Julien Malizard
According to SIPRI, world defence spending is equal to $1.739 billion in 2017, which represents nearly 2% of the world GDP. Given its economic importance, the field of defence has been investigated by many economists. Defence economics relies on the use of all the economic approaches relative to the defence sector. The first articles published in the field mainly focused on industry (microeconomics), alliance (public economics), arms transfers (international economics) or economic impact (macroeconomics). More recently, the scope has been enlarged to new topics such as civil wars, terrorism or new areas, such as China.
From a methodological point of view, a major difficulty arises when it comes to measure defence output. Indeed defence spending is just a sum of inputs (wages and equipment) but does not give any figure of the efficiency of the allocation process (which is the core of economics). The key issue of “how much is enough” remains involved. As a consequence, defence economic academic literature says little about the main purposes of defence and focus only on the pure economic effects.
This chapter reviews the main methods used by economists in order to evaluate the causes and consequences of the defence sector: narrative approach, theoretical model and econometric analysis. Each method is discussed in length to examine their weakness and forces.
Introduction
According to the latest figures published by the think tank, Stockholm International Peace Research Institute (SIPRI), world defence spending (DS) was USD 1,750 billion in 2018, which is the highest level in history, surpassing the levels previously observed during the Cold War. On average, the share of DS is close to 2% and seems rather small compared to previous periods (Sandler and George 2016). There are microeconomic figures corre- sponding to these macroeconomic figures and, in particular, those related to the equip- ment market. From SIPRI figures on the defence industry, the total turnover of the top 100 is close to USD 450 billion. Flows of trade are difficult to measure but a recent esti- mation indicates that arms exports are less than USD 100 billion.
Given the economic importance of the defence sector, economic evaluation of defence matters a great deal. Of course, the main goal of defence is to provide security, and economic factors should play a limited role. However, given the impor- tance of DS at the macroeconomic and microeconomic levels, the relationship between economics and strategy is of interest.1 Economic literature provides useful information on many aspects of defence policy, such as the armament industry, defence budget or economic impact of defence activities. The aim of this chapter is to discuss the economic approach of defence studies and to review briefly the main themes of the academic literature in economics.
Following Robbins, “Economics is the science which studies human behavior as a relationship between ends and scarce means which have alternative uses” (1935, p. 16). Defence economics should not be different than economics: an economic approach requires a comparison of ends and means. However, the key issue is to convert military ends and means into economic ends and means. Strategic means are obviously difficult to evaluate and, thus, economists often rely on defence policy in order to investigate military ends. Military means are more easily converted into economic means because there is a price tag for each item.
The relationship between military ends and means has been investigated by Malizard(2019). He stresses the importance of “strategic autonomy” for the French defence policy that provides the framework for strategic ends. Strategic autonomy is barely defined, but refers to nuclear deterrence, the choice to conduct (alone or in a coalition) a large spectrum of military operations, and the necessity to rely on French equipment; the French interests include not only its national territory, but also overseas depart- ments. Given these ends, France armed forces require a large spectrum of equipment. The military means (in terms of soldiers and equipment) are then converted into economic means: a large defence budget compared to other European countries, and an independent defence and industrial base (DIB). These economic means are more easily analysed by economists as they refer to the budget (macroeconomics) or industry (microeconomics), which constitute the two main areas in economics.
Standard economic evaluation is based on “cost-benefit approach” (CBA). For each alternative, CBA requires data on what is paid (cost) and what is gained (benefit). The net result provides an economic rationale for each alternative and one may rank all the alternatives to get the best solution, which is called “an optimum” in economics. In macroeconomics, such a method is difficult to apply because there is no data on each alternative; in such a case, the solution is to directly analyse the effect of one variable on another, possibly by directly comparing the two alternatives.
Unfortunately, CBA cannot be used for defence economics. The main reason is that the benefits are not measurable. Consider a rise in DS by USD 1. How will this rise affect security? Does this dollar translate into something more than, less than, or equal to one dollar in terms of security? These questions remain unsolved as security does not have (monetary) proxies.
This chapter is about the main economic approaches applied to the defence sector. By doing so, I follow closely the Intrilligator’s definition (1990, p. 3):
Defence economics is concerned with that part of the overall economy involving defence-related issues, including the level of defence spending, both in total and as a fraction of the overall economy; the impact of defence expenditure, both domestically for output and employment and internationally for impacts on other nations; the reasons for the existence and size of the defence sector; the relation of defence spending to technological change; and the implications of defence spend- ing and the defence sector for international stability and instability.
As Smith (2009, p. 2) points out “Defence and peace economics, like much of the rest of economics, uses a mathematical and statistical language that makes it incompre- hensive to many who might be interested but do not have a quantitative training”. This chapter is written in a manner so that it can be fully comprehended by all researchers, whatever their level of competence in mathematics.
Economic reasoning is well established in, at least, what is called “mainstream eco- nomics”: markets provide a useful framework to investigate economic behaviour because this is the best way to achieve “optimality”, except in certain cases, such as externality, asymmetry of information, or public goods. Economic modelling relies on theoretical models that use the concept of markets; then, the model is confronted with data. One issue is to know what should come first between data and model. Data are not neutral per se and reflect theoretical considerations. This point is worth remem- bering as many economists do not wonder how data are built. With these remarks in mind, I first discuss economic modelling applied to the defence sector and, second, data related to defence economics.
