Democracies, Politics, and Arms Supply
Abstract
Throughout the 20th century arms have not only been tradable goods, but also policy instruments. This paper focuses on countries supplying major conventional weapons (MCW), and investigates whether changes in political conditions impact the quantity of MCW supplied to third countries. In particular, it concentrates on democratic exporters and estimates a gravity-type panel tobit for the years 1975–2004. Results suggest that the exporter's chief executive, being right-wing, has a positive and significant impact on MCW exports. This may reflect a general right-wing tendency to support national industry and deregulate heavy industry exports. It is also found that higher concentration of power is associated with lower MCW exports, and that executives which serve the last year of their term and can run for re-election tend to decrease MCW exports.
1. Introduction
The trade in arms has important implications, especially when it involves developing countries: since 1990, armed conflicts have cost Africa around US$ 300 billion, which is equivalent to international aid from major donors in the same period, and at least 95% of Africa's most commonly used conflict weapons come from outside the continent (International Action Network on Small Arms (IANSA), Oxfam, and Safeworld, 2007). However, even though during the past decades the public concern on arms trade has increased exponentially, as Anderton (1995) pointed out, the topic has not received equal attention by economists and political science scholars. The economic papers on arms trade are not very numerous, and most contributions are theoretical (Peleg, 1977; Levine and Smith, 1995, 1997, 2000; Baliga and Sjöström, 2008). The few empirical papers mostly relate to the demand side (Pearson, 1989; Kollias and Sirakoulis, 2002; Smith and Tasiran, 2005), with only two contributions focused on the supply (Blanton, 2000; Brauer, 2000). This paper simultaneously takes into account demand and supply side of the arms market to answer a question that relates to economics and politics: whether the internal political conditions in the exporting country influence the amount of arms supplied to third countries. To answer that, a gravity-type panel tobit is estimated for the years 1975–2004. Gravity equations have been extensively used in economics of trade (Bergstrand, 1985; Frankel and Romer, 1999; Egger, 2000; Glick and Rose, 2002; Anderson and Wincoop, 2003). The link between politics and trade has been studied from different angles (Summary, 1989), however to the best of the author's knowdlege the present paper is the first to focus on the relation between internal politics and arms trade for a panel of exporting countries.
Attention is restricted to major conventional weapons (henceforth MCW), a technologically advanced share of the arms production sector. MCW include aircraft, armored vehicles, artillery, radar systems, missiles, and ships; it does not include small arms.1 All through the 20th century the MCW industry has been highly concentrated: the 20 major MCW producers alone account for 97% of total worldwide exports for the period 1975–2004. Only five out of these 20 countries have ever experienced an autocratic regime. The core of this analysis focuses on the democracies, which account for more than 65% of total MCW exports for the period 1975–2004.
The trade in arms is not just business but also a policy issue involving strategic interests (Smith et al., 1985; Krause, 1991; Skons, 2000), and international relations during the Cold War have alimented this perception. The market for arms lacks an international regulation, being therefore subject to each country's sovereignty. Arms export licenses are exclusively granted by governmental agencies (mostly inter-ministerial committees) and can be revoked by them.2 Even if nowadays licenses for certain destinations are automatically granted, in virtually all exporting countries a relevant share of the arms industry is state property, and arms orders may be used to boost the employment of industrial regions (Martin et al., 1999).3 A well-documented case of public subsidies is the export credits granted by the UK Export Credit Governmental Department: Martin (1999) concluded that in the UK each job generated by arms export is subsidized by just under £2000 per annum and that a one-third reduction in UK defense exports would save the taxpayer £76 million per annum at 1995 prices. Given that arms trade is also a foreign policy issue and that governments control arms exports through different channels, the aim here is to test how internal politics affects arms export decisions. In section 4 evidence is provided that MCW export patterns of democratic and autocratic regimes differ, and then non-democratic producers are excluded from the sample in order to concentrate on the political characteristics of democracies only. In the empirical specification, the dependent variable is the amount of MCW transferred and the equation is estimated for years 1975–2004, that is, the core of Cold War and the years right after. The choice of a tobit model is consistent with the censored nature of data.
