metadata: authors: 0: author: Vezina,Pierre-Louis
count: Africa,World
docna: 0: docna: The Oil Nouveau-Riche and Arms Imports
docty: Policy Research Working Paper
subtopic: Conflict and Fragile States,Social Conflict and Violence,Oil & Gas,International Trade and Trade Rules
teratopic: Conflict and Development,Social Development,Energy,International Economics and Trade
abstracts: cdata!: Countries that strike it rich when
            exploring for oil and gas often fail to see growth
            materialize. This paper shows that one way things can get
            messy is via squandering new wealth, based on future
            resource revenues, on arms imports. In the five years
            following a giant oil or gas discovery, arms imports
            increase by 25 percent. The effect is even larger, at 51
            percent, when the price of oil is as high as $80 per barrel.
            These estimates can be interpreted causally as the timing of
            giant oil discoveries is unpredictable due to the uncertain
            nature of exploration.
repnme: repnme: The Oil Nouveau-Riche and Arms Imports
display_title: The Oil Nouveau-Riche and Arms Imports
listing_relative_url: /research/2020/09/32371070/oil-nouveau-riche-arms-imports
topicv3: Trade m1326924 698,Oil and Gas Systems m1327342 549,Oil and Gas Industry m1327562 539,Military m1505651 3036,Weapons m1505711 3037
publishtoextweb_dt: 2020-09-01T00:00:00Z
historic_topic: Conflict and Development,Social Development,Energy,International Economics and Trade
origu: Office of the Chief Economist (AFECE)
no_of_pages: 35
model_extractions: Policy Research Working Paper 9374 The Oil Nouveau-Riche and Arms Imports _Pierre-Louis Vézina_ Africa Region Office of the Chief Economist September 2020
Policy Research Working Paper 9374

# **Abstract**

Countries that strike it rich when exploring for oil and gas often fail to see growth materialize. This paper shows that one way things can get messy is via squandering new wealth, based on future resource revenues, on arms imports. In the five years following a giant oil or gas discovery, arms imports increase by 25 percent. The effect is even larger, at 51 percent, when the price of oil is as high as $80 per barrel. These estimates can be interpreted causally as the timing of giant oil discoveries is unpredictable due to the uncertain nature of exploration.

This paper is a product of the Office of the Chief Economist, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at pierrelouis.vezina@gmail.com.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# The oil nouveau-riche and arms imports

Pierre-Louis V ́ezina<sup>_†_</sup>

## JEL CODES: F14, Q33

Key Words: giant discoveries, arms trade, resource curse.

> _∗_ I am grateful to James Cust, Pierre Mandon, Kostas Matakos, Rick van der Ploeg, Michael Ross, Wessel Vermeulen, and participants at the 2019 World Bank-OxCarre workshop on extractives in Africa for excellent suggestions. This work is part of the World Bank’s project on Africa’s Resource future.

> _†_ Dept of Political Economy, King’s College London. Email: pierre-louis.vezina@kcl.ac.uk.
# **1 INTRODUCTION**

Natural resources are often thought of as a curse, slowing long-run growth in developing countries (Sachs and Warner, 2001; van der Ploeg, 2011; Ross, 2012). Recent studies have emphasized that resource discoveries can also have short-run economic consequences before windfalls start pouring in. This is because large and unexpected oil and gas discoveries act as news shocks, driving the business cycle (Arezki et al., 2017). Also, only the prospect of resource wealth can unleash malign political forces (Venables, 2016). Arezki et al. (2017) suggest that on average across countries investment increases right after a large discovery hits. This boosts GDP and nightlight intensity in cities (Smith and Wills, 2018), yet growth expectations tend to be too optimistic and disappointments or even reversals may follow (Cust and Mihalyi, 2017). This may be because internal armed conflicts emerge as a results of the newfound wealth (Lei and Michaels, 2014), or coups happen (Nordvik, 2018), or democratic institutions deteriorate (Tsui, 2011) and public money is not spent wisely. This brings us to the question this paper aims to answer: Do newly-oil-rich countries lash out on weapon imports?<sup>1</sup> While newly oil-rich countries may buy weapons to protect their resources from rebels or foreign invasion<sup>2</sup> , lashing out on weapon imports might turn out as anti-development for at least three reasons. One is that weapons cause conflicts, being one of its necessary technology (Hirshleifer, 2000). Pamp et al. (2018) for example shows that arms imports increase the probability of civil wars in conflict-prone countries, and hence suggests that arms spark escalation rather than act as a deterrent against conflicts. Similarly, Craft and Smaldone (2002) showed that arms imports do predict civil wars in sub-Saharan Africa, and Wezeman et al. (2011) suggest that governments often can not secure their stockpiles and weapons are

> 1Previous research suggests that military spending in general increases following discoveries in non-democracies (Cotet and Tsui, 2013) or following oil price shocks if the oil is onshore (Nordvik, 2018). The role of arms imports specifically has not been explored however.

> 2Wezeman et al. (2011) suggest that in several cases arms imports may have helped governments legitimately maintain or restore stability. One example is in Chad, where a UN backed boost to the military in 2008, which included Chad’s first combat aircraft, was instrumental in defeating rebels based in Sudan.

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stolen by rebel groups. Another reason is that arms imports may lead to political repression. Blanton (1999) has indeed shown that arms imports are linked to human right violations in developing countries. A third reason is that arms purchases by governments may crowd out more development-friendly state spending. One example here is Fan et al. (2018), who showed that across countries military expenditures do crowd out health expenditures.

