Guns For Oil? Conventional Weapons Trade in the Post-Cold War Era. Economics Department Working Paper 0509 This version: October 3, 2005

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1 Guns For Oil? Conventional Weapons Trade in the Post-Cold War Era Neha Khanna Associate Professor, Economics and Environmental Studies Binghamton Universy Binghamton, NY Phone: ; Fax: ; and Duane Chapman Professor, Applied Economics and Management Cornell Universy Ithaca, NY Phone: ; Fax: ; Economics Department Working Paper 0509 This version: October 3, 2005 Abstract This paper analyzes the global conventional weapons trade between 1989 and We postulate that a key reason for the huge transfer of weapons to the Persian Gulf region is the enormous value of the oil wealth there along wh the dependence of Western economies on access to the relatively cheap and steady supply of crude oil. We find a strong, posive, and robust empirical association between arms trade and crude oil trade and explain as the result of a Nash equilibrium that was responsible for the remarkably stable crude oil prices during our study period. Key Words: Persian Gulf, secury, oil reserves, panel data Acknowledgements: We thank William Tomek (Cornell Universy) for very useful comments on a previous draft of the paper, Michael Grillot (Energy Information Administration) for making the energy data available electronically, and Dan Wang (Binghamton Universy) for her excellent research assistance. We also thank Chris Barrett and other participants in the development seminar in the Department of Applied Economics and Management at Cornell Universy for their feedback. All errors are ours.

2 1. Introduction The end of the Cold War saw a dramatic reduction in the international trade in conventional weapons. From an all-time peak of 86.7 billion 1999$ in 1987, world arms imports fell to 51.6 billion 1999$ in 1999 (BVC, 2002). But some important trends have emerged during the past decade of the 1990s. A vast majory of conventional weapons are now going to the Persian Gulf countries, wh Saudi Arabia emerging as the world s single largest weapons importer (though by the end of the 1990s, Taiwan was a significant importer as well). On the export side, the singular dominance of the Uned States is notable: in 1999, s share in world arms exports was 64%, an all-time record (BVC, 2002). During the Cold War, much of the arms trade was driven by geopolical factors associated wh the arms race and the action-reaction type behavior of the two super-powers. However, since then, the nature of the international arms market has changed fundamentally, and there has been a shift from polical to economic motives for arms transfers (Anderton, 1995, p. 532). 1 Our goal in this paper is to determine, both analytically and empirically, the main drivers of international arms transfers in the post-cold War period, specifically , the latest period for which these data are available. While geopolical factors remain important, the underlying economic factors are today que different. We postulate that a key reason for the huge transfer of weapons to the Persian Gulf region is the enormous value of the oil wealth there, and the dependence of Western economies, specifically the Uned States and s allies, on access to the reasonably priced and steady supply of crude oil from this region. While is easy to interpret the association between weapons trade and trade in crude oil as a mutually convenient relationship between the Uned 1 Matelly (2003) shows that while the arms race between the Uned States and the former Soviet Union can explain U.S. milary expendures between 1952 and 1989, fails to predict these expendures in the post-cold War period. 2

3 States and Saudi Arabia, the argument is not so simplistic (see also Adelman, 2005). For most of the years between 1989 and 1999, world oil prices remained remarkably stable and between about $15-20 per barrel (the notable exceptions being the Iraq-Kuwa war year of 1990, and 1998; see Chapman and Khanna, 2004b). We will argue that this stabily was the result of a number of underlying economic and polical factors that resulted in a Nash equilibrium. Arms exports from the Uned States and s allies to the Persian Gulf countries not only facilated, but were a key part of the pay-offs that resulted in this equilibrium. We test our analytical argument empirically by examining the factors that explain weapons trade between 1989 and 1999 using an unbalanced panel of data from 156 countries. Our analysis incorporates tradional economic factors such as production costs on the supply side, and income and prices on the demand side, in addion to the polical-economic factors represented by the trade in crude oil. We find that the association between arms trade and crude oil trade is robust, regardless of the empirical specification: there is a strong and posive association between arms exports and crude oil import, on the one hand, and between arms imports and crude oil exports, on the other. The link between arms trade and crude oil trade is not new. After the decline of milary aid programs such as the Uned States Milary Assistance Program, weapons transactions became more commercial. Historically unprecedented OPEC oil revenues in the 1970s and 1980s provided an alternative source of finance and much of these petrodollars were spent on importing weapons. Conversely, weapons exporting countries found a means to offset the balance of payments discrepancy that resulted from the higher oil prices (Smh et al., 1985, pp. 3

