Jumping the Gun: How Dictators Got Ahead of Their Subjects.∗

Jumping the gun: how dictators got ahead of their subjects.∗

Jacob Gerner Hariri†

Asger Mose Wingender‡

December 2019

Abstract

Economic modernization is often seen as a path towards democracy, but the technological progress underlying economic modernization also provides rulers with new means of repression. We collect data on the international diﬀusion of 29 groundbreaking military technologies that can be used to repress internal dissent. The resulting data set covers all independent states in the period 1820-2010 at an annual frequency. We demonstrate that repressive technologies spread faster across borders than civilian technologies, and faster than economic development in general. Coun- tries below the technological frontier have, as a consequence, obtained sophisticated repressive technologies at comparatively early stages of development. We show that the rapid diﬀusion of repressive technologies has delayed democratization across the world by allowing autocratic rulers to suppress popular resistance against their regime.

∗We are grateful to Jeanet Sinding Bentzen, Carl-Johan Dalgaard, Francesco Drago, Casper Worm Hansen, Marc Klemp, and various seminar participants for comments and suggestions. We thank Diego Comin and Marti Mestieri for sharing data on civilian technologies, and Ole L. Frantzen, Kjeld Galster, Karsten Skjold Petersen, Simon Papousek, and Brian Krøjgaard for helping us identify the relevant military technologies. We also thank Kerry Guerin and especially Debbie Smith of the Lithgow Small Arms Factory Museum (Australia) for compiling data on the diffusion of assault rifles for us. Xenia Sofie Heiberg Heurlin, Lukas Krag, and Jonathan Isak Marin have provided excellent research assistance. Hariri gratefully acknowledges financial support from the Sapere Aude Program under the Danish Council for Independent Research (grant no. 6109-00285A). †Department of Political Science, University of Copenhagen, [email protected]. ‡Department of Economics, University of Copenhagen, [email protected]. 1 Introduction

The Industrial Revolution led to sustained improvements in living standards unprece- dented in human history. Beyond the material gains, it also triggered a broad-based modernization of society, something which is often seen as a harbinger of modern democracy (e.g., Lipset 1959). While it may be true that modern democracy rests on foundations laid during the Industrial Revolution, we show in this paper that the Industrial Revolution also had a dark side that has delayed democratization outside Western Europe. With the Industrial Revolution came advances in military technology that made repression cheaper for incumbent rulers. In the first part of this paper, we demonstrate that modern arms have diffused much faster from Europe (and later the United States) to other parts of the world than civilian technologies and societal modernization did. Incumbent rulers outside Europe consequently gained access to powerful means of repression at early stages of development. We then show that the diffusion of modern military technology has impeded democratization by allowing autocratic rulers to suppress resistance against their regimes more effectively and at a lower cost. The basis for our analysis is a new, comprehensive data set on the adoption of military technologies in all independent states in the period 1820-2010. The sample period stretches sufficiently far back in time to capture both the first wave of democratization and the breakthroughs in military technology that followed the Industrial Revolution. In collaboration with experts in the field, we have identified 29 groundbreaking military technologies, used in the sample period, each of which represents a discrete improvements in the capacity to inflict violence. Because of our focus on domestic political institutions, we only include in our sample military technologies that can potentially be used to quell internal dissent. The automatic machine gun is an example of such a technology, whereas the atomic bomb is not. We have collected data on when the technologies were adopted by which states from more than 400 different sources. The final data set contains 5,104 state-technology pairs (176 states x 29 technologies). For 91 percent of the pairs, we know either the exact year in which the technology was adopted by the state, whether it was

1 adopted before our sample period, or whether it was not adopted by 2010. Based on these data, we show that the poorest quartile of countries are two centuries behind Western Europe and North America economically, but less that 50 years behind in terms of arms technology. We proceed to show that more sophisticated military technology impedes democratization. The analysis employs our data set and panel data methods used in recent empirical studies of the determinants of democracy and democratization.1 We include country fixed effects and time fixed effects in all specifications, and control for GDP per capita to account for the modernization hypothesis. Our results show that the conditional probability of a country being a democracy is lower if its military has more advanced arms. The effect is driven by a negative association between military technology and the likelihood of a democratic transition in an autocracy. By contrast, we do not find any effect of military technology on the likelihood of transitions from democracy to autocracy. Why is military technology associated with a lower probability of democratization in autocracies? Our results show that the reason is not external warfare. Wars do affect the likelihood of democratization, especially if a war is lost, but this effect is unrelated to military technology. What matters is that autocratic incumbents use military technology to ward off popular resistance against their regime. Military technology is only associated with a lower probability of democratization when there are popular uprisings, otherwise the association is insignificant and close to zero. Moreover, how successful resistance movements are in obtaining their goals depends negatively on the technological sophistication of the military. We treat this as evidence that the military technologies we study are both used – and useful – to repress the population. While these results are consistent with the theory, we cannot rule out endogeneity bias if, for instance, stable political regimes are more likely to adopt military technology than inherently fragile ones. We therefore re-estimate our baseline regressions using 2SLS and an instrument for technology adoption. Our instrument is based on geographical

1E.g., Acemoglu et al. (2008, 2009), Murtin and Wacziarg (2014), Heid et al. (2012), Barro (2015), and Belloc et al. (2016).