The recent experience of Mozambique provides an illustrative anecdote on the link between newfound wealth and arms imports, and its potential consequences. After discovering – enormous amounts of natural gas off its coast in 2009 estimated at the time to be – worth around 50 times its GDP in net present value terms the country experienced an unprecedented growth spurt and a foreign investment boom (Toews and Vezina, 2017). It ended sharply five years later when a debt crisis hit. It turned out the government had borrowed too much, and illegally, to purchase military ships. This transaction worth hundreds of millions of dollars was made possible by loans worth around 2 billion dollars, or 12.5% of Mozambique’s GDP. It inadvertently revealed cracks in the political system and that was enough to derail the entire economy (see Hill and Nhamire (2018)). Other cases of oil-for-arms episodes include Sudan’s oil for guns deal with China (Herbst, 2008), Azerbaijan wasting the wealth from its post-2010 energy boom on vast amount of weapons (Altstadt and Menon, 2016), or the 1990s Angolagate scandal, which saw Angola’s oil revenues exchanged against weapons from ex-Soviet countries (Economist, 2008). Figure 1 shows that, in those four countries, arms imports did surge after giant discoveries, in 2009-2011 in Mozambique, in 2003 in Sudan, in 2010 in Azerbaijan, and in 1996-1998 in Angola.

To examine the relationship between oil and gas discoveries and arms imports I combine data on oil and gas discoveries from Horn (2011), updated by the World Bank, with arms imports data from the Stockholm International Peace Research Institute (SIPRI), as well as from the United Nations (COMTRADE). I estimate a panel covering 120 developing countries over 1990-2017, i.e. the post-Cold War years which also include the latest oil price boom that peaked in 2011 (Figure 4). I find that in the five years following a giant oil 4
<!-- Start of picture text --> AGO<br>oi<br>ANY AL Eee°<br>1990 1994 1998 2002 2006 2010 2014 2018<br>Discovery _———— Arms imports<br><!-- End of picture text --> <!-- Start of picture text --> MOZz<br>ee |i<br>1990 1994 1998 2002 2006 2010 2014 2018<br>Discovery _———— Arms imports<br><!-- End of picture text -->

<!-- Start of picture text --> AZE<br>in<br> |) a<br>1990 1994 1998 2002 2006 2010 2014 2018<br>Discovery _———— Arms imports<br><!-- End of picture text -->

<!-- Start of picture text --> SDN<br>o<br>lw o.<br>1990 1994 1998 2002 2006 2010 2014 2018<br>Discovery _———— Arms imports<br><!-- End of picture text -->
or gas discovery, arms imports increase by 25%. The effect is even larger, i.e. 51%, when the oil price is as high as $80, and, albeit surprisingly, in less corrupt and more democratic countries. These estimates can be interpreted causally in a regression with country and year fixed effects as the timing of giant oil discoveries is unpredictable due to the uncertain nature – of exploration.<sup>3</sup> And while all imports also increase after discoveries think new watches, – trinkets, and shoes the arms share of imports also increases, e.g. by 17% on average or by 27% when the oil price is around $80. And the arms imports share of GDP increases by around 30% after giant discoveries. Across discovery countries, the average arms share of GDP was 0.5% during 1990-2018. This share shoots up after discoveries. In Sudan for example, the arms imports share of GDP jumped from around 0.5% to more than 8% in 2008. In Equatorial Guinea, it reached around 5% of GDP in 2003, 4 years after the giant discovery. In Azerbaijan, it went up to around 2.5% after the two discoveries in 1999 ad 2010, in Congo-Brazzaville, it shot up to above 3% in 2013, the year of the discovery. On average I find that, 4 years after the discovery, the arms share of GDP is 6 times larger than it was in the discovery year.

While the relationship between oil discoveries and arms imports has not been established before, there is some literature on the links between oil and arms more generally. The latter can help us understand the different reasons why we observe this arms imports surge. The first explanation that may come to mind is that future oil producers may want to protect their resources from rebels or foreign invasion and hence lash out on sophisticated weapons. This was Mozambique’s government justification for buying military speedboats. This explanation however has not been put forward much by the literature. Instead, the literature focuses mostly on supply-based explanations. Nistic`o et al. (2018) for example argue that selling arms is a foreign policy tool to secure access to oil, as providing arms

> 3Giant discoveries of at least 500 million barrels of recoverable oil can be thought of as winning the jackpot when playing the exploration-drilling lottery. Previous studies have indeed suggested that the timing of giant oil discoveries is plausibly exogenous due to their unexpected nature (Arezki et al., 2017; Tsui, 2011; Lei and Michaels, 2014; Cavalcanti et al., 2019). The relationship between resource rents and arms imports on the other hand could be endogenous as a well-equipped military could be a condition to successful extraction, for example.

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might reduce the risk of political instability in oil-rich countries. Nitzan and Bichler (1995) call it the weapondollar-petrodollar coalition. Khanna and Chapman (2010) also suggest that the huge transfer of weapons to Persian Gulf countries during 1989-1999 might have been in order to keep access to a steady supply of oil. This would also fit with the oil-for-arms deal between China and Sudan mentioned above. Other researchers provide a more macroeconomic explanation. Chan (1980) and Snider (1984) suggest that expensive oil affects the supply of weapons, as oil-importing countries such as the United States, United Kingdom, France, Germany, and Italy might be using arms exports to offset the cost of imported oil.