The chapter is organized as follows. The first section provides an insight on “how much is enough”, which, as already discussed, is the key issue for economists. The second section presents the main theoretical approaches and the third section the main empirical analysis. As the scope of economics is vast, a selection of the most investi- gated topics in defence economics are presented in the fourth section. The final section concludes this chapter with a discussion of the future of defence economics.
The main issue: how much is enough?
A lot of actions can be analysed through economic thinking. The decision to do something is related to its costs and benefits. As long as the costs are smaller than the benefits, the action is called profitable. Difficulties arise when the researcher must con- sider all the direct and indirect costs and benefits and translate them into a single measure, such as a monetary one.
Markets are among one of the most useful concepts used by economists. Markets help in determining both the supply and demand for a single good. The interaction of supply and demand may lead to an equilibrium, with an “optimal” quantity of the good supplied and bought at a certain price. Markets allow economists to measure the monetary relationships between two agents. Defence industry and labour forces con- stitute the supply side of the market as they provide inputs for defence. Governments represent the demand side of the market as they provide security.
Economists evaluate complex consequences. These consequences can be either posi- tive or negative and one has to take into account all these elements in an economic evaluation. Some consequences are easier to capture than others. In the case of defence, the consequences are mostly non-economic. In particular, the primary aim in defence is to ensure security. However, the links between defence, which can be measured from a macroeconomic perspective with a defence budget, and security cannot be measured precisely. Following Smith (2009), defence activities rely on inputs, such as soldiers, equipment, experience, or trainings. Defence inputs2 are “bought” by governments by utilizing their defence budget. These inputs are known, but the defence output is unknown. One cannot measure, in economic terms, defence capabilities except by estimating the inputs required for creating the said capability. Security can, thus, be considered the sum of all the capabilities needed, given a defence policy that is suppo- sedly representative of citizens’ preferences. From this point of view, security is the sum of goods and services provided by the “public planner” expressed in monetary terms.
In the case of defence, defence production remains unknown. For example, consider an increase of EUR 1 of the defence budget in France. What are the con- sequences for defence output? Are the inhabitants of France safer by EUR 1? For economists, there is no simple relationship between the amount of money spent on defence and security gained. If security is accurately provided, one empirical possi- bility is to measure the economic gain by comparing it to a hypothetical situation of conflict. This is difficult, given the methodological assumptions required to compute a situation that does not exist.
If security is not measurable, one may investigate the benefits of defence. According to Hartley (2012), the benefits of security are both economic and non-economic. Defence economics is mainly interested in economic benefits of defence, such as the defence sector’s impact. Non-economic benefits are difficult to measure and, therefore, rarely evaluated.
The problem of measuring the benefits has a straightforward, but rather unsatisfy- ing solution. Economists consider output to be equal to the sum of inputs. This solution is also applied to public spending in other sectors, particularly health and education; in these sectors, there is a “private” counterpart, whose effectiveness can be computed and compared with that of public spending. In defence, there are few private counterparts; these are mainly private military companies that are relevant for specific defence missions.
Finally, note that the problem implies flaws not only in macroeconomics, but also in microeconomics. On the macroeconomic side, the key issue is to investigate whether the size of the defence sector is appropriate, with respect to both budgetary and strategic constraints. On the microeconomic side, one major problem is to identify defence firms, which is quite difficult in the case of dual-use firms. More specifically, their inputs are potentially developed for military products, civil products, or both.
Building theoretical models
Economics relies on theoretical models to simplify social interactions between eco- nomic actors. The main goal of the models is to disentangle the main economic chan- nels of interaction between these actors and, then, evaluate the best public policy, if required. Thus, economists have to identify the actors, how they are involved with each other, and the result of their interactions. By comparing the model’s equilibrium with an optimal situation, one may gauge the pertinence of public interventions.
Markets are among the main formal approaches to discuss economic interactions and the way to reach an equilibrium. Markets aggregate both supply and demand and the interactions between generate an equilibrium of both price and quantity. If this equilibrium has undesirable properties, such as idle capacity, public policy intervention is needed.
Even if the defence sector is quite special, markets offer an interesting perspective for both macroeconomics and microeconomics. For instance, the demand for defence arises from government and is measured by DS; the supply side of defence is composed of all the sectors that provide inputs.
This section discusses how defence economists analyse the defence sector in terms of theoretical models. I assume that markets are useful in synthesizing this literature because they offer the possibility of discussing some general economic features. The concept of markets can be “aggregated” to analyse both the microeconomic and mac- roeconomic aspects.
Microeconomics
Microeconomics is the branch of economics that studies the interactions of indivi- duals and firms within markets by choosing the optimal composition of scare resources that maximize utility (individuals) or profit (firms). It is widely accepted among economists that optimality and desirable outcomes may be reached through markets, but sometimes market failures arise, and then, public authorities must intervene to change individual behaviour.
Conceptually, individual behaviours are determined by a maximization problem. An economic agent seeks to maximize its utility or profit under budget or costs constraints. Optimal behaviour is determined thanks to this problem and leads to a situation where marginal cost is equal to marginal benefit.
Several assumptions (“perfect competition”) are made, such as symmetry of infor- mation or multiplicity of actors (on both demand and supply sides), so that they do not influence the optimal price. Within this framework, markets are the best way to achieve optimal outcomes. In this context, prices reflect incentives. However, there are several cases of market failure, such as externalities, public goods or asymmetry of informa- tion, that require public intervention.
