external debt. An increase in income has inverse impact on external debt. The effect of investment is also positive and significant on external debt in the country. Same inference can be drawn for short run results but investment has effect on external but it is statistically insignificant. The causality analysis indicated bidirectional causality between external debt and military spending while strong causation is running from external debt to military spending and same inference can be drawn for economic growth and military spending. Unidirectional causal relation is found from external debt to economic growth and military spending to investment.
The amount of public debt is equivalent to 56% of GDP. The internal debt is mounted to 31% of GDP while external debt is amounted to 25% of GDP in 2008-09. In the context of policy implication, present study suggests that Pakistan is an agrarian country. The exports share of agriculture sector is 1.1948% of merchandise exports while share of imports of agriculture sector is 7.8176% of merchandise exports in 2008-09. It implies that agriculture sector has potential in making contribution to curtail external debt by boosting exports share in trade. In doing so, government must pay her attention to increase research and development expenditures to improve the quality of agri-exports.
This will not only increase productivity of agriculture sector but also enhance its share to trade. The increased share of agriculture will be used to curtail external debt by earning foreign exchange. Furthermore, manufacturing sector should also be on priority to increase its share to trade for foreign exchange reserves by diversifying the quality of intermediate and finished export items.
In the background of our empirical investigation, it can be highlighted that both Pakistan and India are strategically important nuclear states, and their cordial mutual relationship is important for the South East Asian region as well as the global economy and peace.
Therefore, it is highly appropriate if both governments initiate bilateral talks to develop mutual confidence and harmony to fight against poverty. The population size and population growth rate of both countries do not permit them to invest such a huge chunk of their annual budgets on their military spending. It is strategically important for them to start dialogue to reach at a consensus for peace and prosperity by reducing their military size and expenditures. The reductions in military spending of both countries by mutual
understanding will save the countries from external debt and will shift resources to developmental projects and stimulate the pace of economic growth. This will enhance the capacity to develop as well as increase the market share by raising production levels for both economies.
For further research, our model has potential to include other relevant variables such as internal debt following (Narayan and Narayan, 2008) and exchange rate i.e. the rational is that rapid currency devaluations raise the cost of debt servicing which increase debt services and hence total volume of external debt. Inclusion of these variables will provide a comprehensive picture which enables us to capture the exact effect of exchange rate on external debt and, whether military spending raises internal debt or not.
Footnotes
1. They included external debt, military spending, exports, GDP, foreign exchange reserves and interest rate proxied by six-month London Interbank Offer interest rate in their model.
2. This dummy takes value 1 when government is right wing, 2 when government is center right, 3 when government belongs to centre, 4 when government is center left and 5, when government is left wing.
3. Sezgin has used time series data over the period of 1979-2000 with log-linear specification.
4. Sezgin (2004) findings are consistent with the view by Looney (1989) for case of Turkey.
5. Bruck (2000) has noted that civil war in Mozambique is major reason for high burden of external debt.
6. To establish the goodness of fit of the ARDL model, the diagnostic test and the stability test have also been conducted. The diagnostic test examines the serial correlation, functional form, normality and heteroscedisticity associated with the model. The stability test is checked by applying the cumulative sum of recursive residuals (CUSUM) and the cumulative sum of squares of recursive residuals (CUSUMSQ).
7. If cointegration is not detected, the causality test is performed without an error correction term (ECM).
8. However, it should be kept in mind that the results of the statistical testing can only be interpreted in a predictive rather than in the deterministic sense. In other words, the causality has to be interpreted in the Granger sense.
9. ADF, P-P and DF-GLS unit root tests showed unit root problem till lag 5.
10. See Feridun and Shahbaz (2010) 11. For more details (see Lütkepohl, 2005)
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Appendix-A
Innovative Accounting Technique
To investigate the dynamic relationship between military spending, external debt, economic growth and investment, Vector Auto Regression (VAR) Approach has been used. Innovation Accounting Technique (variance decomposition and impulse response function) that has not been used before to investigate causal relationship between the variables. This approach estimates the forecast error variance decomposition which allows inferences to be drawn with the proportion of movements in particular time periods due to its own shocks and shocks arising from other variables in the VAR as well.
Through the application of Vector Auto Regression, effect of a shock of one variable can be checked on the other variables included in the model which also include future values of shocked variables. This procedure tends to break down the forecast error variance of each variable following a “shock” to particular variable that makes possible to identify which variable affects strongly and, vis-à-vis its shock. For instance, innovative shock in military spending leads substantial variations in external debt is examined through application of Vector Auto Regression but shocks in external debt shows only minimal impact on military spending. This leads to conclude that military spending seems to
granger-cause external debt or causal relationship is running from military spending to external debt.
The time path of the effects of innovative shocks of independent variable can be examined through impulse response function. The impulse response function also estimates that how each variable responds over time to the first “shocks” in other variable (s). These two approaches are termed as “Innovation Accounting Technique” which allows a perceptive insight into the dynamic relation between military spending, external debt, economic growth and investment. Military spending granger-causes external debt if military spending explains more of the variance as compared to external debt and vice verse, as it is indicated in variance decomposition method which breaks down the forecast error for military spending and external debt. In the light of the above discussion, one may establish a VAR system that takes following the form:
t t k
i i
t V
V = δ − +η
∑
= 1 1where, Vt =(LREDPCt,LRDSPCt,LGDPCt,LINVPCt) )
, , ,
( REDPC RDSPC GDPC INVPC
t η η η η
η =
δk
δ1− are four by four matrices of coefficients and η is a vector of error terms.
REDPCt = real external debt per capita, RDSPCt = real military spending per capita, GDPCt = real GDP per capita and INVPCt = real investment per capita.