2 diffusion patterns in the spirit of gravity equations, but with the addition of time variation from when the different technologies were invented, and from their speed of diffusion. Instrumented military technology exerts a significant negative effect on the likelihood of democratization. The coefficient is numerically larger than the OLS counterpart, but the difference is statistically insignificant. Because our instrument is based on geography, it is unaffected by political regimes and unrelated to potential confounding variables internal to the country. However, the exclusion restriction might be violated if military technology spreads along the same routes, and with the same time trend, as other factors affecting political regimes. Reassuringly, we do not find evidence of such violation of the exclusion restriction: our results are unchanged when we control for democratic diffusion and international trade possibilities. Our paper is related to the literature on political development in Western Europe, in which the threat of revolution is seen as part of the process eventually leading to modern democracy (e.g., Acemoglu and Robinson, 2006, and Aidt and Jensen, 2014). Whether representation or suffrage were achieved depended on a bargain between the ruler and his subjects. The central result of our paper is that the preconditions for such bargain were fundamentally different outside Western Europe because rulers there had access to sophisticated repressive technologies at earlier stages of development. The ensuing strong bargaining position of rulers relative to that of their subjects is an important explanation for the persistence of autocracy across the world. Or, in the words of Theda Skocpol in her seminal work on social revolutions:

“[M]odern weapons technologies have diﬀused to virtually every sovereign state. National authorities everywhere have wanted the symbolic trappings and coercive support of a modern military establishment. One consequence has surely been to make social revolutions much less likely overall than they might otherwise have been if most new nations lacked modern militaries. In part this is because of the sheer repressive strength of modern military estab- lishments.” (Skocpol 1979, p. 289).

3 The results of this paper are consistent with the predictions of theoretical political economy models. We think of our military technology variable as an empirical counterpart of a parameter governing the productivity of government repression in a theoretical model. In the model of democratization in Acemoglu and Robinson (2005), higher repressive productivity eases the revolutionary constraint on the actions of the incumbent, increases the likelihood of repression, and decreases the likelihood of democratization. In Acemoglu et al. (2010), repression is modeled as the number of soldiers needed to repress the population. If military technology reduces this number by increasing the productivity of each soldier, the model predicts higher returns to being a soldier in a military dictatorship, which increases the likelihood of a democratic transition being blocked by a military coup. In the Grossman (1991) and Grossman (1995) models of insurrections, a higher productivity level of soldiers reduces the likelihood of insurrections and increases the amount of rent-seeking autocratic leaders can engage in. Similar model predictions are found in Grossman and Kim (1996), Skaperdas (2003), and Garﬁnkel and Skaperdas (2007). Our analysis is for the modern period, but our empirical results resonate with how improvements in military technology facilitated political centralization in medieval and early modern Europe. Medieval states were relatively weak. They relied on intermediaries – notably feudal lords – to collect taxes, recruit soldiers, and administer justice. This changed with the advent of modern artillery in the 16th century. Cannons were too costly for a feudal lord, and they became a monopoly of the Crown (Parker 1996, 67f.). Modern artillery also made the castle obsolete, so feudal lords could no longer defend themselves against the Crown. Advances in military technology consequently helped pave the way for absolutism in early modern Europe much in the same way as military technology, according to our results, has strengthened autocratic rulers in the modern period (Downing, 1992). A positive side to military spending, but not to military as such, is emphasized by Tilly (1975), Besley and Persson (2008), Dincecco and Prado (2012), and Gennaioli and Voth (2015), among others. In early modern Europe, states were forced to increase their