The results give original insights into the arms trade suggesting that, ceteris paribus, the government in power, being right-wing, significantly increases the quantity of MCW exported. This may reflect a general right-wing tendency to lower trade barriers with its consequences on exports deregulation, or a greater support toward the national armament sector in terms of subsidies (through partly/fully government-funded research) or offset agreements.4 It is also found that lower concentration (i.e. higher fractionalization) of power within the coalition in office is associated with higher MCW exports. This is in line with previous results on trade deregulation: several sources have pointed out that fractionalized democratic governments liberalize more easily (Frye and Mansfield, 2003; Fehrs, 2006; Belloc and Nicita, 2010), perhaps under threat of defection from a coalition member or to pacify the median voter, who presumably benefits from more open markets. Finally, data suggests that MCW trade varies during the electoral campaign, perhaps because of the scrutiny of public opinion: executives serving the last year of their current term tend to decrease MCW exports.
The rest of the paper is organized as follows: in section 2 the model is briefly described, while section 3 explains data and variables in use. In section 4 results are presented, and section 5 concludes.
2. The Model
(1)
is the unobserved latent variable, armsijt is the nonnegative observed outcome, γi, δj, and φt are fixed effects, and the covariates 
explain both the latent variable and the observed outcome. Fixed effects are a safe choice since γi, δj, and φt are likely to be correlated with the regressors. Moreover, they account by construction for time-invariant country characteristics and time trends.All 20 major exporters are first corsidered as ranked by the Stockholm International Peace Research Institute (SIPRI), which alone account, for 97% of total MCW exports for the period 1975–2004.5 These countries—in order of importance—are: the USA, the USSR, the UK, France, Russia, China, West Germany (FRG), Czechoslovakia, Italy, Unified Germany (GMY), Netherlands, Sweden, Canada, Poland, Israel, Spain, Ukraine, Switzerland, Brazil, Norway. Out of these 20 exporters, the non-democratic ones are later on excluded: the USSR, China, Czechoslovakia, Poland 1975–1988, and Brazil 1975–1984. The remaining democracies still account for more than 65% of total MCW exports for the period 1975–2004. On the importers side all independent countries recognized by the United Nations (UN) are included as potential importers, subject to data availability.6
A relevant issue to be discussed is the timing of the trade. Many categories of arms are grouped under the MCW label and procedures vary from country to country, however responses to political changes seem to be relatively fast.7 Even if the production of some arms can take up to a few years, licenses are required not for the negotiation of the contract but for the delivery.8 When licenses to deliver arms are granted, they expire in a reasonably short time (within one year for France and Italy). Moreover, licenses can be revoked by the governmental agency under a wide range of circumstances. For all those reasons the author sticks to the specification in equation (1) where the response is assumed to be immediate (i.e. within the year): if anything, this is a conservative choice underestimating the size of the total effect. Specifications with the lead dependent variable fit the data considerably worse than the model above (results available upon request).
3. Description of Data and Variables
This section illustrates the main features of the data and the variables in use. The regressors' subscripts refer to the dimensions of variation: i for exporter, j for importer, t for the year. The time span goes from 1975–2004 to 1975–2000, depending on the specification.