DS is considered a public good. Because of its features of non-rivalry and non- excludability, this is a special type of good in economics. Non-rivalry implies that the consumption of this good by an economic agent is not at the expense of the consumption of the same good by some other economic agent. The property of non- excludability indicates that the consumption of this good does not require the consumer to pay for it (or no one can be excluded from its consumption). Rivalry and excludability define private goods. Common goods are a mixture of rivalry and non- excludability and club goods a mixture of non-rivalry and excludability.
Public goods cannot be provided by private players as they have no incentive to enter the market; given both non-excludability and non-rivalry, a firm is not able to make a profit. Public interventions are, then, required because governments may levy taxes to finance public goods. However, the features of public goods may also imply free riding, so that some countries in an alliance have an incentive to not provide the optimal level of DS.
The concept of public good implies that defence is provided collectively. This may involve the defence being provided directly by the public sector, by the private sector or by a mix of the two (Macdonald 2010). Defence remains a “final” public good, but one that requires a sum of private goods (e.g. military equipment for which there is both rivalry and exclusion) and human capital. From a practical point of view, it is a ques- tion of determining for the Ministry of Defense how much should be achieved “in- house” and which missions can be delegated to private sector companies (outsourcing). Traditionally, the private sector provides equipment, but since the end of the Cold War, many services have been carried out by external suppliers (i.e. clothing, catering). As Bellais et al. (2014) point out, the US and the UK practice outsourcing with a mixed record. Finally, some companies known as “private military companies” participate in defence missions, either alongside or replacing the regular forces. Singer (2001) recalls that they are used for operational activities, consulting (strategy and training) or operational and logistical support. The rise of these companies has led to a blurring of the line between regal and private activities (Markusen 2003) and hence justifies the need to regulate them.
A microeconomics-based approach is useful to analyse markets related to the provi- sion of equipment or labour. In these markets, one has to identify the economic agents. In the case of equipment, a government is the buyer and the defence firms are the sellers. As explained by Sandler and Hartley (1995), this situation leads to a “mono- psony” where there is only one buyer, but several sellers. Moreover, economics gen- erally seeks to identify the best alternative from a set of different ones. In the case of defence, in many situations, there is only one choice. As argued by Hartley (2012), lack of incentives is likely to lead to inefficiencies.
Microeconomics theory fails to fully encompass security issues related to defence. Gov- ernments’ goals are linked to not only the economy, but also strategic preferences. For instance, the equipment market in Europe exhibits low concentration, with national cham- pions enjoying monopoly in their own country, but harsh competition in other markets. Economic theory indicates that this situation would require concentration to reduce pro- duction costs through economies of scale. As discussed by Belin et al. (2017), this situation is not likely to occur, given countries’ preferences in terms of equipment. For example, strate- gic autonomy is the French’s defence policy pillar; industrial nations, such as Germany, Italy, and the UK, use the defence sector as a way to do industrial policy, whereas other countries prefer American supplies. Moreover, in terms of use of equipment, individual choices are not identical, as shown by the multiple versions of the aircraft A400M.
Macroeconomics
As stressed in the introduction, the size of the defence sector is significant in terms of macroeconomic measures (GDP, public spending, and trade, to name a few), but some countries bear a greater “burden” than others. For example, in 2018, Oman spent 8.2% of its GDP on defence; this accounted for 19% of its public spending. Thus, it seems reasonable to question the influence of DS on the economy.
From a macroeconomic point of view, economists evaluate all the channels through which defence affects the global economy. Obviously, there are many channels – some are direct (such as the multiplier), whereas others are indirect ones. Summing up all the effects of the different channels may provide a measure of defence’s influence. However, quantifying all the channels is difficult because it requires a vast amount of informa- tion. As a consequence, the economic literature focuses mainly on one channel. This subsection is based on the surveys by Dunne et al. (2005) and Malizard (2011). Three main channels have been identified.
The first channel is the “demand channel.” It mainly relates to Keynesian theory. The basic idea is that an increase of DS implies an increase in GDP by a number called the multiplier. The existence and the size of this multiplier has been widely discussed by economists since the 2008 economic crisis; the key issue is to know whether the multiplier is below, equal to, or above 1. As part of public spending, DS may play a role. There is also a negative indirect impact to DS. As discussed by Smith (1980), DS and private investment compete for a fixed share of the savings, so that an increase in DS leads to less resources for investment because of the increase in the interest rate. This situation is called crowding- out and may reduce the final effect of DS from the demand side.
The second channel is the “supply channel”. Rather than focusing on the quantity of DS, as in the demand channel, this channel analyses the quality of DS: does DS increase the quality of inputs? As discussed by Ruttan (2006), American DS led to several technological breakthroughs that have been reused in a civilian context. Others, such as Schumpeter (1934), consider that DS is unproductive. The final impact, then, depends on the quality of the final expense: defence R&D expenditure is more likely to generate a positive influence than defence compensation. Ram (1993) discusses at length the positive and negative supply-side impacts of DS and, in particular, the debate on technological spillovers and inefficiency of defence industries.
The last channel is called the “security channel” and relies mainly on the primary purpose of defence: ensuring security. Two opposite effects have been identified. In the first, a country may gain greater security when DS is increased because rule of law, especially property laws, is enforced. The second one is related to arms race because an increase in a neighbour’s DS can be perceived as a threat to national security that requires an increase in DS by the country under threat; consequently, the level of DS can be above the optimal level, leading to major inefficiencies. The final effect is the sum of these two effects, but given the absence of data for measuring security, this issue remains unresolved.