Another explanation might be that, when news of these giant discoveries break out, shady businesspersons exploit new opportunities to siphon debt money. This is in line with what happened in Mozambique, where a Lebanese businessperson convinced a handful of Mozambique government officials and dodgy Credit Suisse bankers to issue debt to purchase French military boats. It is also in line with the Angolagate scandal, when the son of the French ex-president was involved in a scheme where Soviet arms were purchased with the revenues of selling oil from Angola to France.

This paper adds to the resource curse literature by suggesting a new channel through which resources can unleash malign development forces. This new channel furthers our understanding of the oil and conflict relationship, e.g. (Lei and Michaels, 2014), by highlighting that discoveries might trigger large amounts of arms imports. These arms imports may fuel future conflicts (Pamp et al., 2018), increase political repression (Blanton, 1999), and crowd out state spending on health, education, and infrastructure (e.g. Fan et al. (2018)). Conflicts and other anti-development forces may hence in some cases be an indirect consequence of balance-of-payment preferences in arms exporters (Chan, 1980; Snider, 1984) or of a handful of greedy businessmen. The import of arms may thus be one reason why oil-rich governments perpetuate the curse of oil.

The paper proceeds as follows. In section 2 I describe the data and my empirical strategy.

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In section 3 I present my results. Section 4 concludes.

# **2 DATA**

I use two sources of data on arms imports. The first is from the Stockholm International Peace Research Institute (SIPRI). SIPRI is the most trusted source of data on international arms transfers. Since official statistics on arms trade may be patchy, SIPRI has been tracking transfers of major weapons, such as aircraft, drones, rocket launchers, missiles, torpedoes, and reconnaissance satellites, using a variety of sources such as newspapers, annual reports of arms producing companies, blogs, defense white papers, and parliamentary records. It then uses known production costs to estimate the value of the military transfers and construct a trend-indicator value of arms imports. While this indicator does not provide information on the sales prices of arms transfers, it provides a real unit that allows to identify trends over time.

As a robustness check I also use official customs data from the United Nations, i.e. UN COMTRADE. I use reports from both importers and exporters to maximize the data coverage as official trade statistics, especially for arms, may be missing. I include all goods classified as firearms of war and ammunition, that is, section 95 of the SITC Revision 1 classification. This includes artillery weapons and all types of guns and bullets (note that SIPRI does not include small arms). I also include armored fighting vehicles, warships and aircrafts using the 4-digit HS-classification codes 8710, 8802, and 8906, to include major weapons. I convert the raw data, in nominal USD, to constant 2010 USD to focus on real changes in arms transfers.

Figure 2 shows that there is a strong correlation between the two measures of arms imports, both in 1990 and 2010. The two measures seem more in line in 2010 then in 1990. Also, COMTRADE data seem to cover more imports, as per its wider definition. Overall, these two sources provide relevant alternatives to capture countries’ arms imports.

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<!-- Start of picture text --> 1990<br>15<br>_e THA eKOR<br>MEX °oBRAPION eTUR SIND<br>o JAM JOR<br>KEN ee<br>ozArF KA bn SayIWR © PAK<br>10 °SPRY ePBRY PER<br>FawaS PAN ° 7AM oma ©URY ®POL °EGoIRQy<br>= ee oF 8 eLBY oPHL °YEM 2 CUB °BGR<br>fe} ooMNT °DZA<br>ONPL equ °CNGAB<br>saleepee LI oTONéuus °COG onicoVNM oIRN<br>8 BiSM<br>eVCT eRWA<br>0 ©UGA oGIN<br>° 2 y P 8<br>SIPRI<br><!-- End of picture text --> <!-- Start of picture text --> 2010<br>20<br>oCHN<br>i ° oPHL cARG oR UsOLBJHAKAZ ° olRbERRTNR° bo MAR”eCRIEGYKORoPAK°| N ;<br>Ww 2 Hee OGHA ° oHUN ReinBOONE! °DZA<br>| PURER non eBB Ek JOR °SDN 2ArG,<br>a<—5 10 BBM3 aashtIe samao, 3ROWSENottNTR 6 GAB=oT Cogan© DANN,<br>fe) eeieg<br>ro) $yBE otc0 oMNG<br>5 8sae ereBey GIN<br>9. oki R<br>° > 4 + :<br>SIPRI<br><!-- End of picture text -->
<!-- Start of picture text --> 15 | GH[1] AfricaAmericas<br>[1 Asia<br>[) Europe<br>[) Oceania<br>n<br>a)=<br>$<br>Oo 10<br>oO<br>2<br>ToT<br>r=<br>oO<br>3|Z<br>—fo}<br>J<br>oO<br>8 fl I<br>£€ 5<br>3<br>i<br>;SESOEEESNDONDOMVoD|D> GDob D~5 D>© gD*SEKN SESS6BDMEMDBSDOD”SSOPVODOiEDEDiDieD*HaeO_O B.DGeer WOiad _AN“QnVantpOePpAV _AD neNTNNO KOohNS_ AMDl<br>120<br>100<br>a<br>n<br>> 80<br>KR<br>3<br>N<br>®<br>s)<br>5 60<br>5<br>40<br>20<br>rrs,sSs a |<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br><!-- End of picture text -->
<!-- Start of picture text --> 600<br>500<br>400<br>i<br>a<br>n<br>300<br>200<br>100<br>5 4 3 -2 cl () 1 2 3 4 5<br>Discovery in year 0<br><!-- End of picture text --> <!-- Start of picture text --> 600<br>400<br>§<br>5<br>38t<br>£<br>200<br>54 3 -2 cl () 1 2 3 4 5<br>Discovery in year 0<br><!-- End of picture text --> <!-- Start of picture text --> 1500<br>1000<br>ra<br>Fa<br>E<br>=<br>8<br>500<br>0<br>5 4 3 -2 A 0 1 2 3 4 5<br>Discovery in year 0<br><!-- End of picture text --> <!-- Start of picture text --> 600<br>400<br>2<br>ioO<br>alal<br>(G)<br>200<br>5 4 3 -2 cl () 1 2 3 4 5<br>Discovery in year 0<br><!-- End of picture text -->
data covering 120 developing countries, defined as non-high income by the World Bank, i.e. those with an average GDP per capita below 12,055 2010 US dollars, over 1990-2017. This period covers only post-Cold War years, which had a large impact on arms trade patterns (e.g. Akerman and Seim (2014)), and it also covers the oil price boom of the 2000s. More precisely, I estimate the following regression: _Armsit_ = _βDit_ 5 + _αi_ + _σt_ + _εit_ where _Armsit_ is the total imports of arms in country _i_ in year _t_ , and _Dit_ 5 is dummy equal to 1 in the year of the discovery and the five subsequent years. I use a five-year post-discovery period as this is less than the average delay between discovery and extraction (Arezki et al. (2017) puts it at 7 years.). Country fixed effects ( _αi_ ) pick up factors that vary little year-on-year such as governance, year fixed effects ( _σt_ ) pick up global factors such as arms races. As mentioned above, giant discoveries can be thought of as plausibly exogenous due to their unexpected nature (Arezki et al., 2017; Tsui, 2011; Lei and Michaels, 2014; Cavalcanti et al., 2019). This allows me to interpret the _β_ coefficient as a causal effect. I discuss the results in the next section.