Data on MCW exports come from the Arms Transfers Database by the SIPRI. MCW consist of aircraft, armored vehicles, artillery, radar systems, missiles, and ships. SIPRI data register MCW transfers to sovereign countries (as well as international organizations, rebel groups, factions and non-governmental armed forces, which appear under a recipients' heading different from the country's central government). In order to be registered in the SIPRI dataset weapons must be transferred voluntarily by the supplier and must have a military purpose; time of transfer refers to the moment when delivery is registered. Units of arms are computed according to a trend indicator value system that reflects not economic prices but amounts transferred: the weapons are evaluated for their technical parameters, so that similar weapons have similar scores.9 This feature improves the quality of the information in several ways. First, in many cases no reliable data on the economic value of a transfer are available. Second, even if the value of a transfer is known, it is in almost every case the total value of a deal, which may include not only the weapons themselves but also other related items (e.g. spare parts, armament or ammunition, specialized vehicles, software changes to existing systems, or training). Third, even if the value of a transfer is known, important details about the financial arrangements of the transfer (e.g. credit/loan conditions and discounts) are usually not known. On the other side, the SIPRI trend indicator not only registers arms sales, but also other forms of supply including weapons transferred as political aid at a zero price. This trend measure is consistent with the focus of this study: since MCW are also policy instruments, price and market laws would just tell a part of the story.10 In all specifications that follow, the dependent variable armsijt is the SIPRI MCW flow from country i to country j at time t (1 unit corresponds to 1 SIPRI point). Only sovereign countries are taken into account, while other entities such as international organizations and non-governmental armed forces are omitted. The SIPRI data are also used to build the variable MCW exportsit, which is calculated as the total MCW flow out of the exporter country i at time t and is aimed to capture the country's internal fluctuations in the armament industry (1 unit corresponds to 1000 SIPRI points).
Data on democracy come from the Polity IV Project by the Center for Global Policy of George Mason University. The author uses the composite polity indicator that ranges from −10 (strongly autocratic regime) to +10 (strong democracy): democracyit is a dummy equal to one if the exporter's polity indicator is greater than zero. This dichotomous classification is adapted for the sake of simplicity, but it does not affect the results since the distribution is almost bimodal: in 96% of the cases where democracyit equals zero, the polity indicator is equal to or smaller than −4. Similarly, when democracyit equals one the polity indicator is equal to or greater than +6 in 96% of the cases, and equal to +10 in 75% of cases. In some specifications there is also a control for the importer's democracy scorejt, in the original scale from −10 to +10. The transition out of Cold War coincided with the so-called third wave of democratization (Huntington, 1991): between 1987 and 1997, 54 countries went through a process of full- or partial-democratization (Papaioannou and Siourounis, 2008). This is also reflected in the Polity IV data: on the total sample of 168 countries, the median polity score for period 1975–2004 is 0, while the median polity score for period 1990–2004 is 5.
Variables reflecting political conditions come from the World Bank Development Research Group's Database of Political Institutions, DPI2006 (Beck et al., 2001). This dataset classifies the chief executives in power as one of the followings, depending on their economic policy: Right (conservative, Christian democratic, or right-wing), Left (communist, socialist, social democratic, or left-wing), Center (for parties that are defined as centrist or when party position can best be described as centrist, e.g. party advocates strengthening private enterprise in a social-liberal context). Just to mention a few examples: all USSR executives are classified as left, while those regarding the US Carter (1977–1981) and Clinton (1993–2001) governments are classified as left, and the Regan (1981–1989), G. H. W. Bush (1989–1993), and G. W. Bush (2001–2009) governments as right. For the UK, Margaret Thatcher (1979–1990) is classified as right, while Tony Blair (1998–2007) as left. Italian leaders belonging to the Christian Democratic party (Democrazia Cristiana) are classified as centrist for the period 1975–1983 and 1988–1992. For the exporter country, two dummy variables are defined, centristit and leftit, accounting for center and left executives. The Right dummy is omitted, as the three categories are mutually exclusive. Those variables are coded as missing if the information is not available or not applicable. In some specification the dummy same orientationijt is used, which equals one if the chief executives in exporter and importer countries are both left-wing, both centrist, or both right-wing, and zero otherwise. This is to test whether the arms trade is affected by political friendship and strategic considerations, as documented by Alesina and Dollar (2000) for international aid. The variable concentrationit is also included, which expresses the concentration of power within the coalition in office. It is computed as the Herfindahl index of government seats, that is, the sum of the squared seats shares of all parties in the government. This index goes to zero whenever the government is composed of many small parties and equals one if there is a single party in the government. This variable is meant to test whether the concentration of power, which is a measure of (lack of) political competition, affects the arms trade policies of the political parties in office. Finally, the dummy end termit equals one if the the exporter's executive is serving the last year of the current term (without specific rule limiting immediate re-election). Other time-invariant country characteristics (such as whether the country has common vs civil law, or a parliamentary vs presidential system) are taken into account by the fixed effect.
