Effect of military spending on private investment in Nigeria: does a crowding-out effect exist?

This study adopts ARDL and VAR estimation methods to examine whether military spending crowd-out or crowd-in private investment in Nigeria. We use the data that covers the period from 1970 to 2019. Our results, based on the ARDL method, show that military spending only crowds-out private investment in the short run. In the long run, military spending crowds in private investment. The results are robust to the use of alternative estimation methods. Specifically, IRF results show that military spending has a contemporaneous negative effect on private investment. However, the negative effect turns positive after the third period. Also, FEVD results show that most of the variation in private investment is explained by its shock and few by military spending. Our findings have policy implications. While it is advisable to spend more on the military to curtail the activities of insurgents, bandits and kidnappers and to restore confidence in investors, it is important also to take cognisance of the fact that military spending can crowd out private investment.


Introduction
The role of government spending in engendering economic growth has received both commendation and condemnation in the economic literature. From a commendation perspective, it is believed that government provides some vital services that aid and sustain the economy.
Such services include the provision of public goods such as national defense, the rule of law and law enforcement, among others. These goods are not provided by individual citizens or private organisations in most countries because there are no incentives to produce such goods (Wagner, 2007). Thus, the duty of the government is to ensure that the lives and properties of its citizens and foreigners living within its territory are protected against internal and external invaders. Apart from this, when the economy is in crisis, especially during an economic recession, the government is usually called upon to spend more to bail the economy out of recession. Government spending during this period, as argued, would raise effective demand, boost production and investment and spur economic growth through the multiplier effect (Keynes, 2018). From a condemnation perspective, however, it is believed that government spending, in most cases, is financed by taxes paid by individual citizens or private organisations. Such tax-financing government expenditure increases the tax burden on citizens and private organisations and thereby leads to a reduction in citizens' spending or private organisations' spending and investment, which would eventually lead to the "crowding-out" of private investment (Stratmann and Okolski, 2010).
Apart from health and education, the government also spends on the military for the procurement of arms and ammunition to provide security against external aggressions. Besides the procurement of military arsenals, the government also spends on the military to take care of military personnel welfare, which includes the regular payment of salary and other benefits. While some defense economists believe that military spending can be beneficial to investment, economic growth and employment, others submit that such an increase in military spending has opportunity costs. Those who are against the increase in military spending argue that, in the presence of scarce resources, the allocation of more funds to the military implies depriving other areas of the economy. Smith (1980) submits that increase in military spending affects the public components of consumer consumption expenditure which is maintained through political pressure. Besides, he further argues that military gadgets are produced by highly capitalised industries and thus, an increase in military spending has a direct effect on private investment with an inelastic capacity. Hence, military spending and private investment usually compete for a fixed proportion of public resources. In this case, military spending does crowd-out private investment in the long run (Scott, 2001;Hou and Chen, 2014). Apart from the arguments for and against a perpetual increase in military spending, there is growing concern among citizens across the world, especially in advanced countries, about the perpetual increase in military spending (Sajid, 2021).
Many of the citizens are openly expressing their dissatisfaction through different media, such as protests, jingles and other means. In the United States, for instance, Sajid (2021) noted that a great proportion of the budget is devoted to military spending on an annual basis. In 2020, out of a total of $4.7 trillion budget, about $0.73 trillion went to defense spending, suggesting that other areas of the economy, such as education and health, are likely to be affected. The ultimate consequence of the growing military spending in the country is mounting public debt and budget deficit on an annual basis, which in turn, could have a detrimental effect on the overall economy (Caruso and Di, Domizio, 2017;Sajid, 2021). only led to the loss of lives, loss of properties and displacement of people but also the loss of investment, especially in the Northern region. According to the Global Conflict Tracker (2021), more than 350,000 people have lost their lives, more than 3 million people have been displaced from their permanent habitations, and nearly 310,000 people have become refugees.2 Besides, farming in the North, especially in the northeast, has been adversely affected, resulting in low agricultural productivity (Adelaja and George, 2019). Moreover, cross-border trading or business has been drastically reduced between the north and its neighbouring countries such as Niger (Kimenyi, et al. 2014). In fact, Okereocha (2012) submits that the country has lost about N1.33 trillion in Foreign Direct Investment.
In light of this, the primary objective of this study is to examine whether or not an increase in military spending crowds out private investment in Nigeria. Researchers have been keenly interested in studying the relationship between military spending and private investment to detect a crowding-out effect. The empirical findings are diverse depending on a group of countries or a country studied, the models adopted, the estimation techniques used and the measures of military spending used. Besides, most of the extant studies focused on developed economies such as OECD member countries and others (Smith, 1980-OECD;Smith and Dunne, 2001;Hou and Chen, 2014;Dunne and Smith, 2020;OECD;Gold, 1997;Atesoglu, 2004;Perioni, 2009-USA;Scott, 2001-UK, Smith andDunne, 2001;Malizard, 2015-France;Üçler, 2016-Turkey;Kennedy, 2021-Indonesia). Studies that examine how military spending affects investment in Nigeria are relatively scarce. However, there are a couple of studies that examined the effect of military spending on foreign direct investment but not on domestic private investment (Adediran et al., 2018;Edith et al., 2019). It is, however, known that foreign investors, in most cases, bring their capital to invest in many developing countries like Nigeria. Most of them hardly depend on the domestic capital market to finance their investments. Hence, the issue of the government competing with them for funds in the financial market does not arise. Thus, it cannot be said that military spending crowds out foreign direct investment. The same cannot be said of domestic private investors who, in most cases, seek funds in the domestic financial market.
To explore the impact of military spending on private investment, we set out to achieve two objectives. The first objective is to examine whether the crowding out of military spending occurs in the short run or the long run. This is important for policy decision-making. Assuming that the crowding-out effect occurs in the short run, it would inform the government that its spending, including military spending, does not have a permanent effect on private investors. However, if it occurs in the long run, this may call for drastic reforms in the way the government gets involved in the financial market. To achieve this objective, we employ a novel Autoregressive Distributed Lag (ARDL) estimation method developed by Pesaran et al. (2001). The method can be used to distinguish the effect of military spending on private investment in the short run from the long run.
Although Error Correction Model (ECM) can also be used to achieve the same objective, however, the method has some limitations. One of the limitations of using ECM estimation is that they are only applicable when the variables are integrated of order 1, a condition that may not be met in practice. In practice, some variables could be integrated of order 0 or order 1 or a mixture of both.
ARDL estimation method becomes useful when the variables display these characteristics. It must, however, be stated that ARDL can be rendered unusable if any of the variables are integrated of higher order, especially the integrated order of 2. The second objective is to examine the dynamic effect of military spending on private investment over time. In this case, we employ Sims (1980)'s Vector Autoregressive method and compute the necessary Impulse Response Function (IRF) as well as Forecast Error Variance Decomposition (FEVD). With this approach, it is possible to trace the dynamic effect of military spending on private investment over time.
The rest of the study is structured as follows. Section 2 reviews the existing studies. Section 3 presents the theoretical framework, estimation model as well data sources. The results are presented in section 4. Section 5 concludes with policy implications.