# **3 RESULTS**

Baseline results are in Table 1. It shows the effect of giant discoveries on arms imports based on SIPRI data (columns 1-2), COMTRADE data (columns 3-4), and on the arms share of total imports or GDP, based on COMTRADE and World Bank data (columns 5-8). While odd columns give the results of the baseline regression as described in the previous section, even columns show how robust the results are to the inclusion of an additional control variable, i.e. the number of previous giant discoveries. I include this control as a country which experiences a first giant discovery might not react the same way as a country which had many discoveries in the past, as Angola for example (see Figure 1).

I find a positive and statistically significant effect of giant discoveries on arms imports, 12
using both SIPRI and COMTRADE data. The lower bound estimate suggests that in the five years after a giant discovery, arms imports are 25% larger (asinh(0.258)). The arms share of GDP increases by 30%. The arms share of imports also increases, by 17%, though the effect is not statistically significant here. This may be because in some countries total imports increase as well, as the demand for say manufacturing goods increases.<sup>4</sup> Yet the large coefficient suggests that the increase in arms imports is larger than that of total imports on average.

In Table 2 I show the results of estimating the same regressions but on a restricted sample of countries, i.e. those countries which have experienced at least one giant discovery over the period of study. The idea here is that discovery countries in non-post-discovery years provide a more conservative counterfactual than countries without giant discoveries or even without oil exploration, which may be different from discovery countries in terms of arms imports behavior in general. This robustness check confirms the baseline results and even suggests slightly larger effects. The lower bound estimate here suggests that arms imports increase by 31% in the five years following a giant discovery. The coefficient on the arms share of imports is now statistically significant, and suggests that the arms share of total imports increases by around 21%. The effect on arms imports as a share of GDP remains stable around 30%.

In Figure 6 I run the baseline regression of the odd columns in Table 1 but using different post-discovery treatment periods. More precisely, I estimate six different regressions where _Dit_ is a dummy equal to 1 in the year of the discovery only or also in the one, two, three, four or five subsequent years. While I find positive and significant effects across these different specifications, the 5-year period captures the largest effect. This is in line with arms imports occurring not necessarily right after but in any of the post-discovery years, before extraction happens. It is also in line with the event studies in Figure 5 which show arms imports surging mostly in the 3 to 5 years after the discoveries.

> 4Gollin et al. (2016) show that across 116 developing countries, imports of food and manufacturing, as a share of GDP, go up when natural resource exports increase.

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Table 1: Dependent Variable: Arms imports

|(1)<br>SIPRI|(2)<br>SIPRI|(3)<br>(4)<br>COMTRADE<br>COMTRAD|(5)<br>E<br>Import share|(6)<br>Import share|(7)<br>GDP share|(8)<br>GDP share|
|---|---|---|---|---|---|---|
|Discovery in past 5 years<br>0.284<sup>_∗∗_</sup>|0.260<sup>_∗_</sup>|0.398<sup>_∗∗_</sup><br>0.411<sup>_∗∗_</sup>|0.234|0.252|0.297<sup>_∗_</sup>|0.322<sup>_∗_</sup>|
|(0.135)|(0.138)|(0.176)<br>(0.177)|(0.150)|(0.151)|(0.165)|(0.165)|
|Nb of previous discoveries|0.104|-0.049||-0.067||-0.100|
||(0.145)|(0.110)||(0.087)||(0.100)|
|N<br>3408|3408|3392<br>3392|3392|3392|3436|3436|
|R-sq<br>0.71|0.71|0.72<br>0.72|0.42|0.42|0.37|0.37|
|The dependent variable i|s in inv|erse hyperbolic sine.|The regressi|ons include|country|and year|
|fxed efects. Robust sta|ndard|errors in parenthesis,|and * stan|ds for stati|stical sig|nifcance|
|at the 10% level, ** at t|he 5%|level and *** at the|1% percent|level.|||