The way these channels are modelled is crucial. Focusing on a specific channel may imply an incomprehensive approach. Depending on the preferences of the researcher, this can lead to biased results. In this vein, Malizard (2011) shows that within the inconclusive macroeconomic literature, regularities in terms of results arise when papers are classified according to the underlying theoretical model. Modelling choices are not neutral.
For the purpose of interpretation, economic models tend to distinguish between short- and long-run effects. The former is mainly related to the demand-side channel, whereas the supply-side channel witnesses the latter. The basic idea behind this inter- pretation is the following: a DS shock may affect the economy in the short run because of extra public spending flowing into the economy – this is particularly useful when unemployment prevails. In the long run, the effect is more likely to arise because of changes in productivity or technological spillovers.
Empirical issues
Having described the theoretical models in the previous section, the next step is to check whether the conclusions of these models are in line with data. Since the end of World War II, national accounts have gained credibility and data are available to the scientific community. Notably, this allows economists to publish more papers based on empirical analysis (Angrist et al. 2017) and provide more insightful recommendations on public policy. Moreover, the development of econometric theory provides reliable tools to identify the causal relationship between two variables.
However, in defence studies, there still exist major gaps in measuring defence activ- ities. For instance, there is no “defence sector” in national accounts. As mentioned in the first section, defence production is not measurable. Because the defence sector is characterized by secrecy, many of the measures used do not provide an accurate econometric analysis. Data exist but are subject to criticism. This section is about how data are used by defence economists despite the various limitations.
Two types of data are available. From a macroeconomic point of view, the main figures are defence budget and arms trade. From a microeconomic point of view, the key issue is to determine the turnover associated with defence activities in a company.
This section first discusses some relevant sources. Then, I briefly present the main problems associated with these data: perimeter, accuracy and econometric issues due to data limitations.
Data
Economists enjoy providing and using economic data. This is a language which also requires strong academic backgrounds to be comfortable with. However, there is little discussion among them on the appropriateness of these data because most papers are “technical”, rather than being theory oriented. As stressed by Smith (1998, p. 423), “many articles give the impression that the authors have applied the statistical procedures without looking at the data”. In this subsection, I review some main data sources.
Macroeconomic data
Quantitative measures of the defence sector rely mainly on defence budget or the size of the army, both in terms of personnel and equipment, and provide researchers the opportunity to discuss defence policy. Two main sources are worth mentioning. The first, SIPRI, has gained much of its visibility because of its long-lasting evaluation of defence budget, as well as that of arms transfers and defence industry. The Interna- tional Institute for Strategic Studies (IISS) provides useful information with respect to the size of armed forces and defence budgets. Other sources, such as data published by national authorities in charge of DS, can also be mentioned.
Defining a defence budget, at least theoretically, seems easy, but there are numerous practical difficulties. One definition is commonly used by international organizations and is based on the NATO definition that includes operating costs (personnel and pensions), procurement, construction, and R&D. Statistics from the Ministry of Defense and other ministries can be used, as long as their budgets are aligned with those of military forces. Even if the definition appears clear, many problems arise when it comes to evaluating the budget, such as inclusion of paramilitary forces, direct funding of the defence industry through other public expenditures, peacekeeping operations, and so on. Malizard and Richter (2018) discuss the different sources of these problems in France and the UK and show major gaps between them that ulti- mately lead to different empirical estimates. It appears that, for France, DS is over- estimated by SIPRI, but the NATO figures suffer from major breaks.
One major drawback of DS is that it is an imperfect measure of power and security. DS does not reflect the military capabilities of one country and the capabilities are subject to depreciation, which is not considered in official publications. The size of the armed forces is, then, a complement of DS because it provides useful information regarding the ratio of troops to equipment and, ultimately, military capacity.
Trade data are used to gauge the intensity of the arms trade between two nations. This kind of data requires information regarding the quantity of equipment (including all the intangibles, such as training, technological offsets, etc.), the price, whether the equipment is new or refurbished, and the time between order and delivery. It is crucial to get a single measure that encompasses all the production costs from different coun- tries, with different production systems, as was the case in USA and USSR during the Cold War.
SIPRI is the international standard in arms trade data and serves as a primary source for the World Bank database. The database is rich enough to analyse trade flows between countries since 1950. SIPRI develops its own measures, such as trend indicator value (TIV), to compute trade. TIV does not measure the monetary value of the arms trade; rather, it relies on production costs for comparable features of specific arms. Other data sources, such as Arms Control and Disarmament Agency (ACDA) or national data, are not used in the literature given their lack of transparency and com- parability (Malizard 2018).
Microeconomic data
It is worth remembering that DS has a microeconomic counterpart because public spend- ing generates economic activities for firms or workers, both in the private and public sec- tors. In the case of defence, microeconomic data rely mainly, on defence firms.
Given its production volume and turnover, the defence industry is subject to numer- ous debates. These potential controversies require the most transparent and reliable data to perform an accurate empirical analysis. Two mains sources regarding the defence industry have been used in the economic literature. The first one is provided by SIPRI, which cover the top 100 defence firms, and discusses some parameters related to their performances; these include the turnover, the share of turnover because of military activities (which is, in a sense, one measure of the duality of a firm), and the number of employees. This database is easily downloadable from the SIPRI website and covers the period 2002–2017; older data are available in print in the form of SIPRI Yearbooks. The publications from Defense News are another source.