4 , and Levine et al., 1994, p. 3). But the period considered in this study, , was an era of relatively low and stable oil prices, and this argument is not very satisfactory. 2 The outline of the paper is as follows. In section 2, we describe the analytical framework for trade in conventional weapons. The world oil price Nash equilibrium (which remained in place from but collapsed in 1998 and 1999 and was reinstated from 2000 to 2004) is also described here. The data are described in section 3, and section 4 reports the results of the empirical analysis. Section 5 concludes. 2. Analytical framework Determining prices and quanties wh any degree of accuracy in the weapons market is complex. This holds true both at the analytical level as well as the empirical level (we defer the discussion of the empirical difficulties until section 3). Weapons are often traded as part of a larger package that may include related aspects such as access to technology and spare parts and seemingly unrelated aspects such as offset agreements (Levine et al., 1994, p. 4, and Levine and Smh, 1997, p. 346). In one example from the 1980s, Smh et al. (1985, p. 241) note that the Belgian purchase of milary vehicles from Bombardier of Canada was tied to landing rights in Toronto for the Belgian commercial airline, Sabena. Furthermore, the extremely close relationship between arms firms and national governments often means that the production and trade of the weapons, not to mention the research and development and marketing efforts, are often heavily subsidized by governments, and that major international trades are more in line wh national priories than wh the prof maximization goals of the private firms (Smh et al., 1985, p. 242, and Levine et al., 1994, p. 4). In addion, the price at which weapons are traded 2 Levine and Smh (1997, p. 344) allude to an association between weapons trade and oil trade in the post-cold War period in the Middle East, but they do not support their statement wh any explanation. 4

5 need not be closely correlated wh the cost of production, and different recipients may pay vastly different prices for identical products (Levine et al., 1994, p. 4, Levine and Smh, 1997, pp ). Despe these complications, economists have addressed the international exchange of weapons using the standard economic model of welfare maximization, wh s attendant demand and supply functions (see, for example, Levine et al., 1994, and Smh, 1995). In the context of the arms trade, these models assume that the relevant economic agents are national governments, wh full information and well defined welfare and secury functions (Levine and Smh, 1997, p. 339). While this approach remains somewhat unsatisfactory, ensures a logical consistency and provides a useful benchmark for empirical analyses (Smh, 1995, p. 76), and forms the starting point for our analysis as well. (a) The supply side: arms exports On the supply side, the export of weapons can be derived by maximizing a supplier s welfare function, where welfare depends on the prof from as well as the milary secury repercussions of the sale (Levine et al., 1994). Supply is thus determined by economic as well as strategic and polical factors (Smh et al., 1985, pp ). In the post-cold War era, we hypothesize that the supplier s welfare, S V, is determined as follows: Max V q S = V S ( Π, S ) S S subject to = P( Qt ) q C( q ) S S S S = S ( W, X ) Π (1) t 5

6 where Π is the prof from the sale of weapons, P(Q t ), is the demand function facing the supplier, S q is the quanty of weapons supplied by supplier i at time t, Q t is the total weapons q S supply at time t, ( ) which depends on C is the supplier s cost function, and W t, the global stock of weapons at time t, and that relate to polical and economic secury issues. S S is the supplier s national secury S X, a vector of other factors In the unipolar, post-cold War period, national secury issues are just as likely to relate to concerns of economic secury as to milary secury. 3 Wh more than 75% of the world s identified crude oil reserves and more than 50% of remaining global oil resources located in the Persian Gulf countries (USGS, 2000), stable access to the crude oil of this region has become a major economic concern for the key oil consuming countries of the OECD. 4, 5 Given the concentration of oil resources in the Persian Gulf, is conceivable that the countries of this region, and more broadly OPEC, could exert monopoly power and set prices to extract monopoly rents. On the other hand, at about $5 per barrel, the Persian Gulf countries have some of the lowest extraction costs in the world and these countries also have the power to undercut competion from the lower 48 Uned States, Alaska, and the North Sea. 6 Yet, production in high-cost regions of the world co-exists wh Persian Gulf production, and world oil prices in the 3 During the Cold War, secury issues included strategic and polical concerns such as the desire to maintain an indigenous and diversified defense industry and polical leverage in foreign policy (see, for example, Smh et al., 1985, pp ). 4 Identified reserves refer to the economically recoverable crude oil at known reservoirs and fields wh expected technology. Remaining resources is an estimate of total conventional crude oil available for recovery. It is the sum of undiscovered resources, a probabilistic concept based in geological extrapolation of potential crude oil, and identified reserves (Chapman, 1999, p. 152). 5 Reflecting the val importance of access to Persian Gulf oil, then U.S. President George H. Bush remarked in 1991, Our jobs, our way of life, our freedom, and the freedom of friendly countries around the world would all suffer if control of the world s great oil reserves fell into the hands of Saddam Hussein (Yergin, 1991, p. 773). 6 $5 per barrel was commonly regarded as the likely equilibrium price in a purely competive world oil market (see, for example, Adelman, 1986, 1993, Economist, 1999, and Yergin, 1991). 6