Armed conflicts in the importing country may proxy for the MCW demand side. Data on conflicts come from the Armed Conflict Database provided by the International Peace Research Institute of Oslo (PRIO) and the Uppsala Conflict Data Program (UCDP). This dataset provides detailed information on the type and the severity of conflicts that took place between independent states and/or political factions from 1946 onwards. Conflicts are originally classified in four categories: interstate armed conflict (which occurs between two or more states), internal armed conflict (which occurs between the government of a state and one or more internal opposition groups without intervention from other states), internationalized internal armed conflict (which occurs between the government of a state and one or more internal opposition groups with intervention from other states) and extra-systemic armed conflict (which occurs between a state and a non-state group outside its own territory). Since more than 70% of the conflicts in the sample are internal armed conflicts, only one dummy (conflictjt) is included, which equals one if there is a conflict of any kind in act in the importing country.
Data for per capita gross domestic product (GDP) and population, which proxy for countries' supply and demand potentials, come from the Penn World Table Version 6.2 (2007) provided by the Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania. The variables pgdpit and pgdpjt refer to the countries' per capita GDP (expressed in thousands of US$), while popit and popjt refer to the countries' population (in millions of inhabitants).
In the international market for arms, a formal obstacle to trade is represented by international embargoes, which are relatively frequent and whose effectiveness is highly controversial. There are several types of embargo: international organizations such as the UN, the OECD or the EU impose mandatory or non-mandatory embargoes, and some countries also initiate unilateral export restrictions. Attention is restricted here to UN mandatory arms embargoes and information is retrieved by combining UN secretariat sources and the dataset on international arms embargoes provided by SIPRI: the dummy embargojt equals one if the importer country is under a UN mandatory arms embargo regime at time t.
Since geographical and cultural factors correlate with trade, the author controls for distanceij, which refers to the average distance between the two countries in thousands of kilometers (Gleditsch and Ward, 2001). Trade exchanges lead to a diplomatic familiarity and an economic interdependence that may facilitate MCW transfers, and therefore total tradeijt is included to represent bilateral trade flows between exporter country i and importer country j expressed in billions of US$. These data come from the Expanded Trade and GDP Dataset described in Gleditsch (2002). Descriptive statistics are reported in Tables 1 and 2.