Literature review
Most of the studies on the effect of military spending on the economy are concentrated on how military spending affects economic growth. Beginning with Benoit (1978), who concluded that military spending is positively correlated with economic growth in developing countries, a huge number of studies had been conducted in different countries and groups of countries to thoroughly examine the effect of military spending on economic growth. However, a concrete consensus as regards the real effect of military spending on economic growth has not been reached. Dunne and Smith (2013) summarised the rationales for this development as follows: different theoretical considerations and methodological approaches, different countries considered, different data and periods employed (see also Alptekin and Levine, 2012). Consequently, some studies documented the positive effect of military spending on economic growth (Saba and Ngepah, 2019), while others established a negative effect of military spending on economic growth (Shahbaz, Afza and Shabbir, 2013). There are some strands of studies that did not establish any cogent nexus between military spending and economic growth. (Minzt and Huang, 1990).
As regards the effect of military spending on investment, Table 1 summarises the empirical findings from the existing studies. Like the studies on the nexus between military spending and economic growth, the relationship between military spending and investment remains inconclusive. From the table, three empirical findings could be deduced irrespective of different estimation techniques deployed, country or countries studied as well as periods considered. The crowding-out effect of private investment by military spending, however, dominates the literature (Smith, 1980;Dreger, 1986;Knight, et al., 1996;Scott, 2001;Pieroni 2009;Hou and Chen, 2014;Malizard, 2015;Lorusso and Pieroni, 2017;Kennedy, 2021). This notwithstanding, a handful of studies concluded that military spending crowded-in investment (Üçler, 2016;Kollias and Paleologou, 2019). The third strand of studies did not find any evidence of a relationship between military spending and investment (Smith and Dunne, 2001;Morales-Ramos, 2020;Atesoglu, 2004;Dunne and Smith, 2020).
The relationship between military spending and investment could still be complementary. This is established by Malizard (2015) who investigated whether military spending crowded-out investment in France using disaggregated data. The evidence of complementarity comes from the relationship between public investment and private investment. According to him, the reason for this phenomenon could be adduced to the fact that the military equipment sector in France is highly capitalistic and, as such, defense in R & D may contribute to the private sector productivity.
The way military spending affects private investment could depend on the level of economic development of a country. Kollias and Paleologou (2019), who investigated the effect of military spending on economic growth and investment, concluded that military spending has a positive and significant effect on investment in high-income countries whereas its effect on low-income and middle-income countries is negative. This outcome for the low-income and middle-income countries is rationalised by the fact that most of the countries belonging to these groups could be facing the challenges of resource constraints. Hence, devoting more resources to the military at the expense of other productive projects appears to be disastrous to private investment. However, a couple of studies have examined the effect of military spending on foreign direct investment in Nigeria (Aderemi, et al. 2018;Edith et al., 2010). Some studies also examined the effect of the general government on private investment in Nigeria without a specific reference to the influence of military spending on private investment (Akinlo and Oyeleke, 2018;Olaifa and Benjamin, 2020). Given the paucity of studies on the nexus between military spending and private investment in Nigeria, this study is conducted to fill this gap.