Table 2: Dependent Variable: Arms imports. Sample: Only discovery countries.

||(1)<br>SIPRI|(2)<br>SIPRI|(3)<br>COMTRADE|(4)<br>COMTRADE|(5)<br>Import share|(6)<br>Import share|(7)<br>GDP share|(8)<br>GDP share|
|---|---|---|---|---|---|---|---|---|
|Discovery in past 5 years|0.327<sup>_∗∗_</sup>|0.344<sup>_∗∗_</sup>|0.397<sup>_∗∗_</sup>|0.425<sup>_∗∗_</sup>|0.233|0.261|0.291<sup>_∗_</sup>|0.324<sup>_∗_</sup>|
||(0.125)|(0.136)|(0.175)|(0.183)|(0.150)|(0.156)|(0.166)|(0.171)|
|Nb of previous discoveries||-0.060||-0.091||-0.095||-0.112|
|||(0.136)||(0.121)||(0.098)||(0.109)|
|N|1338|1338|1280|1280|1280|1280|1289|1289|
|R-sq|0.69|0.69|0.65|0.65|0.30|0.30|0.31|0.32|

The dependent variable is in inverse hyperbolic sine. The regressions include country and year fixed effects. Robust standard errors in parenthesis, and * stands for statistical significance at the 10% level, ** at the 5% level and *** at the 1% percent level.

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<!-- Start of picture text --> SIPRI COMTRADE<br>Discovery year '<br>\ \<br>Discovery year + 1 +——#——4 TO<br>Discovery year + 2 | |<br>Discovery year + 3 po i<br>Discovery year + 4 | | bt<br>Discovery year + 5<br>(1 ' (eS<br>Import share GDP share<br>Discovery year \ \<br>\ \<br>Discovery year + 1 OO -———_s——1<br>Discovery year + 2 | |<br>Discovery year + 3 pa. pp<br>Discovery year + 4 !<br>i i<br>Discovery year + 5 a ee a<br>0 5 1 0 5 1<br><!-- End of picture text -->
In Figure 7 I run the baseline regressions of odd columns in Table 2, i.e. using the sample of only discovery countries, but using dummies based on 200 placebo discoveries instead of the real post-discovery treatment periods. The placebo discoveries are generated by shuffling the discovery dummy either randomly across country-years (column 1 in Figure 7), across countries within years (column 2), or across years within countries (column 3). While I find that among 200 random placebo discoveries, some do have positive effects on arms imports (note that placebo discoveries here may coincide with real discoveries), overall the distribution is centered around zero. The effect of real discoveries, indicated by the dashed vertical line, suggest that we are only likely to find such effects by chance around 90% of the time, confirming the robustness of the results.

In Table 3 I examine how the effect of giant discoveries on arms imports depends on the oil price level. If one reason for the arms transfers is to offset expensive oil, we should expect them to be more likely in times of high oil prices, i.e. around 2007-2014 (see Figure 4). In these times discoveries are also more likely to attract the attention of businesspersons and bankers, as the lure of easy money from arranging large arms transfers is strongest. In these regressions the log of the oil price is centred around $80. The coefficient on the discovery dummy thus gives the effect of a discovery when the price of a barrel of oil is $80, and the coefficient on its interaction with the oil price variable shows how the effect varies with the oil price. The lower bound estimate here suggests that when a barrel costs $80, a giant discovery increases arms imports by 51% and the arms share of imports by 27%. The effect is smaller when the oil price is low and higher when the price is high. The difference in effects across high and low prices is statistically significant. The variation in effects is illustrated in Figure 8. The discovery effect is positive and significant only when the oil price is above $40, and peaks at 65% when the oil price is above $100.

The two bottom panels in Figure 8 show how the discovery effect on arms imports varies with the level of democracy and when the head of the executive is a military office in the importing country. Given the role of governments and corrupt businessmen in facilitating 16
<!-- Start of picture text --> Figure 7. The effect of real vs. placebo discoveries<br>SIPRI<br>3 4 2.5<br>3 2<br>2<br>1.5<br>2 E ffect of real<br>discoveries1<br>1<br>1 .5<br>0 0 0<br>-.4 -.2 0 .2 .4 -.4 -.2 0 .2 .4 -.4 -.2 0 .2 .4 .6<br>COMTRADE<br>2.5 2<br>2<br>2 1.5 1.5<br>1.5<br>1 1<br>1<br>.5 .5 .5<br>0 0 0<br>-.5 0 .5 -.6 -.4 -.2 0 .2 .4 -.5 0 .5<br>Import share<br>2.5 3 2<br>2 1.5<br>2<br>1.5<br>1<br>1 1<br>.5 .5<br>0 0 0<br>-.4 -.2 0 .2 .4 -.4 -.2 0 .2 .4 -.4 -.2 0 .2 .4<br>GDP share<br>3 2.5 2<br>2 1.52 1.5<br>1 1 1<br>.5 .5<br>0 0 0<br>-.4 -.2 0 .2 .4 .6 -.4 -.2 0 .2 .4 -.6 -.4 -.2 0 .2 .4<br>Effect of placebo discoveries Effect of placebo discoveries Effect of placebo discoveries<br>200 shuffles across country-years 200 shuffles within year 200 shuffles within country<br>Density<br>Density<br>Density<br>Density<br><!-- End of picture text --> Notes: The figures show the distributions of the effects of 200 placebo discoveries on arms imports based on SIPRI (row 1), COMTRADE (row 2), on the arms share of imports (row 3), and on the arms share of GDP (row 4). The 200 placebo discoveries are generated by shuffling the discovery variable across country-years (column 1), across countries within years (column 2), and across years within country (column 3). These figures are generated by estimating 200 regressions akin to that of columns 1, 3, 5, and 7 in Table 2, using the sample of only discovery countries. The vertical line shows the effect of real discoveries as reported in columns 1, 3, 5, and 7 in Table 2.