| Variable | N | Mean | Min | Max | SD |
|---|---|---|---|---|---|
| armsijt | 38,069 | 9.93 | 0 | 2,979 | 80.94 |
| democracyit | 38,069 | 0.87 | 0 | 1 | 0.33 |
| pgdpit | 38,069 | 14.44 | 0.23 | 39.54 | 8.44 |
| popit | 38,069 | 136.78 | 3.35 | 1,294.85 | 282.96 |
| pgdpjt | 38,069 | 7.30 | 0.20 | 54.29 | 7.59 |
| popjt | 38,069 | 40.62 | 0.09 | 1,294.85 | 144.68 |
| embargojt | 38,069 | 0.01 | 0 | 1 | 0.09 |
| conflictjt | 38,069 | 0.15 | 0 | 1 | 0.36 |
| same orientationijt | 38,069 | 0.42 | 0 | 1 | 0.49 |
| post Cold Wart | 38,069 | 0.55 | 0 | 1 | 0.50 |
| Variable | N | Mean | Min | Max | SD |
|---|---|---|---|---|---|
| armsijt | 32,528 | 10.44 | 0 | 2,979 | 82.37 |
| leftit | 32,528 | 0.49 | 0 | 1 | 0.50 |
| centristit | 32,528 | 0.10 | 0 | 1 | 0.30 |
| concentrationit | 32,528 | 0.72 | 0.16 | 1 | 0.27 |
| end termit | 32,528 | 0.18 | 0 | 1 | 0.39 |
| pgdpit | 32,528 | 16.27 | 4.39 | 39.54 | 7.61 |
| popit | 32,528 | 59.77 | 3.35 | 295.41 | 69.14 |
| pgdpjt | 32,528 | 7.53 | 0.20 | 54.29 | 7.73 |
| popjt | 32,528 | 41.67 | 0.09 | 1,294.85 | 148.87 |
| embargojt | 32,528 | 0.01 | 0 | 1 | 0.09 |
| conflictjt | 32,528 | 0.15 | 0 | 1 | 0.36 |
| same orientationijt | 32,528 | 0.41 | 0 | 1 | 0.49 |
| post Cold Wart | 32,528 | 0.59 | 0 | 1 | 0.49 |
| MCW exportsit | 32,528 | 1.42 | 0 | 15.23 | 2.90 |
| distanceij | 32,528 | 6.77 | 0.08 | 19.84 | 4.14 |
| total tradeijt | 27,735 | 2.49 | 0 | 415.26 | 12.48 |
| democracy scorejt | 28,687 | 3.98 | −9 | 10 | 6.91 |
4. Results
Democracies vs Autocracies
Since the goal is to study the impact of politics on arms exports, a first distinction is set based on the nature of the political regime: whether it is democratic or autocratic. In what follows evidence is provided that democracies and autocracies differ in their MCW exporting behavior. The results presented in Table 3 are based on the full sample of 20 exporters, where democracies account for 87% of the sample. All results refer to a fixed-effects gravity-type tobit where the dependent variable armsijt is the amount of MCW transferred in a given year. In column (1) a specification with no interactions is presented. The dummy democracyit captures disparities in overall MCW exports. In addition to the baseline characteristics (per capita GDP and population of exporter and importer), a control is made for embargoes and conflicts in the importer country, and for a dummy expressing the same political orientation. A dummy post Cold Wart, which takes a value one for years 1990 onwards, is there to capture the sudden worldwide demilitarization which brought a reduction of 40% in military expenditure and international arms transfers (see Skons, 2000; Dunne et al., 2003). post Cold Wart and same orientationijt are also and interacted, in order to take into account the changes in the international scenario. During the Cold War political orientation also reflected bloc division, while after 1989 a major political break and a simultaneous MCW market restructuring took place. In column (2) all regressors of column (1) are interacted with the dummy democracyit. This is a straightforward test to check whether democracies and autocracies differ in any of the previous dimensions.
| dependent variable: armsijt | (1) | (2) |
|---|---|---|
| democracyit | 18.050*** | 142.081*** |
| (1.969) | (2.144) | |
| pgdpit | 0.735*** | 23.639*** |
| (0.101) | (0.114) | |
| democracyit * pgdpit | −19.035*** | |
| (0.112) | ||
| popit | −0.222*** | 0.374*** |
| (0.005) | (0.005) | |
| democracyit * popjt | −1.499*** | |
| (0.014) | ||
| pgdpjt | 5.904*** | −40.634*** |
| (0.118) | (0.135) | |
| democracyit * pgdpjt | 46.451*** | |
| (0.133) | ||
| popjt | 0.569*** | 0.348*** |
| (0.004) | (0.005) | |
| democracyit * popjt | 0.219*** | |
| (0.005) | ||
| embargojt | −2.748 | −193.025*** |
| (4.989) | (8.798) | |
| democracyit * embargojt | 202.353*** | |
| (8.815) | ||
| conflictjt | 31.162*** | 39.855*** |
| (2.145) | (2.809) | |
| democracyit * conflictjt | −14.166*** | |
| (2.838) | ||
| same orientationijt | 39.918*** | 182.499*** |
| (1.701) | (2.068) | |
| democracyit * same orientationijt | −159.619*** | |
| (2.068) | ||
| post Cold Wart | −45.489*** | −58.024*** |
| (2.326) | (2.536) | |
| democracyit * post Cold Wart | −31.044*** | |
| (2.515) | ||
| same orientationijt * post Cold Wart | −28.793*** | −27.655*** |
| (2.093) | (2.307) | |
| Constant | −1,969.802*** | −1,974.245*** |
| (2.076) | (2.158) | |
| Observations | 38,069 | 38,069 |
| Years | 1975–2004 | 1975–2004 |
- *** p < 0.01, ** p < 0.05, * p < 0.1. Robust standard errors in parentheses.