7 1990s were not nearly as high as they might be under a pure monopoly (for a comparison of global oil prices under perfect competion and pure monopoly, see Chapman, 1993). The dominant firm model can explain the co-existence of low- and high-cost production in a market where a single, low-cost (dominant) firm co-exists wh a high-cost competive fringe, and an equilibrium price that lies between the purely competive and pure monopoly price extremes, provided the difference in marginal costs is not too great (see, for example, Kahn 2005, pp ). However, when the marginal costs of the dominant firm are significantly lower than those of the competive fringe, this model predicts a pure monopoly outcome. 7 Furthermore, as a partial equilibrium model of a single market, is unable to explain the association between the trade in crude oil and the trade in conventional weapons not only between the consumers and producers of the oil but also between the low- and high-cost oil producers. To explain this, we turn to the interplay of economic and polical motivations in a simple game theoretic framework which yields a Nash equilibrium (see also Chapman and Khanna, 2001). The motivation for this approach is perhaps best captured in the following statement made by the OPEC President in April 2000, If prices fall below $22, we will cut production to push prices back up. When prices are above $28, we will increase production. (New York Times, April 7, 2000.) Consider this background: following then U.S. Vice-President George H. Bush s vis to Saudi Arabia in 1986, OPEC established a price-band arrangement wherein crude oil production was increased or decreased to maintain world crude prices whin the target range. This mutually acceptable price-band arrangement had joint pay-offs for the Persian Gulf producers and for their key importers in the West (see Chapman and Khanna, 2001, for details of the pay-off matrix 7 Production in the Artic National Wildlife Refuge (ANWR) becomes cost effective when crude oil prices rise above $20-25 per barrel. In the lower 48 Uned States, production costs for oil produced in 2005 may vary between $15 and $25 per barrel. 7

8 under alternative price ranges). When prices are low, crude oil production outside of the Persian Gulf declines. In the Uned States where production has been steadily declining since the early 1970s, falls rapidly as high-cost facilies are shut down and drilling plummets. Non-Persian gulf oil producers revenues are affected twice: first by reduced production, and second by a lower price. Therefore, at very low oil prices, petroleum companies move to influence policy to raise prices, as in 1986 and In contrast, very high oil prices constute a break on the importing economies, and consumers and oil-using businesses dominate policy. Thus, is in the economic interest of major oil consuming countries to enable the Persian Gulf countries to maintain the target price range, and they do so by providing these countries wh the requise weaponry and milary secury (also see our discussion of the demand side of weapons trade). Milary support from the Uned States and s European allies was crucial in turning back the 1990 Iraqi invasion of Kuwa, and we see this milary support as the bulwark of the Persian Gulf countries abily to maintain crude oil prices whin the target range. (b) The demand side: arms imports The demand for weapons can be derived analogously to the demand for milary expendure using the standard economic model of welfare maximization (see Smh, 1989 and 1995, for models of milary expendure). National welfare, secury, D S D V, is determined by national, and non-defense consumption, c, and is maximized subject to a national budget constraint as well as a secury production function. This can be represented formally as: 8

9 Max V W D = V D ( S, c ) D D D D subject to S S ( q, X ) = (2) Y = p q + c D where c represents non-defense consumption, D q is the quanty of (conventional) weapons consumed, p is the relative price of conventional weapons, Y is national income, and D X is a vector of other factors that determine national secury. This maximization yields a weapons demand function whose arguments include (relative) prices and income, as well as shift factors that are related to national strategic and polical factors, which can be interpreted as representing variations in national preferences in the context of the demand for weapons. Levine and Smh (2003, p. 2) state that the biggest driver of the demand for arms is war or fear of war. The countries of the Persian Gulf are in a unique suation in this context. Wh approximately 1.5 trillion barrels of remaining oil reserves, and assuming that the price of crude oil remains at about $50 per barrel for the remainder of this century, the oil in the Persian Gulf region is worth on the order of $75 trillion (see also Chapman and Khanna, 2004a, Table 13). The precise value of this oil wealth is, of course, debatable, but we believe that s undoubtedly enormous magnude poses a serious problem for the Persian Gulf countries insofar as creates an incentive for milary action such as the Iraqi invasions of Iran and Kuwa. If Iraq had succeeded in s attempt to invade Kuwa, and subsequently to gain access to the oil fields of Saudi Arabia as had apparently planned, would have had control over 40% of identified (known) global oil reserves and 75% of known Persian Gulf reserves. It is not surprising, then, that Persian Gulf governments undertook major milary expansion in the 1990s. Between 1994 and 1999, three Gulf countries -- Kuwa, Saudi Arabia, 9