Although these data may be useful for quantitative analysis, they suffer from some limitations. First, the sample may be limited. Often, the time period covered is quite short and does not include the period of transition following the end of the Cold War that shaped the defence industry in terms of its concentration (Bellais et al. 2014). Besides, the ranking only includes 100 firms and, thus, neglects smaller firms, which could be potentially crucial for procurement. Second, the activities of firms are not decomposed into domestic turnover and external turnover; this, in turn, complicates the firm-level analysis of arms trade. Finally, for dual-product firms, the number of employees, R&D, and capital (i.e. the inputs) are not separated into civil and military outputs. This is a frustrating issue as economists cannot discuss the performance of firms by comparing inputs and outputs. One solution is to assume that the share of inputs is equal to the share of outputs. This solution is simple to implement but does not fully capture the process of production, especially the potential cross dependencies between civilian and military productions.
Problem with data
For empirical analysis, it is crucial to measure precisely the same economic phenom- enon. Thus, cross-countries studies require a single measure of defence activities to avoid any bias of over- or under-estimation. Countries do not have the same inter- pretation of DS and, as already stressed earlier, international institutions such as SIPRI may lead to a bias. Moreover, for a single country, one may observe a break in the definition; for example, France’s 2009 exclusion of its paramilitary forces (gen- darmerie) from the defence category has not yet been corrected by NATO in its official statistics.
Different sources are used to compute DS. SIPRI lists three categories. Primary sources consist of budgetary documents provided by the government. Secondary sour- ces are quotes from primary sources, such as NATO or IMF; other secondary sources are less reliable. According to Perlo-Freeman and Ferguson (2015), primary sources have increased since 1988 and constitute almost 80% of data in the most recent period; this gives confidence in the reliability of data. However, the authors warn the recent increasing trend of absence of data for numerous countries.
Cross-country comparisons are also complicated due to the purchasing parity conversion. A dollar bill may have different purchasing power in each country and standard market exchange rate does not adequately address this problem. One solu- tion is to consider the purchasing power parity (PPP) exchange rate. As noted by Smith (2009, p. 91), the difference on converting Chinese DS from the market exchange rate to the PPP exchange rate is huge – the value of the latter is almost four times that of the former.
The last difficulty with data is comparison over time. Macroeconomic data are sub- ject to major quantity and price changes over time. Thus, it is necessary to control for the change in price, captured by the GDP deflator, to give real values of the measured economic variable. In the case of defence, there is no “military deflator” and the common solution consists of using GDP deflator as a proxy for the military deflator. This solution is unsatisfactory because defence inflation appears to be higher than that indicated by the GDP deflator (Hartley 2016).
Opportunity costs are widely used by economists to discuss all available alternatives in making an economic decision. In the case of defence, one major issue is the impos- sibility to quantify all the alternatives: we observe DS or wars but there is no real situation in which there is no DS or the absence of conflict,3 even if one can calculate counterfactuals to compare the alternatives.
Transparency is a key issue in DS. Many data are subject to classification because they are related to national security issues. The economic literature uses open source data when they exist; since the beginning of the 2010s, SIPRI does not provide any information on many Middle Eastern countries. Developing countries may either lack statistical institutions or, given the nature of their political regimes, lack the incentives to provide an accurate measure.
Trade data also suffer from severe limitations. Even if a large number of countries ratify the arms trade treaty, it seems that data still lack transparency. According to Malizard (2018), SIPRI data are a kind of “black box” offering few insights on the way data are computed. Further, because SIPRI uses TIV measures, rather than monetary ones, data are not comparable.
Econometrics is a useful tool as it relies on statistical routines to evaluate the sig- nificance of the relationship between different variables that are based on theoretical models. The development of new econometric approaches allows the researcher to apply more effective routines by improving the precision of the estimates. Given the power of the econometric methods, one can distinguish between correlation and caus- ality, except in a case of theoretical misspecification.
Two types of econometric problems may arise: the consequences of measurement error due to data limitations and reverse causality. According to Smith (1998), empiri- cal studies are more interested in “using cooking tricks” instead of amending the model to fit data. These tricks potentially lead researchers to use the best empirical results in terms of statistical significance given the potential publication bias with “star wars”,4 as stressed by Brodeur et al. (2016).
The data limitations that have been discussed above may have potentially huge consequences on econometric results. Greene (2003) shows that the estimates may suffer from a severe bias. There is no good solution to avoid this problem but check- ing the collected database and investigating sources to ensure that there are no breaks, is one of the obvious steps in any empirical analysis. Malizard and Richter (2018) indicate that different measures of DS lead to different conclusions about its economic impact.
Econometric analysis allows the researcher to investigate the causal influence in a given relationship by distinguishing endogenous (dependent) and exogenous (indepen- dent) variables. For instance, growth theory indicates that GDP is “explained” by investment and DS; however, it is as likely that GDP explains investment or DS, so that the variables considered exogenous variables prove to be endogenous. This is called “reverse causality”. This problem is very serious in empirical economics and requires specific remedies. One of them is to consider whether the system formed by all the variables is endogenous (each variable is explained by all the others), but this solution needs many observations. Instrumental variables have been also used by econome- tricians; this involves a two-stage model in which one has to find an “instrument” that is related to exogenous variables, but not to the endogenous ones. In practice, finding a suitable instrument is difficult.
The defence economic literature, a critical survey
The defence economics literature embraces many different subjects incorporating economics tools relevant to the defence sector. I decided to focus on subjects that have been widely examined by numerous authors and correspond with the central features explained above: how do we deal with security issues without taking them accurately into account?