- Fixed effects for i, j, t.
Results suggest that, once we account for country-specific effects, democracies tend to export more MCW. This comes as no surprise, as democracies tend to have more open (and hence more export oriented) economies than autocracies: using the same Polity IV data as used here, Milner and Kubota (2005) showed that countries becoming more democratic experience on average a reduction in tariffs. Also, the interaction terms in column (2) are significant. Exporter's per capita GDP is always positively correlated with MCW exports, but for democracies the effect is much smaller. In contrast, exporter's population is positive for autocracies, but negative for democracies. For what concerns importer's characteristics pgdpjt and popjt, democracies tend to export MCW to rich countries while autocracies tend to export to poor countries, and importer's population has much more (positive) impact on democracies' exports. Since population and GDP proxy for importer's willingness to pay, this can be interpreted as democratic exporters being more sensitive to pure economic incentives. Embargo policies seem to impact autocracies' exports only. In case of armed conflict autocracies tend to export more MCW than democracies. The fact that importer and exporter share the same political orientation (both left-wing, or right-wing, or centrist) always has a positive effect, which as expected decreases in magnitude after the end of the Cold War. However, this effect is very small for democracies and much bigger for autocracies. The post Cold Wart dummy reconfirms the sudden drop in MCW exports after 1989 (Figure 1) and its interaction with democracyit suggests that the drop was more at the expenses of democracies, who were set on an higher export level before.
Trends in MCW Flows 1975–2004
Democratic Exporters
The previous subsection has shown how democracies and autocracies differ with respect to MCW exporting behavior. Attention is now restructed to the subsample of democratic exporters (excluding USSR, Czechoslovakia, China, Poland 1950–1988, and Brazil 1964–1984), and explore how internal political conditions impact MCW export policies. Results in Table 4 still refer to a fixed-effects gravity-type panel tobit where the dependent variable armsijt is the amount of MCW transferred in a given year.