10 and the Uned Arab Emirates -- purchased a quarter of the global supply of conventional weapons, spending nearly $67 billion on conventional weaponry (see Table 2 in Chapman and Khanna, 2004b), and the Persian Gulf is now the single largest regional importer of weapons. In addion to the fear of external threats, a country may fear internal threats as well and this will also impact s demand for weapons (Anderton, 1995, p. 536, Smh et al., 1985, p. 240). We hypothesize that internal strife is likely to be correlated wh greater income and social inequaly, lack of economic freedom, and the presence of relatively autocratic rather than democratic polical instutions. 3. Empirical model: variables, data, summary statistics Based on the above discussion, we hypothesize the following empirical relationships: Arms exports = f{un production cost(-), milary secury (global weapons stock(+/-)), economic secury(crude oil imports(+)), other factors(+/-)} (3) Arms imports = g{income(+), weapons prices(-), war or fear of war (economic value of oil reserves(+), economic and social inequaly(+), polical freedom(-), conflict(+))} (4) The signs in parentheses indicate the direction of the relationship we expect to obtain. The weapons industry is characterized by high fixed costs and economies of scale, and exports constute an important way of expanding the market and lowering un costs (Smh et al., 1985, p. 242, Anderton, 1995, p. 534, and Garcia-Alonso and Hartley, 2003, p. 40). Thus we expect to see higher exports associated wh lower un costs. We have been unable to locate international data on production costs for the defense industry, and following Smh et al. (1985), we use total 10

11 milary expendure as a proxy for the scale of production, and we expect this to be posively correlated wh arms exports. We expect that the variation in the global stock of conventional weapons is highly correlated wh the global delivery of such weapons, and we include the total real value of conventional weapons delivered in each year as an explanatory variable to reflect exporting countries concern for milary secury. Following Levine et al. (1994), we postulate that there is a non-linear relationship between national milary secury and changes in the global stock of weapons and we include the squared value of total arms deliveries in each year as well. We anticipate that when the global weapons stock is relatively low, increases in this stock increase national secury, but once the global weapons stock becomes high further increases in lower the national secury of weapons exporting countries. As argued earlier, economic secury in the post-cold War era is correlated wh the dependence on crude oil imported from the Persian Gulf, and the successful operation of the target price system. Thus, we expect a posive association between crude oil imports and conventional weapons exports. To the extent that arms production and sales are typically subsidized by national governments, we hypothesize that larger and richer countries are likely to have higher weapons exports and we include gross national product (GNP) and GNP per capa as addional explanatory factors. Among, other factors, we also include a binary variable to indicate whether the country in question was engaged in (or likely to be engaged in) internal or external conflict between 1989 and 1999 as the fact of or potential for war may result in a country 11

12 developing s domestic weapons industry, as well as an interaction term between crude oil imports and the conflict variable. 8 On the demand side, we use total milary expendure to reflect the relevant national income constraining weapons purchases. We measure the economic value of a country s oil wealth by the value of s crude oil exports. We also include GNP and GNP per capa to capture the effect of any other missing variables that might explain variation in arms imports. It is impossible to find data on the sale price of conventional weapons, whether for individual weapons categories, or in more aggregate terms. Even if such price data were available, there are many conceptual problems associated wh defining the prices at which international arms trade takes place (Levine and Smh, 1997, pp ). This is partly due to the highly policized and yet secretive nature of arms deals, and partly due to the various elements of that larger package that characterizes international trade in weapons. Omting prices from the arms import estimation is equivalent to assuming eher that the demand for weapons is perfectly price inelastic or that the relative prices of defense and non-defense consumption are constant over space and time (both hypotheses are untestable in the absence of price data). The most likely suation is that the estimated equation is misspecified and not a true demand function (Smh, 1995, p. 78). 9 To the extent that the global trade in weapons is likely to be correlated wh variations in weapons prices, we use total (global) deliveries of conventional weapons as a proxy for weapons prices. We expect that the real value of global weapons deliveries will be higher when weapons prices are lower, and thus anticipate a posive 8 See discussion below for details on how we constructed this variable. 9 Smh (1989, p. 351) suggests that in the absence of price data is better to use shares of expendures as the dependent variable in the demand function as this assumes a unary price elasticy. However, in a subsequent paper (Smh, 1995, p. 78) he claims that when price data are unavailable or are of suspect qualy, this is also an untestable hypothesis. 12