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Table 3: Dependent Variable: Arms imports. Add-on: interaction with the oil price.

|(1)<br>SIPRI|(2)<br>SIPRI|(3)<br>COMTRA|(4)<br>DE<br>COMTRADE|(5)<br>Import share|(6)<br>Import sha|(7)<br>re<br>GDP share|(8)<br><br>GDP share|
|---|---|---|---|---|---|---|---|
|Discovery in past 5 years<br>0.608<sup>_∗∗_</sup>|0.633<sup>_∗∗_</sup>|0.340|0.394|0.188|0.286|0.178|0.288|
|(0.223)|(0.268)|(0.240)|(0.259)|(0.215)|(0.236)|(0.248)|(0.264)|
|_×_ Oil price<br>0.646<sup>_∗_</sup>|0.686|-0.119|-0.033|-0.095|0.065|-0.244|-0.065|
|(0.376)|(0.405)|(0.301)|(0.325)|(0.275)|(0.295)|(0.339)|(0.358)|
|Nb of previous discoveries|-0.019||-0.043||-0.079||-0.088|
||(0.160)||(0.128)||(0.099)||(0.110)|
|N<br>3408|3408|3392|3392|3392|3392|3436|3436|
|R-sq<br>0.72|0.72|0.72|0.72|0.42|0.42|0.37|0.37|
|The dependent variable|is in in|verse hy|perbolic sine.|The log of|oil price|is centere|d around|
|$80.<br>The regressions in|clude|country|and year fxe|d efects.|Robust|standard|errors in|
|parenthesis, and * stand<br>*** at the 1% percent le|s for st<br>vel.|atistical|signifcance at|the 10%|level, **|at the 5%|level and|

such arms deals we could have expected corruption and lack of democratic accountability to accentuate the discovery effect on arms deals. This is not what the results suggest. Instead we find a positive and significant effect only in the most democratic and least corrupt countries (these results are similar using the World Governance Indicators’ measure of corruption). This may be because a minimum threshold of state capacity is required to generate revenues from the oil find. These interactions are not statistically significant however. Hence I do not want to stress these results, especially as there might not be much relevant difference in institutional quality across countries in the sample. As for the role of military heads of state, the results are different whether I use SIPRI or COMTRADE data. The latter suggests that discoveries trigger arms imports only in countries where the head is a military officer, while SIPRI data suggests the opposite. I guess it is cautious not to make any conclusions about the role of military heads here.

In Tables 4 and 5 I check if giant discoveries trigger arms imports from all or only some countries among the major arms exporters. I estimate both the baseline model and the model where the discovery dummy is interacted with the oil price to check for effects when the oil price is high. According to SIPRI data, arms imports from the United States, the Russian Federation, Germany, France, and China account for around 75% of all imports during 18
<!-- Start of picture text --> SIPRI<br>2<br>fe}ta<br>£4<br>no<br>E<br>oO<br>c<<br>co}<br>@© 0<br>co}><br>1s)2<br>To<br>4<br>3<br>Fa<br>wi<br>-2<br>20 40 60 80 100 120<br>Oil price (2017 USD)<br><!-- End of picture text --> <!-- Start of picture text --> SIPRI<br>1<br>t3°<br>£<br>ao<br>E 5<br>oO<br>6<br>on 7. ane<br>38<br>30<br>Ke}<br>8<br>Fa<br>wi<br>-5<br>-10 5 0 5 10<br>Polity IV<br><!-- End of picture text --> <!-- Start of picture text --> SIPRI<br>6<br>t3°a<br>£4 é<br>ao<br>E<br>oO<br>5 2<br>2<br>g<br>Oo 0 °<br>2<br>no}<br>Ke}<br>3 5<br>£<br>wi<br>-4<br>No Yes<br>Military executive<br><!-- End of picture text --> <!-- Start of picture text --> COMTRADE<br>1<br>tfe}a<br>£<br>oa 5<br>E<br>oO<br>c<<br>co}<br>2© 9<br>co}><br>21s)<br>To<br>2-5<br>3<br>Fa<br>wi<br>A<br>20 40 60 80 100 120<br>Oil price (2017 USD)<br><!-- End of picture text --> <!-- Start of picture text --> COMTRADE<br>1<br>t3°<br>£<br>ao<br>E 5<br>oO<br>n<br>6 eee<br>38o<br>3 0<br>Ke}<br>8<br>Fa<br>wi<br>-5<br>-10 5 0 5 10<br>Polity IV<br><!-- End of picture text --> <!-- Start of picture text --> COMTRADE<br>1<br>t3°a<br>£<br>ao<br>E<br>oO<br>5, t<br>2<br>g<br>3<br>2<br>no}<br>Ke} ?<br>oS<br>£<br>Wig<br>No Yes<br>Military executive<br><!-- End of picture text -->
Table 4: Dependent Variable: Arms imports - COMTRADE

