Relationship Between Military Expenditure, Economic Growth, and Social Expenditure in India, China, and Bangladesh

Abstract

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Introduction

The relation between military expenditure and economic growth on the one hand and social expenditure and economic growth, on the other hand, has received considerable theoretical and empirical scrutiny. Most economists take the view that unproductive public expenditure generally slows down economic growth. When it comes to military spending, however, they have often argued the opposite – that public expenditure boosts economic growth. Emile Benoit (1973; 1978) set the ball rolling with some surprising statistical findings, allegedly showing that military expenditure enhances economic growth in developing countries. The fundamental issue addressed in many studies is whether a high ‘military burden’ (usually defined as the share of military expenditure in GDP) tends to decelerate economic growth in developing countries. So our fundamental point is whether there exists a trade-off between warfare and welfare, in the sense that high levels of spending frequently raise concerns as to the ‘opportunity cost’ involved in military spending—the potential civilian uses of such resources that are lost. So the matter which is of utmost importance to policy makers is the portion of the budget in a nation that is to be dedicated to development, security and welfare. When one looks back to a decade earlier, e.g. 2004 to 2014 it clearly shows an increasing trend in defense expenditure which is more prominent for South Asia than the world as a whole (figure 1).

Figure 1.

Changes in military expenditure in South Asia and world (2004- 2014)

(Source: U.S. Department of State, 2016)

The existing literature on the relationship between military expenditure and economic growth confirms that three kinds of linkages may be plausible:

• 1.

  There exists a positive and significant relation between them;

• 2.

  There exists a negative and significant relation between them;

• 3.

  There exists no significant relation between them;

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Literature Survey

Benoit's analysis (1978) finds a significant, positive correlation between defense expenditure as a proportion of national income and the growth rate of civilian output between 1950 and 1965. Heo (2010) similarly explains that the positive employment effects of defense spending also boost aggregate demand in the United States economy. The basic idea by which military expenditure may positively related to economic growth is through aggregate demand. It is related to the capacity utilization, and also that when an economy is in a phase of recession an increase in military expenditure will boost the economy. This has been referred to as ‘military Keynesianism’. A second explanation argues that military expenditure is important safe guard national security that is vital for supporting economic activities. A third explanation argues that military expenditure leads to employment. Blank and Rothschild (1985) report that defense programs generate employment in the U.S. because of the large size of the U.S. armed forces. A more plausible argument is that military expenditure stimulates economic growth through various kinds of ‘spillover effects’ on civilian production, as argued in some detail in Benoit’s original study. In developing countries, where advanced military technology has much less to contribute to basic civilian needs, military R&D is unlikely to give a major boost to technological innovation in the civilian sector. However, military expenditure in developing countries may have other types of spillover effects, such as civilian uses of military infrastructure (e.g. roads and satellites) and the role of the army in disaster relief. Another possible spillover effect is the military influence on civilian attitudes and human capital. One version of this idea is that the military establishment contributes to the process of modernization by fostering values such as efficiency, discipline and national unity (Benoit, 1978, pp. 277-8). Balan (2015) tried to examine the direction of causality between political instability, defense spending and economic growth for 12 countries during the period 1988-2013 using panel data analysis. He found both directional positive causality relationship between the variables for the set of countries.

Key Terms in this Chapter

Educational Expenditure: Educational expenditure constitutes a very important component of social expenditure of a nation. General government expenditure on education (current, capital, and transfers) is expressed as a percentage of GDP. Expenditure on education is an investment that can foster economic growth, enhance productivity, contribute to personal and social development and reduce social inequality. The proportion of total financial resources devoted to education is one of the key choices made by governments, enterprises, students and their families. The indicator covers expenditure on schools, universities and other public and private institutions delivering or supporting educational services. Expenditure on institutions is not limited to expenditure on instruction services but includes public and private expenditure on ancillary services for students and their families, where these services are provided through educational institutions. At the tertiary level, spending on research and development can also be significant and is included in this indicator, to the extent that the research is performed by educational institutions. Public expenditure includes both direct expenditure on educational institutions and educational-related public subsidies to households administered by educational institutions. Private expenditure is recorded net of these public subsidies attributable to educational institutions; it also excludes expenditure made outside educational institutions.

Health Expenditure: Health expenditure measures the total cost of health care (public and private expenditures) as a percent of GDP for a few nations. World Health Organization (WHO), Organization for Economic Co-operation and Development (OECD) provides total health expenditure by nations. As per Total health expenditure per capita in US dollars (PPP), 2014, USA, Switzerland, Norway, Netherlands, Germany, Sweden, Ireland, Austria, Denmark, Belgium are some countries which figures the top. It provides an estimate of a country’s expenditure towards meeting social needs and leading a better and healthy life.

Military Expenditure: A military expenditure (or military budget), also known as a defense budget, is the amount of financial resources dedicated by a nation to raising and maintaining an armed forces or other methods essential for defense purposes. Military budgets often reflect how strongly an entity perceives the likelihood of threats against it, or the amount of aggression it wishes to employ. It also gives an idea of how much financing should be provided for the upcoming year. The size of a budget also reflects the entity's ability to fund military activities. USA, China, Saudi Arabia, UK, France, Japan, India, and Germany are considered to be the countries with largest military expenditure (as per 2014 figures). Stockholm International Peace Research Institute (SIPRI) Military Expenditure Database, World Military Expenditures and Arms Transfers (WMEAT) and World Development Indicator (WDI) are some of the important sources of this data.

Economic Growth: Economic growth of a nation is generally measured by the growth of per capita income or real GDP. It provides the first-hand measure of the level of standard of living of the population of a particular nation. Economic growth captures only the GDP or income growth and so it is a necessary but not a sufficient condition for development of a nation.

ARDL Test: To overcome this problem of non-stationarity and prior restrictions on the lag structure of a model, econometric analysis of time series data has increasingly moved towards the issue of co-integration. The reason being that, co-integration is a powerful way of detecting the presence of steady state equilibrium between variables. In applied econometrics, Autoregressive Distributed Lag (ARDL) co-integration technique or bound test of co-integration techniques have become the solution to determining the long run relationship between series that are non-stationary, as well as re-parameterizing them to the Error Correction Model (ECM). ARDL co-integration technique is preferable when dealing with variables that are integrated of different order, I(0), I(1) or combination of the both and, robust when there is a single long run relationship between the underlying variables in a small sample size. The long run relationship of the underlying variables is detected through the F-statistic (Wald test).

Time Series Data: The entire analysis is based on secondary time-series data. A time series is a set of statistics, usually collected at regular intervals. Time series data occur naturally in many application areas. The aims of time series analysis are to describe and summarize time series data, fit low-dimensional models, and make forecasts. In general, the time series models employ the stationary series as they are mean reverting, ensuring the constancy of parameters (mean, variance etc.) and having limited memory of past behavior (i.e., shocks are only transitory). For non-stationary series, such as random walk, the parameters are time dependent (or varying). The presence of either unit root (s) or deterministic trend (or both) will lead to the non-stationarity.