Does Military Spending Explode External Debt in Pakistan?

Abstract

This paper investigates the effect of military spending on external debt in case of Pakistan for the period of 1973–2009. For this purpose, the autoregressive distributed lag bounds testing approach to cointegration is used to examine cointegration among the variables. The ADF, P-P, and ADF-GLS unit root tests are applied to test the integrating order of the variables. The Ordinary Least Square (OLS) and error correction method regressions are used to investigate the marginal impact of military spending on external debt in the long and short run. Our findings indicate the existence of cointegration that confirms the presence of a long-run relationship among military spending, external debt, economic growth, and investment. Further, our results reveal that a rise in military spending increases the stock of external debt; an increase in investment also increases external debt; however, there is an inverse effect of economic growth on external debt. An implication of the findings reported herein is that there is a need to formulate a comprehensive economic policy for curtailing external debt in case of Pakistan.

Keywords:

• Military spending
• External debt
• Cointegration

JEL Codes:

• C12
• O22

Notes

¹In their model they included external debt, military spending, exports, GDP, foreign exchange reserves, and interest rate proxied by six-month London Interbank Offer interest rate.

²This dummy takes the value 1 when government is right wing, 2 when government is center right, 3 when government belongs to center, 4 when government is center left, and 5 when government is left wing.

³Sezgin (2004) has used time series data over the period of 1979–2000 with log-linear specification.

⁴The Sezgin (2004) findings are consistent with the view by Looney (1989) for the case of Turkey.

⁵Bruck (2000) has noted that civil war in Mozambique is the major reason for the high burden of external debt.

⁶To 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 (CUSUM_SQ).

⁷If cointegration is not detected, the causality test is performed without an error correction term (ECM).

⁸However, it should be kept in mind that the results of the statistical testing can only be interpreted in a predictive rather than in a deterministic sense. In other words, the causality has to be interpreted in the Granger sense.

⁹ADF, P-P, and DF-GLS unit root tests showed a unit root problem till lag 5.

¹⁰See Feridun and Shahbaz (2010).

¹¹For more details see Luetkepohl (2005).