Theoretical consideration and model specification
There are three theoretical models (supply model, demand model and supply-demand model) used to explore the crowd-out effect of military spending (see Morales-Ramos, 2002). In this study, we adopt a demand model. We chose the demand model because we can easily get data for its estimation. The demand model rests on the Keynesian Theory, which considers military spending as part of aggregate demand. Following Hou and Chen (2014), the demand model begins with the national accounting identity specified as follows: where Y denotes actual production in the economy, Q is the potential output, W refers to the gap between actual output and potential output.
, , C I M and B are aggregate consumption, investment (could be private or public investment), military spending and balance of trade, respectively. Equation 1 can be re-specified if we express it as a share of potential output. Thus, According to Smith (1980), the share of consumption is Here u is the unemployment rate, g is the actual output growth rate. It is believed that when the shares of unemployment and actual output in potential output increase, the share of consumption in it should decline. When this happens, equations 2 and 3 become: Then equation 4 can be rearranged as: In many empirical studies that employ the demand model to investigate the crowding-out effect of military spending on investment, equation 5 is often adopted. Equation 5 can be formalised as an econometric model as follows: On a priori ground, if the coefficient of military spending is negative, that is, 3 α is negative, it means that military spending crowds out private investment.
Following Raifu and Afolabi (2022) and Raifu, Obijole and Nnadozie (2022), equation 6 is formalised into the ARDL framework as follows: In equation 7, ∆ is the first difference operator, 0 α is a constant and drift component of the model, H α α α α ≠ ≠ ≠ ≠ . The error correction model, which shows the speed of adjustment from the short-run disequilibrium towards the long-run equilibrium, is specified as follows.
where ect is the error correction term, it is expected that λ the coefficient of error term must be negative, less than 1 and statistically significant to claim that there is an adjustment from the short-run disequilibrium towards the long-run equilibrium.
For the second objective, we use Vector Autoregressive (VAR) method. The basic principle of VAR is that all variables are treated endogenously and expressed as the lags of one another, that is, the lag of dependent variables and independent variables for each variable. Our VAR model consists of four variables which include military spending, private investment, real GDP and unemployment. The general VAR framework is specified as follows: where t X = (private investment, military spending, real GDP, unemployment), t ε is the error term, 0 β denotes the identity matrix and 1 k β β − are four by four matrices of the coefficients. We compute impulse response which shows the response of private investment to innovative shocks to military spending as well as other variables included in the model. The orthogonalised IRS is computed using a Cholesky decomposition of the variance-covariance matrix of residuals following Shan (2002). Thereafter, the FEVD, which depicts the proportion of private investment that can be explained by its shock and shock to military spending is also estimated.

Data sources and preliminary findings
The   Table 4. Evidence from the Table shows that all the variables are integrated of order 1, that is, they contain unit root and they become stationary after the first difference.      spending as a percentage of GDP, real gross domestic product and unemployment rate respectively WC, WC&T and WDC&T denote unit root test with constant, with constant and trend and without constant and trend respectively. *, ** and *** denote 10%, 5% and 1% level of significance respectively.  Table 6 show how fast the economy returns to equilibrium when it is temporarily destabilised by either internal or exogenous shocks.