The outline of this section is the following. First, I discuss the literature on alliance with a specific focus on burden sharing. Second, I analyse arms trading and compare the causes and consequences. Third, I review the main results of the literature on the defence industry and R&D. Finally, I assess the economic impact of DS.
Alliance and burden sharing
Defence spending is considered a public good. Given its nature, public economic theory emphasizes that the government is the only agent that has the ability to pay for defence. The share of national wealth that is allocated to defence is subject to numer- ous factors (Smith 1989). One such factor is alliance membership.
Olson and Zeckhauser (1966) offered the first formal analysis of alliance using economic theory. The authors discussed the production of security from NATO and concluded that it is a pure public good. This good is produced at the expense of richer economies that bear a disproportionate share of the burden while smaller economics have a tendency to free-ride. Empirically, the authors show a significant correlation between the share of DS to GDP and gross national product (GNP) for each ally. This result is called the exploitation hypothesis.
However, after 1970, NATO changed its doctrine, and more recent contributions do not support the conclusion of Olson and Zeckhauser (see, for instance, the survey from Sand- ler and Hartley 2001). The change in doctrine led to different benefits for alliance members (deterrence, but also more local defence activities). Therefore, the costs of membership alliance are more aligned with the benefits, and the exploitation hypothesis no longer holds. For many scholars, the key issue now is to evaluate the degree of publicness of international security provided by an alliance (see Gates and Terasawa 2003).
From an empirical point of view, many researchers have addressed burden sharing within NATO. For instance, Khanna and Sandler (2002) computed a benefit function that includes GDP, population, and area and compare it with cost (which is simply the relative share of an ally’s DS to the sum of all individual defence budgets). Until recently, these papers showed that benefits and costs are in line so that the exploitation hypothesis is rejected. However, a recent paper by Shimuzu and Sandler (2012) indi- cated that, since 2004, NATO has exhibited divergence between costs and benefits, and the authors conclude that there is a risk that NATO could become a two-tier alliance. The costs-benefits approach is simple and flexible enough to be extended to other alli- ances such as the EU (Kollias 2008).
Nonetheless, this approach suffers many drawbacks. Costs are mainly proxied by defence budgets, which do not have qualitative effects: France spends less than 2% of its GDP on defence, but its military capabilities are probably greater than Estonia, which respects the 2% criteria. Benefits are also subjective and subject to criticism: they mainly include what a country wants to protect (its population, its wealth, and its area), but do not reflect any preference in terms of defence policy such as interventions in military operations or contributions to international organizations.
Trade
Arms trade is controversial among economists; there is an obvious ethical concern given the nature of arms. Arms trade is related to the industrial organization of a country’s defence sector but also to attitudes toward international security and inter- national treaties. A critical question regarding arms trade is the following: what are the drivers that explain the export and import of arms? It is important to distinguish between economic and security factors. Another subject of debate among economists and social scientists, is related to the use of arms and whether arms trade has a stabi- lizing effect (i.e. deterring aggression) or a destabilizing effect (i.e. fuelling conflict).
Initially, it is appealing to analyse global arms trade as a market. One feature of arms exports relies on its extreme concentration. Only a few countries export arms: according to the SIPRI database, the top 10 countries exported 96% of global arms during the Cold War and 90% after the Cold War. The United States and the Union of Soviet Socialist Republics (USSR) (later, Russia) are the market leaders followed by some European countries. Emerging countries are still marginal (China ranked sixth after the Cold War), but their influence is growing. Turning to the demand side, arms imports are less concentrated: during the Cold War, the top 10 countries accounted for less than a third of arms imports and, after the Cold War, the top 10 countries’ share was close to 50%. Given these statistics, standard economic theory indicates that the supply side should have substantial market power. However, economic factors play a minimal role in the explanation of the global arms trade while political and strategic factors emerge as crucial factors.
The analysis of arms trade as a standard market has been a focus of researchers. Anderton (1995) summarized this literature, which is mainly theoretical. As emphasized by Levine et al. (1994), two opposite drivers may lead to arms exports: economic profit and external security. Arms exports provide insecurity for the seller when the buyer is a rival or security when the buyer is a friend. Arms exports show that depending on the quality of the buyer, the level of exchange is different from a pure economic model. These results explain some features of the global arms trade, but standard economic theory does not fully capture the frictions arising from the peculiarities of arms.
Following new political economic theory, some researchers analyse arms trade using political factors such as proxy of democracy, political orientation, or security factors such as alliances, conflict, and neighbours engaged in conflict.
Some papers evaluate the decision to export arms (proxied by a dummy that takes the value of one if one export is observed or zero otherwise) and the quantity of exports. From an econometric point of view, this type of model is known as the Heckman model. Blanton (2005) provided an example of this model and showed that in the selection stage, democracy is a key positive determinant of US exports; that is, the more democratic the country, the more likely the United States is to export arms to that country. Comola (2012) insisted on the importance of political factors as an explanatory variable for arms exports. More recently, Martinez-Zarzoro and Johannsen (2019) show that alongside economic factors, both political and security factors play a significant role based on a sample of 104 exporting countries over the period 1950 to 2007. The authors also note major shifts due to the end of the Cold War. These papers offer interesting insights on arms exports, but they fail to circumvent endogeneity: the papers use conflicts as expla- natory variables, but they do control for the fact that trade may have consequences regarding conflicts. This type of circularity could potentially cause reverse causality.