||(1)<br>All|(2)<br>USA|(3)<br>RUS|(4)<br>CHN|(5)<br>DEU|(6)<br>FRA|(7)<br>RoW|
|---|---|---|---|---|---|---|---|
|Discovery in past 5 years|0.398<sup>_∗∗_</sup>|-0.062|0.428|0.928<sup>_∗∗_</sup>|0.329|0.333|0.707<sup>_∗∗∗_</sup>|
||(0.176)|(0.286)|(0.513)|(0.425)|(0.432)|(0.530)|(0.245)|
|N|3392|3523|3523|3523|3523|3523|3523|
|R-sq|0.72|0.66|0.57|0.61|0.72|0.64|0.66|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
||All|USA|RUS|CHN|DEU|FRA|RoW|
|Discovery in past 5 years|0.340|-0.016|0.121|1.001<sup>_∗_</sup>|1.126|1.043|0.941<sup>_∗∗∗_</sup>|
||(0.240)|(0.420)|(0.592)|(0.567)|(0.736)|(0.852)|(0.313)|
|_×_ Oil price|-0.119|0.091|-0.610|0.144|1.584<sup>_∗_</sup>|1.409|0.464|
||(0.301)|(0.838)|(0.973)|(0.857)|(0.784)|(0.929)|(0.560)|
|N|3392|3523|3523|3523|3523|3523|3523|
|R-sq|0.72|0.66|0.57|0.61|0.72|0.64|0.66|

The dependent variable is in inverse hyperbolic sine. The regressions include country and year fixed effects. Robust standard errors in parenthesis, and * stands for statistical significance at the 10% level, ** at the 5% level and *** at the 1% percent level.

the whole study period, with a long list of other countries accounting for the remaining 25%. I thus check if discoveries trigger arms imports from these large arms exporters in particular. The first surprising result is that arms imports from the United States, the largest arms exporter in the world, do not react at all to giant discoveries. This is true across specifications and datasets. For other countries the results are different whether we look at COMTRADE or SIPRI data. Using COMTRADE data I find arms imports from China to be particularly responsive to giant discoveries, while arms from France and Germany react strongly to discoveries but only in times of high oil prices. SIPRI data on the other hand suggest that it is Russian arms that react most to giant discoveries. Overall these results suggest that different discovery countries might import arms from different countries.

In Table 6 I look at how different types of discoveries affect arms imports. I focus on three dimensions, i.e. oil vs gas, onshore vs offshore, and by field distance to the nearest border. The top panel shows results when I include separate dummies for oil and gas discoveries 20
Table 5: Dependent Variable: Arms imports - SIPRI

||(1)<br>All|(2)<br>USA|(3)<br>RUS|(4)<br>CHN|(5)<br>DEU|(6)<br>FRA|(7)<br>RoW|
|---|---|---|---|---|---|---|---|
|Discovery in past 5 years|0.284<sup>_∗∗_</sup>|-0.014|0.114|0.001|0.033|0.012|-0.065|
||(0.135)|(0.101)|(0.129)|(0.122)|(0.085)|(0.101)|(0.161)|
|N|3408|3640|3640|3640|3640|3640|3628|
|R-sq|0.71|0.71|0.56|0.60|0.57|0.61|0.58|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
||All|USA|RUS|CHN|DEU|FRA|RoW|
|Discovery in past 5 years|0.608<sup>_∗∗_</sup>|0.058|0.524<sup>_∗∗_</sup>|0.037|0.146|0.086|0.334|
||(0.223)|(0.137)|(0.225)|(0.114)|(0.115)|(0.110)|(0.218)|
|_×_ Oil price|0.646<sup>_∗_</sup>|0.140|0.800<sup>_∗∗_</sup>|0.070|0.219|0.144|0.782<sup>_∗_</sup>|
||(0.376)|(0.223)|(0.321)|(0.158)|(0.135)|(0.128)|(0.394)|
|N|3408|3640|3640|3640|3640|3640|3628|
|R-sq|0.72|0.71|0.57|0.60|0.58|0.61|0.59|

The dependent variable is in inverse hyperbolic sine. The regressions include country and year fixed effects. Robust standard errors in parenthesis, and * stands for statistical significance at the 10% level, ** at the 5% level and *** at the 1% percent level.

on the right-hand side. While the results using SIPRI data suggest that both oil and gas discoveries lead to higher arms imports, the results using COMTRADE data suggest only gas discoveries are associated with larger arms imports. These are not statistically significant results however. This may be because, while both oil and gas discoveries probably can trigger arms imports, identifying their average effects separately requires a larger time period.

The second panel suggests that it might only be offshore discoveries that trigger arms imports. While this result is in line with the Angola and Mozambique anecdotes, it does not appear to be in line with the evidence from Nordvik (2018) who suggested that while onshore oil motivates military build-ups, offshore oil does not. It also goes against the idea that arms imports are to be used to protect onshore fields.