13 correlation between global weapons deliveries and arms imports. We acknowledge that this is a noisy measure of weapons prices and that there is likely to be measurement error, and we recognize the inherent limations and the empirical issues that this raises. To measure international and intertemporal variations in polical freedom, we use the Poly2 index developed under the Poly IV Project at the Universy of Maryland. 10 This index reflects the structure of governance (for example, the degree to which all cizens are guaranteed civil liberties) and the openness of a country s polical instutions. It ranges from 10 (very autocratic) to +10 (very democratic). For details, see Marshall and Jaggers (2002). The Poly IV Project also provides information on the occurrence of an armed conflict. We used the individual country reports available on the webse for the Poly IV Project to construct a binary conflict variable that equals one if a country was involved in a significant internal or external armed conflict between 1989 and This includes wars for independence, ethnic warfare, revolutionary warfare, and genocide/policide. 11 We also include an interaction term between conflict and crude oil exports. For our analysis, not only is the fact of war relevant, the fear of war is important as well because countries may engage in an arms build-up eher by importing weapons or by developing the domestic weapons industry in response to this fear. 12 To account for this we include 19 addional countries that, in our opinion, faced the potential for armed conflict from , as having a value equal to 1 for the conflict variable. Table A2 in the appendix highlights these countries, which include all the countries in the Persian Gulf region Policide refers to mass, targeted killings in which a group of people are killed due to their polical or ideological beliefs. It is similar to genocide, but different in that policide does not specifically target ethnic, racial or cultural groups. 12 For example, in the late 1990s, Taiwan emerged as a significant importer of weapons following s deteriorating relationship wh China. 13

14 We measure economic freedom wh the Economic Freedom Index as described Gwartney et al. (2001). This index is based on five major indicators of economic freedom size of government, legal structure and secury of property rights, access to sound money, freedom to exchange wh foreigners, and regulation of cred, labor, and businesses. Each of these are, in turn, based on several sub-indicators that reflect economic freedom through personal choice, voluntary exchange, freedom to compete, and protection of person and property. The Economic Freedom Index ranges from 0 (very low economic freedom) to +10 (very high economic freedom). Index values are available for every fifth year and we interpolated the missing data by assuming a constant annual average growth rate between successive index values. Finally, we measure the degree of income and social inequaly by the Gini index. We were unable to obtain national time series data on the Gini index and we used the index value for any year between 1989 and 1999 for which the data were available and assumed that countryspecific Gini index values remained constant over this eleven year period. Quanty data on weapons trade are regularly published by the Uned States Bureau of Verification and Compliance (and formerly by the U.S. Arms Control and Disarmament Agency) as well as the Stockholm International Peace Research Instute (SIPRI), and they are available by weapons type. But whout corresponding price data by weapons type, these quanty data are not very useful for our purposes. Our best option is to utilize the value of arms transfers series published by the Bureau of Verification and Compliance (BVC). 13 These data reflect the annual value of international trade in conventional weapons. The BVC obtains annual data on the value of weapons trade from each country which are reported in nominal domestic currency. These 13 SIPRI also publishes a data series that reflects financial value of arms trade for each country, but we believe the BVC data are better sued for our purposes since they account for inter-country variation in inflation rates, whereas SIPRI uses the U.S. consumer price index to deflate all nominal values ( 14

15 data are then deflated by the country s annual GNP deflator to obtain real (1999) values, before being transformed into real US$ using annual market exchange rates (BVC, 2002, p. 199). A similar methodology is used to obtain the real milary expendures and GNP. To account for any sector-specific inflation, we further deflate the value of arms trade and the milary expendures by the U.S. producer price index (1999=100) for the defense and space industry. Table 1 summarizes the major trends in conventional weapons trade between 1989 and The Uned States remained the world s largest exporter of conventional weapons, and s share in the world s conventional weapons exports more than doubled, rising from about 30% in 1989 to 64% in The Uned States, Uned Kingdom, France, Germany, Soviet Union/Russia, and China together accounted for 90% of the total world exports of weapons. The Persian Gulf was the leading regional importer of these weapons, receiving about a quarter of the world s supply of conventional weapons. Saudi Arabia was the world s single largest recipient of weapons and s share in world imports rose from about 11% in 1989 to a peak of approximately 24% in 1995, and then declined to 15% by Nearly half of Saudi Arabia s weapons imports came from the Uned States and another third from the Uned Kingdom. 15

16 Table 1: Trends in Conventional Weapons Trade, A. Persian Gulf Arms Imports (1999 million $) Country World total Bahrain Iran Iraq Kuwa Oman Qatar Saudi Arabia U.A.E Total Persian Gulf (PG) PG / World (%) Saudi Arabia/World (%) B. Leading Arms Exporters (1999 million $) Country World total Uned States Uned Kingdom Soviet Union (Russia) France Germany China Total of above Total 6 / World (%) U.S. / World (%) Source: Based on U.S. Department of State (2002, Table II) 16