ARDL results
To determine whether there would be adjustment in the long run, the coefficient of error correction term must be negatively signed, less than one and statistically significant. Our results in the two models (model 1 and model 2) follow a priori expectations in the sense that they are negatively signed, less than one and statistically significant. Precisely, the coefficients of ECM in model 1 and model 2 are -0.530 and -0.567, respectively. This implies that there is an adjustment towards the long-run equilibrium from the short-run disequilibrium. Thus, we can conclude that about 50% of errors that occurred in the previous year can be corrected in the current year.
As regards the crowding-out of military spending, our results show that in the short run, military spending as a percentage of GDP has a negative effect on private investment (model 1).
However, the negative effect is statistically significant and occurs with lags, specifically when military spending is lagged for two periods. Thus, when military spending as a ratio of GDP We conduct a series of robustness tests to check the reliability of our results. In Table 6 Table 7. It is evident from the Table   that military spending crowds-in private investment in the long run irrespective of estimation methods.
For other control variables, we find that economic growth has a positive significant effect on private investment. This finding is inconsonant with economic rationality that the period of economic growth leads to a boom in private investment. However, unemployment has a negative insignificant effect on private investment, especially in the long run. Similar findings were documented for France by Malizard (2015), who found the accelerator effect of economic growth on private investment and the inverse or adverse effect of unemployment on private investment.
Post-estimation diagnostic test results are also presented in  3.65 4.66 Note. 1(0) is the lower bounds while I(1) is the upper bounds. F-statistic (at least at 5%) below lower bounds implies no cointegration, above upper bounds means cointegration and in-between the two bounds means indecision.   spending as a percentage of GDP, real gross domestic product and unemployment rate respectively *, ** and *** denote 10%, 5% and 1% level of significance respectively

VAR estimation results
Our second objective is to examine the dynamic effect of military spending on private investment.
The optimal lag length selected is 4 using the Akaike information criterion. We do not report the result of the VAR estimation. We only report the results of impulse response function (IRF) and forecast error variance decomposition (FEVD). The results of the impulse response function are reported in Figures 5 and 6 for models 1 and 2. The IRF results show that one standard deviation shock to military spending has a contemporaneous negative effect on private investment. This implies that military spending crowds out private investment when there is a sudden shock to military spending. However, we observe that the negative effect of military spending on private investment turns positive after three periods, that is, after three years. This suggests that military spending crowds-in private investment in the long run. As previously mentioned, the increase in military spending in recent times in Nigeria is to tackle insecurity and thereby provide enabling environment for investment to thrive in the country. Thus, the need for security is a possible explanation for the positive shock of military spending to investment in Nigeria (Kollias and Paleologou 2019). This is contrary to the explanation given by Kollias and Paleologou (2019) for the high-income or industrialised countries that have military hardware manufacturing companies.
The explanation of the persistent positive association between military spending and investment in high-income countries, according to Kollias and Paleologou (2019) is premised on the supplyside effects which are engineered by technological spillovers from the defense industry to the other sectors of the economy The results of FEVD, which shows the proportion of variation in private investment explained by military spending shock, are reported in Table 8 for models 1 and 2 (The graphs of FEVD are put in the appendix). The results show that most of the variability in private investment is due to its shock. In specific terms, especially from the baseline model (model 1), 100% of the variation in private investment is due to its shock in period 1. However, it can be observed that the variation in private investment to its shock declines over time. In period 10, only about 36.55% variation in private investment is due to its shock. Military spending account for a 0% variation in private investment in period 1. However, as the period increases, the variation in private investment caused by the military spending shock increases. In period 10, military spending shock explains about a 14.06% variation in private investment, signifying that military spending has a positive effect on private investment over time.