The lower panel looks at how the discoveries’ distance to the border affects their impact on trade. The idea here is based on Caselli et al. (2015) who suggested that bordering countries are more likely to engage in conflict when one of the countries has resources near 21
the border. Countries may thus be more likely to import arms after discoveries closer to the border if the arms imports are linked to potential interstate conflict. Here I interact the discovery dummy with a measure of the country’s discovery’s minimum distance to the border. Since this distance does not exist for countries with no discoveries, I focus only on discovery countries here. Furthermore, since this distance exists only for years with a discovery, I aggregate it at the country level by taking the minimum of the distances of the country’s discoveries. The coefficient on the discovery dummy’s interaction with _MinDist_ therefore captures whether discoveries have a larger effect on arms imports in countries where discoveries are closer to the border. I take the minimum rather than the average distances as only one discovery near the border in a multi-discovery country may have as much effect as one in a single-discovery country. Results suggest no significant interaction however, and COMTRADE data suggest that discoveries far away from borders might have more of an effect, if any at all. Overall there does not seem to be any clear pattern suggesting that certain types of discoveries matter more than others.

# **4 CONCLUSION**

In this paper I have shown that arms imports increase by about 25% in the 5 years after a giant oil discovery. This effect is even larger, at around 51%, if the oil price is high. As a share of GDP, arms imports increase by around 30% in the 5 years after a giant discovery. I suggest that this relationship may be due to newly oil-rich countries wanting to protect their resources from rebels or foreign invasion, or to arms exporters using arms to _offset_ pricey oil, or to oil-dependent countries seeking to secure future oil supply and contain the risk of instabilities in a future oil supplier. I also suggested, based on the anecdotes of Mozambique and Angola, that an alternative mechanism is that shady businessperson exploit these opportunities to siphon debt money made available by the new discoveries.

This paper adds to our understanding of the resource curse by suggesting a new channel 22
Table 6: How the type of discovery affects arms imports

||Oil vs. gas|
|---|---|
|(1)<br>(2)<br>(3)<br>SIPRI<br>SIPRI<br>COMTRADE|(4)<br>(5)<br>(6)<br>(7)<br>(8)<br>COMTRADE<br>Import share<br>Import share<br>GDP share<br>GDP share|
|Oil<br>0.243<br>0.616|0.430<br>0.484|
|(0.307)<br>(0.425)|(0.352)<br>(0.370)|
|Gas<br>0.144|0.284<br>0.174<br>0.211|
|(0.167)|(0.251)<br>(0.209)<br>(0.238)|
|N<br>2359<br>2415<br>2353|2402<br>2353<br>2402<br>2391<br>2432|
|R-sq<br>0.69<br>0.72<br>0.69|0.69<br>0.42<br>0.42<br>0.38<br>0.37|
|On|shore vs. ofshore|
|(1)<br>(2)<br>(3)<br>SIPRI<br>SIPRI<br>COMTRAD|(4)<br>(5)<br>(6)<br>(7)<br>(8)<br>E<br>COMTRADE<br>Import share<br>Import share<br>GDP share<br>GDP share|
|Onshore<br>0.111<br>-0.180<br>(0.269)<br>(0.360)|-0.283<br>-0.209<br>(0.327)<br>(0.351)|
|Ofshore<br>0.245|0.853<sup>_∗∗∗_</sup><br>0.661<sup>_∗∗_</sup><br>0.713<sup>_∗∗_</sup>|
|(0.222)|(0.297)<br>(0.242)<br>(0.260)|
|N<br>2371<br>2430<br>2359|2425<br>2359<br>2425<br>2397<br>2455|
|R-sq<br>0.70<br>0.71<br>0.68|0.70<br>0.41<br>0.42<br>0.37<br>0.38|
|Dista|nce to nearest border|
|(1)|(2)<br>(3)<br>(4)|
|SIPRI|COMTRADE<br>Import share<br>GDP share|
|Discovery<br>1.082|0.682<br>0.661<br>0.422|
|(1.063)|(2.035)<br>(1.743)<br>(1.866)|
|_×_ Min Dist<br>-0.186|-0.060<br>-0.089<br>-0.029|
|(0.212)|(0.378)<br>(0.323)<br>(0.348)|
|N<br>921|874<br>874<br>883|
|R-sq<br>0.69|0.63<br>0.31<br>0.32|

Notes: The dependent variable is in inverse hyperbolic sine. Discovery dummies are for the past 5 years. Min Dist is the log of the country minimum of the country’s discoveries’ minimum distance to the nearest border. Period limited to 1990-2010 due to data availability. The regressions include country and year fixed effects. Robust standard errors in parenthesis, and * stands for statistical significance at the 10% level, ** at the 5% level and *** at the 1% percent level.

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through which resources can hinder development. The purchase of arms may indeed explain why growth in newly oil-rich countries often fails to materialize. Public money could be spent on health, education, and infrastructure instead of arms. This arms-imports effect also adds to our understanding of the relationship between oil and conflict as arms imports may fuel future conflicts (Pamp et al., 2018) as well as political repression (Blanton, 1999). This anti-development spending choice and the consequences on conflict may be an indirect consequence of balance-of-payment preferences in arms exporters (Chan, 1980; Snider, 1984) or of a handful of greedy businesspersons. The import of arms may thus be one reason why oil-rich governments perpetuate the curse of oil. And since SIPRI data on arms transfers are publicly available and updated yearly from a wide range of sources from online news reports to parliamentary records, it provides a potential way of spotting arms import surges early. This is certainly valuable if we think the presource curse plays out over a few years after discoveries.

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