17 We base our analysis of the trade in conventional weapons on an unbalanced panel of 156 countries from Table 2 summarizes our data for each year over this eleven year period. (Table A2 in the appendix lists our data sources and provides a country-by-country summary.) This was a period of declining real trade in conventional weapons, coupled wh declining real milary expendures. The U.S. producer price index for the defense and space industry (not shown) also declined steadily over this period, except for the last two-three years when there was a slight increase. The slump in the global economy in the early 1990s, followed by the subsequent recovery, is clear from the GNP and GNP per capa data. On average, the degree of polical freedom increased between 1989 and However, most of the Persian Gulf countries had autocratic polical instutions during this period, and the average value of the Poly2 index for these countries between 1989 and 1999 is (Saudi Arabia and Qatar had an average value for the Poly2 index equal to 10; see appendix also). On the other hand, most OECD countries had very democratic polical instutions. A ltle over a third of the countries included in our dataset faced an armed internal or external conflict between 1989 and The degree of economic freedom in the world increased on average between 1989 and 1999, but remained more or less constant for many African countries. 14 Unfortunately, Economic Freedom Index values are available for only four of eight Persian Gulf countries. Of these, Kuwa and Oman experienced significant improvement in economic freedom, whereas Iran and Uned Arab Emirates had a roughly constant degree of economic freedom (wh average index values of 4.6 and 7.3, respectively). 14 Some of the notable exceptions in Africa include Uganda, Kenya, Namibia, South Africa, and Zambia, which experienced major posive changes over the decade. 17

18 As shown in Table 2, the overall degree of economic inequaly remained stable between 1989 and The greatest income inequaly was reported for Namibia wh a Gini index of 70.66; at the other extreme, Hungary had a Gini index value of Table 3 provides a preliminary insight into the general patterns of correlations in the data. It is clear that arms trade is closely associated wh the trade in crude oil. Even though the Uned States and Saudi Arabia are key countries in this context, as confirmed by Figure 1, the correlation coefficients between arms export/import and crude oil import/export remain posive and statistically significant even when these two countries are excluded. The strong and posive correlation between arms trade and GNP, GNP per capa, and total milary expendure is also apparent. 18

19 Table 2: Trends in Economic and Polical Variables, Variable (uns) Arms import Mean (million 1999$) Std. deviation # of countries Arms export Mean (million 1999$) Std. deviation # of countries Crude oil export Mean (1000 bl/day) Std. deviation # of countries Crude oil import Mean (1000 bl/day) Std. deviation # of countries GNP per capa Mean (1999 $) Std. deviation # of countries GNP Mean (billion 1999$) Std. deviation # of countries Milary Mean expendures Std. deviation (billion 1999$) # of countries Poly2 index Mean (Range: -10, +10) Std. deviation # of countries Conflict Mean (1 = conflict, Std. deviation = no conflict) # of countries Economic Mean Freedom index Std. deviation (Range: 0, 10) # of countries Gini index Mean (Range: 0, 100) Std. deviation # of countries Total arms deliveries (bill. 1999$) Note: Summary statistics for arms trade and milary expendures are shown before being deflated by the U.S. PPI for the defense and space industry. Summary statistics for the Economic Freedom and Gini indices are based on the countries for which data are available, and do not include the assigned values for the Persian Gulf countries (see discussion in section 4.b). 19

20 Table 3: Pair-wise Correlations ( ) Arms Exports Arms Imports Number of Countries; Number of observations Crude oil imports (<0.0001) Crude oil exports (<0.0001) GNP per capa (<0.0001) GNP (<0.0001) Milary expendures (<0.0001) Total arms deliveries (0.5247) Conflict (0.0023) (<0.0001) (<0.0001) (<0.0001) (0.1160) (<0.0001) Poly (0.0009) Gini Index (0.0139) Economic Freedom Index (0.1160) 156; ; ; ; ; ; ; ; ; ; 1061 Note: the p-value for the null hypothesis that the correlation coefficient is 0 is reported in parentheses. Correlations are calculated after deflating the arms trade and milary expendures by the U.S. PPI for the defense and space industry, and the correlations between arms imports and the Gini Index and the Economic Freedom Index include the assigned values for the Persian Gulf countries (see discussion in section 4.b). 20

21 Figure 1: Arms Trade and Crude Oil Trade ( ) Panel A: Crude Oil Exports and Arms Imports Arms Imports (10^ $) Taiwan Saudi Arabia Crude Exports (1000 bls/day) Arms Exports (10^ $) Soviet Union Panel B: Arms Exports and Crude Imports U.K. France Japan Crude Imports (1000 bls/day) U.S.A. 21