Arms trade generates effects beyond commercial operations because of the nature of arms, their effects in terms of security, and their influence on economic structures. As acknowledged by SIPRI (1971, p. 73), “perhaps the most important question about arms supplies is … what effect they have on the development of wars – on the like- lihood of wars breaking out, on the course of wars and their general severity”. In this vein, several papers cast doubt on the destabilization hypothesis as they do not conclude any significant impacts concerning arms trade on conflicts (Moore 2012). Fauconnet et al. (2019) indicate that French arms trade does not exhibit any significant role on intrastate conflicts, whereas arms trade in the rest of the world fuels conflicts. Unfortunately, these papers cannot address the endogeneity issue and may imply reverse causality.
Industry and R&D
Among all the studies on defence economics, those that discuss the defence industry are most closely related to microeconomic theory and, in particular, industrial orga- nization. Standard economic theory provides a useful framework within which to investigate the defence industry, but the specificities of the defence sector such as sovereignty technology, the importance of fixed costs, and barriers to entry, among others, must be considered. These features imply that the equipment market is somewhat unique: there are only a few firms and one buyer (the government), and these entities form a monopsony. In some cases, the context is called a bilateral monopoly if there is only one firm.
Defence industries represent the “supply side of the market for defense equipment” (Sandler and Hartley 1995, p. 177). All firms included in the defence industries define the defence industrial base (DIB). Defining the DIB requires a perimeter which is, unfortunately, difficult to operationalize. For instance, should housing services be included in the perimeter? Dunne (1995, p. 402) had a clear definition by distinguishing three types of products: lethal large or small weapons systems, non-lethal strategic products (fuel), and other products consumed by the military (food). Reliable DIB perimeters are crucial for public policy as they may shed light on the efficiency of a country in providing arms. For example, should a country rely on its own capacity? The notions of self-sufficiency and security of supply are crucial determinants in con- structing a DIB because arms can be produced domestically or through international trade with partners. In this vein, the procurement choices of the Ministry of Defence are multiple and correspond to a trade-off between national sovereignty, cost of equipment and existence of industrial capacity. Not all countries have the same strate- gic ambitions and needs, so acquisition policies are diverse (Hartley and Belin 2019).
As the survey by Hartley (2007) noted, the literature is mainly composed of papers on R&D issues such as defence financing and its impact on the economy, procurement, and industrial cooperation. This subsection briefly follows this outline.
Many Western armed forces rely on technological superiority for their missions. Consequently, defence equipment is considered highly advanced in terms of complexity (Mowery 2010). The innovation concept is complex and widely analysed through the lens of R&D (a measure of inputs) or patents (a measure of outputs). The means of innovation are crucial, and some papers follow their evolution (Mowery 2010): the defence sector absorbed a large share of budgetary and financial means during the Cold War, but these shares declined after 1990. Nonetheless, for many Western countries, the defence sector is at the core of their national system of innovation given the architectural knowledge; that is, the way that knowledge is applied to optimize production by assembling parts from different subcontractors.
Relative to innovation output, patents are used by economists, and there is a need to control the quality (rather than the quantity) of innovation. Patents are identified in technological classes, but defence is not one of them. Therefore, the identification of defence patents is complicated and requires industrial expertise. Some indicators mea- sure the influence of a patent, and papers show that the economic impact is significant due to their effect on productivity (Moretti et al. 2017).
Data measurement is the crucial issue for this subfield: patent behaviour is suppo- sedly the same among civilian and military sectors, which is not the case given the secrecy associated with the military sector. For example, Acosta et al. (2017) showed that defence firms patent less than civil firms. Moreover, the statistical approaches used by economists acknowledge that a firm with 1% of its turnover accounted for by the military sector is a firm of the defence industrial base. There is obviously a limit because given the different defence submarkets and their industrial organization, the gradient must range between 0% and 100% (and cannot be a binary measure).
The procurement issue is crucial because it allows us to analyse the industrial choices of states in terms of armaments. Given the importance of R&D, the price of weapons systems is rising faster than civilian inflation (Hartley 2016). From this point of view, it is necessary to arbitrate between the cost over the entire life cycle (procurement, maintenance, dismantling) and the need to master technologies crucial to sovereignty (Rogerson 1995). Contractual mechanisms (fixed-price contracts or cost-plus contracts) are used to assess the risk sharing inherent in the production process (Hartley 2007). In order to avoid cost overruns, fixed-price contracts are the standard choice in many developed countries (Bellais et al. 2014).
Industrial cooperation is a popular subject in Europe. There are many firms and programmes, and duplication is well-established (Hartley 1995) compared to the inte- grated American equipment market. Economic theory shows that cooperation is eco- nomically optimal given the costs savings (Sandler and Hartley 1995). However, as reviewed by Hartley and Braddon (2014), both the demand and supply sides generate inefficiencies, particularly the complexities that cause partners to collaborate. These inefficiencies are likely to increase costs and delays.
Economic impacts
A main subject in the defence economics literature is the economic impact of military activities. As DS is a component of public spending, many papers address the influence of DS on macroeconomic performance such as GDP, unemployment, or investment. However, as defence activities are also localized in a few areas (for historical or geographical reasons), another arm of the literature examines the impact of DS on regional activities. From this huge literature body, little consensus emerges on the macroeconomic effect of DS. On average, based on a sample of estimates in the litera- ture, Yesilyurt and Yesilyurt (2019) indicated that DS has no significant effect on growth. DS is not a crucial determinant of growth (Sala-i-Martin et al. 2004), and its effect is minimal compared to other factors such investment.