Discussion
The controversies surrounding the crowding-out effect of military spending depend on many factors such as whether a country is an exporter or importer of military hardware, the stage of development of the country (developed countries or developing countries), how the government raises funds to finance the military spending and allocation of available resources between the military spending and other government spending such as health, education and infrastructure (Morales-Ramos, 2002;Malizard, 2015;Atanassovy and Nanda 2018). With regard to military hardware exporting countries such as the United States, Malizard (2015) argued that an increase in military spending can have a spillover effect on the private sector (investment) through technological progress, which can stimulate growth and investment. However, the same cannot be said about the military hardware importing countries, mainly developing countries. The necessity for ensuring the security of the environment, as submitted by Kollias and Paleologou (2019), could engineer or justify the increase in military spending in developing countries, especially country like Nigeria, which is currently facing severe insecurity that is threatening the foundation of its existence and the economy. The increase in military spending can then be used to procure more arms and ammunition to secure society and provide the enabling environment conducive enough for private investment and economic growth.
In light of this, we investigate the crowing-out effect of military spending in Nigeria with two objectives in mind. First, we examine whether or not the crowding-out effect of military spending is a short-run or long-run phenomenon or both. The first objective is achieved by using a novel ARDL estimation method. Second, we examine the response of private investment to an increase in military spending via the Impulse Response Function (IPF) and Forecast Error Variance Decomposition (FEVD) methods. Our findings based on the first objective reveal that the crowding-out effect of military spending is a short-run phenomenon. In the long-run, military spending does crowd-in private investment in Nigeria. Apart from the fact that our finding is in tandem with submission from existing studies such as Gold (1997) and Hou and Chen (2014), who argued that the negative effect of military spending could fizzle out in the long run, it also justifies the increase in military expenditure in recent time by the government of Nigeria. As previously stated, the country is currently facing security challenges which appear to be threatening the foundation of its existence and future economic prosperity. To combat the insecurity coming from not only the terrorists (Boko Haram and ISWAP) but also bandits, kidnappers, robbers, hoodlums and many others who have become dangerous species to the lives and properties of law-abiding citizens, the increase in military spending becomes inevitable.
Many studies have documented the negative effect of insecurity and both private and foreign investment in Nigeria (Olubunmi, 2018;Jelilov, Ozden and Briggs, 2018;Oji and Afolabi, 2022;Yusuf and Mohd, 2022). Yusuf and Mohd (2022) specifically stated that domestic gross capital formation (domestic investment), unemployment rate, foreign direct investment and government spending on education are negatively affected by the growing insecurity. This happens because insecurity drives away investors, who are always afraid to make investments in an unsecured country. The resultant effect of heightened insecurity is a drastic decline in investment activity.
Such a decline in investment activity is detrimental to the economy. Thus, the increase in military spending in Nigeria is not a waste in the long run because it signals to the investors that government is serious about providing adequate security. This would, in turn, restore the confidence of the investors and enable them to further make more investments in the country.
Our findings based on the second objective corroborate the results based on the first objective.
Based on the IPF results, we discovered that shock to military spending only causes a temporary decline in private investment. The decline effect, as discovered, fizzle out over time, leading to the crowd-in effect of military spending in the long run. However, from FEVD, most deviations in private investment are explained by the shock to private investment and such deviations decline over time. Only about 14.06% of the deviation in private investment can be explained by the shock to military spending. This discovery solidifies the short-run effect of military spending on private investment in Nigeria.
While our study has shown the benefit of an increase in military spending on private investment, particularly in the long run, it, however, has its limitation because it does not cover the whole aspect of investment in the country, especially public investment (government investment. In the literature on military spending, it has been argued that an increase in military spending also crowd-out public investment because such an increase in military spending, in the face of resource or budget constraints, implies that other expenditures such as education, health and infrastructure have to decline or remain unchanged. Hence, in the case of Nigeria, future studies need to investigate the relationship between military spending and public investment.

Conclusions and policy implications
In this study, we investigated the possibility of a crowding-out effect of an increase in military spending on private investment in Nigeria using the data that spans from 1970 to 2019. Two main objectives were set, and they were achieved through the use of the ARDL and VAR estimation methods. For the first objective, the ARDL estimation method enables us to know whether the crowding-out effect of military spending occurs in the short run or the long run. In the case of the second objective, the VAR estimation method, through IRF and FEVD, helps to trace the response of private investment to military spending shock and the proportion of variation in private investment explained by military spending shock. We also performed some preliminary analyses, which included correlation analysis and unit root tests.
Our findings are summarised as follows. ARDL results show that military spending crowds out private investment in the short run, whereas it crowds in private investment in the long run. This is supported by the results obtained from IRF, which show that shock on military spending has an instantaneous negative effect on private investment. However, the effect of military spending on private investment turns positive after three periods. The FEVD results reveal that most variation in private investment is explained by its shock, while military spending explains little variation in private investment.
Given our results, we conclude that while it is important for the government to spend more on the military for the procurement of arms and ammunition to stem the tide of insecurity bedevilling the country from the activities of insurgents, bandits and kidnappers and to restore confidence in investors who are sceptical of investing in the country, it must, however, take cognisance of the fact that military spending can crowd-out private investment.