22 4. Results and discussion We start by estimating the structural relationships outlined in equations (3) and (4), and then test the robustness of our results. Due to the time invariance of the binary conflict variable and the Gini Index, we are restricted to estimating country random effects in any model including these variables. Similarly, there is no inter-country variation in the global weapons stock, and this precludes us from estimating time fixed effects. Since arms trade agreements may span several years, in all models we include the one-year lagged dependent variable as an explanatory variable so as to allow for intracountry autocorrelation in the error terms. 15 The number of observations in our unbalanced panel data set may vary across models as the availabily of data varies across countries and variables. (a) Arms exports We estimated equation (3) using random effects and information from 156 countries for which data on all the relevant variables were available. The results are reported in column 1 of Table 4. The strong posive association between weapons exports and crude oil imports is confirmed. To test whether the posive association between arms exports and crude oil imports is driven primarily by the overwhelming dominance of the Uned States in arms exports, we re-estimated the model by excluding Uned States from the dataset (see column 2 of Table 4). While the magnude of estimated coefficient on crude oil imports reduced to about half, remained posive and statistically significant, and overall the qualative results obtained are similar to those 15 We estimated all models in Tables 4 and 5 whout the lagged dependent variable and the results obtained are qualatively similar to those reported here. 22

23 reported in column 1. The coefficient on the one-year lagged value of arms exports turns from posive and significant in model 1 to negative and significant in model 2, and this reflects the decreasing importance of other (non-usa) exporters in the weapons market. On the whole, we conclude that while the Uned States dominates in terms of the magnude of weapons exports, is not an outlier in terms of the statistical relationship between arms exports and crude oil imports. In addion, crude oil importing countries that experienced or were likely to experience an internal or external conflict were likely to have higher arms exports than countries that did not. This result is primarily driven by the fact that the Uned States, which is the both the dominant exporter of conventional weapons as well as the leading importer of crude oil, was engaged in an international conflict between 1989 and 1999 (Operation Desert Storm). When we re-estimate the model whout the Uned States in the data matrix, the coefficient on this variable is negative and statistically significant. In the inial specification of the model, we allowed a quadratic relationship between changes in the global weapons stock and arms exports. However, due to the high degree of correlation, the estimated coefficient on neher the linear nor the quadratic term was statistically significant, and therefore we report results for models wh only the linear term. (The estimated coefficients on the other variables were qualatively identical to those reported in Table 4.) Arms exports are only weakly and posively associated wh changes in the global weapons stock: the estimated coefficient is statistically significant at the 15% level. This supports our hypothesis that in the post- Cold War era, national secury is likely to be tied more closely to economic secury than to milary secury. To the extent that a large fraction of global weapons were exported 23

24 to the Persian Gulf countries, this weakly posive association also suggests that the weapons exporters may have perceived an association between their national secury and the changes in the stock of weapons in this region. This is consistent wh our hypothesis that is in the national interest of the major weapons exporters to maintain milary secury in the Persian Gulf region due to the large value of the oil wealth there (also see the analysis of weapons imports). Contrary to our expectations, countries wh higher GNP and GNP per capa have lower weapons exports on average. This can be explained by the fact that 90% of the conventional weapons exports originate in only five countries and many of the richest and most developed countries in the world export very small quanties of weapons, if any at all. While the coefficient on the quadratic power of per capa GNP is posive and significant in model 1, suggesting a U-shaped relationship between arms exports and per capa GNP, the turning point lies well beyond the highest value of per capa GNP observed in our sample. This suggests that the relationship is non-linear but monotonically negative for observed values of per capa GNP, and this is true for model 3 in Table 4 as well. The coefficient on milary expendures is posive and statistically significant in all specifications in Table 4, confirming our expectation that the arms industry experiences economies of scale in the production of conventional weapons. Since the estimated coefficients on conflict and the global weapons deliveries are not statistically significant, we exclude these variables and re-estimate the model using country fixed effects and time fixed effects to further test the robustness of our results (see column 3, Table 4). The results obtained are qualatively identical to those in 24

25 column 1 and the strong and posive association between arms exports and crude oil imports remains. As a final test of robustness, and following Smh et al. (1985), we estimated equation (3) using a double log specification where arms exports, crude oil imports, GNP, GNP per capa, and milary expendures were expressed in natural logs, and using random effects. This specification yielded an abysmally poor f (the R-squared was approximately 0.02) and we do not report these results here. 25

26 Table 4: Arms Exports Variable Crude oil imports (CI) *** (0.0014) GNP *** (1746.4) GNP per capa ** (0.1975) GNP per capa squared *** (0.0059) Milary Expendures *** (0.0036) Conflict (1.4532) Conflict * CI *** (0.0016) *** (0.0009) *** (1255.5) (0.1237) (0.0038) *** (0.0027) (0.8667) *** (0.0027) *** (0.0017) *** (2303.1) *** (0.3832) *** (0.0087) *** (0.0064) *** (0.0024) Total arms deliveries (0.0021) * (0.0019) Lagged arms export *** (0.0104) ** (0.0077) * (0.0107) R squared # of countries; # of obs. 156; , ; 1516 Models 1 and 2 were estimated using country and time random effects, model 3 was estimated wh country and time fixed effects. Model 2 was estimated using a data set that excludes the Uned States. *** indicates significance at 1%, ** indicates significance at 5%, * indicates significance at 10% 26