| Dependent variable: armsijt | (1) | (2) | (3) | (4) |
|---|---|---|---|---|
| leftit | −3.152* | −2.035** | −6.257*** | −7.738*** |
| (1.696) | (0.882) | (1.974) | (2.014) | |
| centristit | −6.973*** | −4.517*** | −18.718*** | −19.822*** |
| (2.234) | (1.033) | (2.318) | (2.337) | |
| concentrationit | −30.368*** | −6.670*** | −21.006*** | −19.949*** |
| (2.570) | (1.199) | (2.922) | (2.979) | |
| end termit | −13.952*** | −9.523*** | −16.450*** | −15.717*** |
| (1.652) | (0.838) | (1.828) | (1.857) | |
| pgdpit | 5.947*** | 2.039*** | 2.765*** | 3.565*** |
| (0.109) | (0.057) | (0.142) | (0.147) | |
| popit | −1.142*** | −4.761*** | 0.122*** | −0.059*** |
| (0.014) | (0.017) | (0.019) | (0.020) | |
| pgdpjt | 5.590*** | 2.588*** | 5.886*** | 7.232*** |
| (0.121) | (0.060) | (0.171) | (0.184) | |
| popjt | 0.562*** | 0.069*** | 0.656*** | 0.746*** |
| (0.004) | (0.002) | (0.004) | (0.004) | |
| embargojt | −5.121 | 21.701*** | −35.411*** | −33.346*** |
| (5.127) | (2.347) | (5.486) | (5.508) | |
| conflictjt | 29.748*** | 17.575*** | 35.918*** | 37.749*** |
| (2.111) | (1.200) | (2.331) | (2.366) | |
| same orientationijt | 21.218*** | 2.420*** | 17.877*** | 18.389*** |
| (1.782) | (0.900) | (2.019) | (2.046) | |
| post Cold Wart | −105.263*** | −27.755*** | −99.120*** | −124.541*** |
| (2.360) | (1.190) | (2.747) | (2.975) | |
| same orientationijt post Cold Wart | −28.312*** | −4.054*** | −32.667*** | −30.104*** |
| (2.097) | (1.100) | (2.539) | (2.645) | |
| MCW exportsit | −22.739*** | −22.457*** | ||
| (0.400) | (0.412) | |||
| distanceij | −0.664*** | −0.892*** | ||
| (0.253) | (0.257) | |||
| total tradeijt | 1.987*** | 1.984*** | ||
| (0.029) | (0.030) | |||
| democracy scorejt | 4.065*** | |||
| (0.197) | ||||
| post Cold Wart democracyjt | 0.265 | |||
| (0.291) | ||||
| Constant | −1,755.915*** | −354.000*** | −1,943.513*** | −1,946.829*** |
| (2.172) | (1.071) | (2.417) | (2.466) | |
| Observations | 32,528 | 21,433 | 27,735 | 24,645 |
| Years | 1975–2004 | 1975–2004 | 1975–2000 | 1975–2000 |
- *** p < 0.01, ** p < 0.05, * p < 0.1. Robust standard errors in parentheses. Fixed effects for i, j, t.
Column (1) contains the baseline specification which covers the entire period 1975–2004. The regressors included are: exporter's political variables (two dummies for centrist and left-wing executives respectively, the concentration of power within the government, and a dummy equal to one if the government is serving the last year of the current term with possibility of re-election), per capita GDP and population of exporter and importer, embargoes and conflicts in act in the importer country, a dummy for the same political orientation (both left-wing, or right-wing, or centrist), and a dummy for the post Cold War period interacted with same orientationijt to capture bloc division.
Strong democracies (with polity indicator equal to 10) account for the large majority of our sample of democracies (more than 75%). Among strong democracies, the USA has always ranked first in MCW exports from the 1950s onwards and still nowadays it exports the majority of the world's weapons. In columns (2) the regressors are the same as in the baseline, but the exporters' sample is restricted to strong democracies and exclude the USA. This is to be reassured that the results do not reflect the poor quality of democratic institutions and are not driven by a single outlier country.
In column (3) trade-related controls are added: the exporter-specific annual trend in arms exports MCW exportsit, the distance between the two countries, and the value of the bilateral trade flows between exporter and importer in that given year. As a result of data availability, this specification restricts the time span to 1975–2000.