There are two explanations for the impact of DS. The first is related to Keynesian theory: an increase (or decrease) in DS positively (or negatively) affects economic activities; a multiplier effect. The second reason is linked to the quality of defence inputs: defence activities are widely technological, and the technologies conceived and used for the military generate spillovers to the civilian sector. Ruttan (2006) provided many examples of such military technologies: nuclear, semi-conductor etc.
Direct economic impacts have received much attention from economists. This is because the impacts are easier to assess from an econometric point of view. The sim- plest way to model a direct relationship is to use only two variables, DS and GDP, in level form or defence burden and growth rate of GPD in a relative form. This approach has been widely used because of a lack of global theory encompassing all the channels through which defence affects the economy. This type of model is close to the one chosen by Benoit (1972) in his seminal publication. Another advantage is the fact that the endogeneity issue can easily be controlled using proper econometric tools. These atheoretical approaches lead to interesting results, notably, the existence of a multiplier effect. However, there are several significant limitations. The approach neglects oppor- tunity costs: What would have been the effect if public spending had been included? Finally, as mentioned by or Dunne and Smith (2010), the results are sensitive to the econometric specification.
Many economists use a theoretical model given the limits of the atheoretical approach. Growth theory provides a useful framework to investigate the influence of public spending, and the theory has been popularized among defence economists for its solid foundations (Dunne et al. 2005). Many empirical analyses focus on both defence and non-defence spending and compare their respective effect on GDP growth among other determinants such as private investment or population growth. This approach tends to show a negative impact of DS.
Some indirect models have been used. These models evaluate the existence of a drain of resources that, ultimately, reduce economic performance. Smith (1980) popularized a model where the crowding-out effect is estimated considering that private investment and public spending compete for the same pool of resources. An increase in DS implies less resources for private investment and a higher interest rate. DS has a strong nega- tive impact on private investment, which is the key determinant for economic growth (Sala-i-Martin 1997). However, DS composition matters as shown by Malizard (2015) French DS implies crowding-out, but French defence equipment spending fosters pri- vate investment.
Finally, some models rely on multiple equations that encompass direct effect, indirect impact, and endogeneity of DS (Deger and Smith 1983). The system of equations is based on a growth equation to quantify the direct effect, an investment equation to model the indirect effect, and a DS equation to circumvent endogeneity. Many papers show that DS has a positive direct impact that is surpassed by a negative indirect impact leading to a global negative impact.
As explained by Malizard (2011), some regularities emerge from the global picture of the results. Some models are well-designed to exhibit a positive effect of DS, whereas others lead to the opposite conclusion. A comparison of models may lead to proper evaluations relative to DS impact. Additionally, the defence economics literature is somewhat original in the global literature: except for growth estimates, the results are based on specific models with- out points of comparison with other macroeconomic papers.
To summarize the economics of DS, the primary purpose of defence is not to foster economic activity but to ensure security. Economists are unable to discuss the effect of security. If DS exhibits a positive effect, it could be a type of double dividend in a sense that both security and economic activities are stimulated. However, as Smith (2009) noted, a high macroeconomic impact cannot be expected given the relatively weak influence of DS in terms of its percentage of GDP.
Conclusion: What is the future of defence economics?
In their introduction to the second volume of the Handbook of Defense Economics, Hartley and Sandler (2007) acknowledged that defence economics has changed. From a semantics point of view, a prominent journal in the defence economics field changed its name from Defense Economics to Defense and Peace Economics. This change is a recognition that defence economics is interested in economic means but also strategic ends such as security, peace, war, and conflicts.
Peace and conflicts economics are not, by far, the core of defence economics from a historical point of view. Conflicts economics is mainly interested in the causes, consequences, and costs of all forms of violence (interstate conflicts, civil wars, and terrorism, among others). The modelling perspective is reversed: defence economics considers conflict as exogenous whereas conflict economics uses defence as exogen- ous. There is a kind of simultaneity between defence and conflict. However, the lit- erature is now intertwined as economic factors are crucial in the explanation of conflicts (Collier and Hoeffler 2007) and conflicts have huge consequences on economies (Abadie and Gardeazabal 2003). Besides, the concept of security web has been popularized by Dunne et al. (2003) by gathering data on both conflict and defence sources.
The crucial issue for defence economics is to measure output: security is a multidimensional concept that cannot be summarized in a single index. The absence of conflicts is one item that can be included as a means of defence. From a statistical point of view, a conflict is coded as 1 so that 0 has different meanings (Fauconnet et al. 2019): the durable absence of conflict or a peaceful period between two periods of conflict. Statistical measures must be cautiously evaluated as peace (i.e. 0) is the absence of conflict in this approach. However, these data are used by many economists and social scientists to discuss conflict but also development issues.
From an economic perspective, there is still a missing link: translating defence means into monetary measures. In my opinion, “How much is enough?” would be a question without answer. Therefore, defence economics is then a complement to other disciplines, notably strategy and international relations. However, it helps policy- makers to formulate better informed description and prescription when economics tools are relevant.
Notes
1 Smith (2009) discusses the notion of “military value chain” and shows how economics is ori- ginal among social sciences, especially because of the analysis of means in economic terms.
2 See the first subsection of our second part for a discussion on how governments source its national defence. Note that these inputs are private goods, despite the fact they are collectively procured.
3 Abadie and Gardeazabal (2003) investigate the costs of terrorism for the Basque region by
considering a counterfactual region in which there are no terrorist attacks.
4 Statistical significance is labelled in table thanks to “ * ”.
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