27 (b) Arms Imports Except for Iran, we were unable to obtain Gini Index values for any of the Persian Gulf countries. Also, Economic Freedom Index values are not reported for Saudi Arabia, Qatar, and Iraq. Since the Persian Gulf countries are leading weapons importers as well as leading exporters of crude oil, an analysis of the international demand for conventional weapons that does not include these countries is not very instructive. Thus, as a first step and based on the authors judgement, we assumed that these countries have extremely low economic freedom coupled wh a high degree of economic and social inequaly, and we assigned the lowest observed value of the Economic Freedom Index (least economic freedom, Uganda in 1989 = 2.9) and the highest observed value for the Gini index (highest income inequaly, Namibia = 70.66) to all Persian Gulf countries for which data on these measures were unavailable. 16 This allowed us to include all the Persian Gulf countries in our analysis, yielding a dataset wh 102 countries wh information on all variables included on the right hand side of equation (4). The results based on this constructed data set are reported in columns 1, 2 and 4 of Table 5. The model in column 1 is our benchmark model and the other models are used to the test the robustness of the results obtained. The overall strong and posive association between weapons trade and crude oil trade is apparent yet again in column 1: the coefficient on crude oil exports is posive and statistically significant. To test the role of Saudi Arabia in determining the direction and strength of this relationship, we re-estimated the model after excluding Saudi Arabia from the dataset, and the results are reported in column 2 of Table 5. The coefficient on 16 The Gini Index value for Iran is approximately and assigning this value to the Persian Gulf countries wh missing Gini Index values in place of the highest reported value of yields no qualative differences in the estimated coefficients. 27

28 crude oil exports is no longer statistically significant, emphasizing the dominant role of Saudi Arabia in the international demand for conventional weapons. As anticipated, countries engaged in or wh a potential for internal or external conflict were likely to have greater arms imports on average. The coefficient on the interaction term between conflict and crude oil exports is not statistically significant in any specification estimated wh the full data set. However, when Saudi Arabia is excluded from the data set the coefficient on the interaction term is posive and significant. Countries wh higher GNP per capa were likely to have higher arms imports, and we conclude that this relationship is concave, though monotonic, since the estimated peak of the quadratic relationship lies beyond the sample range of per capa GNP. Neher GNP nor milary expendures are statistically significant. We suspect this is due to the high correlation between these two variables (correlation coefficient = 0.87) and when we exclude GNP from the estimating equation, the coefficient on milary expendures is posive and statistically significant. (All other estimated coefficients remain largely unaffected by the exclusion of GNP.) The coefficient on the global arms deliveries is posive and statistically significant, as expected. Given the anticipated negative correlation between total deliveries and the average sales price of weapons, this indicates that weapons imports are higher when the average price is lower, as implied by economic theory. Given that our measure of weapons prices is noisy, we test the robustness of our results using the annual US producer price index (PPI) for the defense and space industry as an explanatory variable in place of global arms deliveries. In these alternative specifications, we do not 28

29 deflate the defense sector variables (arms imports and milary expendures) by the PPI. The results are reported in Appendix Table A1. While the overall f of these models is not as good as those reported in Table 5, the broad qualative results are comparable, especially as relates to the coefficient on crude oil exports. The coefficient on the PPI is posive and statistically significant. This suggests the demand for weapons is price inelastic so that total expendure on weapons (real value of arms imports) is posively correlated wh the price of the weapons. Contrary to our expectations, the estimated coefficients on none of the three variables reflecting polical, economic, and social freedom/inequy are statistically significant in any specification reported in Table 5. The main reasons for this, we suspect, is that our conflict variable reflects both external and internal conflict, and there is a high degree of correlation between and Poly2, the Gini Index, and the Economic Freedom Index: the pair-wise correlation coefficients are -0.38, 0.25, and -0.36, respectively. The Gini Index and Economic Freedom Index were also the two variables for which we had to assign assumed values for most of the Persian Gulf countries. So, to test the robustness of the estimated coefficients on the remaining variables, we exclude these three variables in an alternative specification of the model. This also allows us to expand our dataset to include the same 156 countries used to analyze arms exports. The results, shown in column 3 of Table 5, are qualatively identical to those reported for our benchmark model in column 1. Finally, we re-estimate the model from column 1 using a double-log specification where all the non-binary and non-index variables are expressed in natural logs. As shown in column 4 of Table 5, the coefficient on the log of crude oil exports remains 29