All democracies proclaim an ideological concern for democratic governance. In line with previous studies (Blanton, 2000), column (4) controlled for the democracy score in the importing country (in a scale from −10 to +10). The importer's democracy score is also interacted with the post-Cold War dummy: during the Cold War, the countries in the Eastern bloc were classified as non-democratic, so in principle democracies' reluctance to export to non-democratic countries might have been just because of the fact that many of those countries were part of the Eastern bloc.11
Results in Table 4 are consistent across specifications and political variables show interesting patterns. The dummies leftit and centristit are significant and negatively signed, which implies that the exporter's chief executive, being right-wing, has a positive impact on MCW exports. This may reflect a general right-wing tendency to lower trade barriers, with its consequences on deregularization of heavy industry exports, or a greater importance of national industry in political agenda, resulting in a higher economic support toward the heavy armament sector. The index concentrationit is negative and significant: lower concentration (i.e. higher fractionalization) of power within the coalition in office is associated with higher MCW exports. This is in line with previous results on trade deregulation: several sources have pointed out that the fractionalization of power within the government is a potent force for trade liberalization (Frye and Mansfield, 2003; Fehrs, 2006; Belloc and Nicita, 2010). The argument provided by Fehrs (2006) is that fractionalized democratic governments liberalize more easily under threat of defection from a coalition member or to pacify the median voter, who presumably benefits from more open markets. Furthermore, a government coalition that includes many different parties will need to remain attuned to proposals for liberalization in many sectors, while a one-party majority government can stop after liberalizing politically important sectors. If we interpret the degree of fractionalization as a measure of political competition, this reconfirms the argument that competition may induce the political parties in office to adopt those market oriented policies which increase economic growth (Besley et al., 2010). For what concerns the end of the executive's current term, the coefficient of end termit is negative and significant. That is, democratic executives decrease their MCW export when running the campaign for re-election. This evidence is hardly surprising, as the public scrutiny of democratic voters is sensitive to arms-related arguments.
All other results go in the expected direction. The exporter having a higher per capita GDP and being less populous increases the quantity of MCW traded. On the other side, per capita GDP and population of the importer, which may proxy for its likelihood to pay in the MCW open market, are positively significant. UN embargoes pending on the importer country are negative (with one exception), but not always significant, in line with the high rate of non-compliance reported by anecdotic and official sources (Amnesty International, IANSA, and Oxfam International, 2006). Conflicts in act in the importer country are always positively significant. The dummy post Cold Wart is negative, consistently with the general crisis in the industry that led to a reduction of 40% in military expenditure and international arms transfers (Skons, 2000; Dunne et al., 2003). The coefficient of same orientationijt is positive and significant, but the negatively signed interaction with the post-Cold War dummy more than compensates the main effect: it seems that after the end of the Cold War there is no more space for strategic considerations of political friendships. In column (3) not surprisingly we find that the distance between countries is negatively significant and that bilateral generic trade flows are positively significant. Exporters' arms market trends MCW exportsit appear negative, which is compatible with the low flexibility of heavy industry supply. For what regards column (4), only the importer's democracy score seems positively significant, while its interaction with the post-Cold War dummy is not.
5. Conclusions
All through the 20th century arms have been not only tradable goods, but also policy instruments. Politics can influence arms trade through several channels: regulation is country's sovereignty, export licenses are exclusively granted by governmental agencies, a relevant share of the armament industry is state property, and the arms production sector attracts subsidies and other measures in defense of national industry. This paper focuses on major conventional weapons (MCW) and investigates whether exporter's internal political conditions impact the quantity of MCW supplied to third countries. For this purpose, a gravity-type tobit equation is estimated for years 1975–2004. Results suggest that the determinants of MCW supply for autocratic and democratic regimes differ. For what concerns democracies, the government in power being right-wing significantly increases the quantity of MCW exported. This may reflect a general right-wing tendency to deregulate trade, or a greater support toward the national armament sector. It is also found that higher fractionalization of power within the coalition in office is associated with higher MCW exports, which is in line with previous results on trade deregulation. Finally, data suggests that the arms trade is particularly affected during an electoral campaign: democratic executives which serve the last year of their current term and can run for re-election tend to decrease MCW exports. This is in line with Gonzalez (2002), which has shown the importance of the election calendar on economic policy, in particular for democracies. The contribution of the paper is to use longitudinally comparative data and a sound quantitative framework to shed light on the political determinants of arms trade through the 20th century. The trade in arms is a debated topic involving political institutions, ethic and economic considerations: a better understanding of its mechanisms is necessary to design an efficient regulation, and this paper is hopefully a step in this direction.