4 fiscal capacity to raise resources for military competition with neighbors. High-quality fiscal institutions are persistent, and allow governments to undertake the investments necessary to support economic development (Barro 1990, Besley and Persson 2010, 2013). Moreover, rulers gave increased representation and secure property rights in return for higher taxation to finance wars (North and Weingast 1989, Bates and Lien 1985, Levi 1989, Stasavage 2011). Our results are well in line with this narrative. We show in Section 3 that technological progress in the military sphere reduces the relative price of inflicting violence, so modern governments need to raise far less resources to attain the same capacity for violence as early modern European governments did. Autocrats with access to modern arms are therefore not under the same pressure to increase fiscal capacity or to strike bargains with the population. Moreover, they need fewer soldiers to sustain their regime, making the distribution of rents less costly for a given level of repression. Our paper is also related to the empirical literature on technology diffusion across countries. For instance, Comin et al. (2010) analyze adoption patterns of 24 technologies, some of which are military technologies, in the years 1000 BC, 0 AD, and 1500 AD. Their results show that already in this early period, there is a tendency for military technology to diffuse faster than civilian technologies.2 Comin et al. (2008) and Comin and Mestieri (2018) study the diffusion of a range of civilian technologies (e.g., fertilizer, telephones, and heart surgery) in the same period as we are studying. We demonstrate that military technology diffusion has been faster than the diffusion of the civilian technologies in their sample, at least until the second half of the 20th century. Our paper is structured as follows. In Section 2, we review the historical evolution and diffusion of arms technology. In Section 3, we describe how we measure military technology and our sources and methods for collecting data. We show in Section 4 that military technologies have diffused faster than civilian technologies, and in Section 5 we demonstrate that the fast diffusion of modern arms has delayed democratization. Section 6 concludes. 2Comin et al. (2010), Table 13. In all three years, the standard deviation of their binary adoption variable is smaller for the subset of military technologies than for their entire sample of technologies.

5 2 A brief history of arms diﬀusion

To put the quantitative analysis of this paper into a historical context, we briefly review how arms, and the market for them, have evolved over time. Gunpowder spread from China to Europe in the 13th century, and Europe quickly became the technology leader. Yet, it was not until the invention of the matchlock musket and field artillery in the late fifteenth century that gunpowder became decisive on European battlefields. Casting tech- niques improved in the 17th century, making cannons lighter and more durable, and the flintlock was invented as an alternative to the unreliable matchlock ignition mechanism. The armament of European armies hardly changed over the next two centuries. Soldiers of the British Empire were, for instance, issued with flintlock muskets from the middle of the 17th century. The most famous of these muskets, the Brown Bess, was introduced in 1722 and remained in service until 1838. Then came the Industrial Revolution. Breakthroughs in chemistry led to the invention of the percussion cap ignition mechanism, steel made artillery more powerful and durable, and new precision tools and industrial processes made it possible to manufacture breech- loading rifles and machine guns. In the 20th century, the internal combustion engine and the heavier-than-air flying machine moved military technology in entirely new directions. The Industrial Revolution also radically changed the organization of the arms industry. Before the Industrial Revolution, arms production was largely confined to small arti- sans and government arsenals. In the second half of the 19th century, however, the arms industry became dominated by a small group of private companies such as Colt (rifles and Gatling guns), Krupp (artillery), Mauser (rifles), Maxim-Nordenfeldt (machine guns), Remington (rifles), Schneider (artillery) and Whitworth (artillery). These companies dealt directly with kings and presidents, and they competed intensely for international contracts (Manchester, 1968). Representatives of arms companies toured Europe and Latin America extensively to showcase their goods, helped by innovations in transportation and com- munication. Like other national champions, private arms manufacturers were helped by national governments. France, Germany and the United Kingdom sent military advisers

6 to gain influence in Asian, Latin American, and Balkan countries, and the advisers, in- evitably, recommended armament produced in their home countries. Arms manufacturers also used The World’s Fair and other international exhibitions for promoting new models. The Prussian steel-maker Krupp showcased its revolutionary sliding-block breech-loading cannon for the first time at the 1873 World’s Fair in Vienna, and immediately secured orders from countries as far away as Japan, Siam, and Brazil. Four years later, in 1876, Krupp had more than 20 countries as customers for its artillery.3 By 1914, Krupp supplied artillery to 52 states, many of which ended up on the opposing side in World War I. Alfred Krupp had otherwise assured the Prussian government that he would never “peddle a gun which might some day be turned against Prussia” (Krause, 1995, 59). But in the spirit of 19th century laissez-faire capitalism, governments did little to regulate arms exports. The global arms market became somewhat more regulated after World War I, and even more so during the Cold War. Not that armament became substantially harder to come by: the USSR and the US supplied modern equipment to their allies, and used military support to compete for influence in non-aligned states. France and the United Kingdom supplied arms to their former colonies. And private producers still competed for market shares, despite geopolitical obstacles. There has also been a substantial secondary market for used equipment throughout the period we study. When an army adopts a new standard rifle or a new type of tank, the old ones usually are sold off. As early as the 16th century, obsolete firearms were shipped from Western Europe to Africa and Asia, and exchanged for slaves, spices, and raw materials. Danish traders were especially prolific in this trade, lending their name to the Dane gun widespread in Sub-Saharan Africa until the mid 19th century. When the technical progress in arms manufacturing accelerated in the second half of the 19th century, arms became obsolete faster, and European armies began to adopt new firearms and artillery as often as once per decade. The supply of arms in the secondary market grew rapidly as a consequence, and even poor states could afford relatively recent vintages of rifles and artillery. The supply of arms in the secondary market also expanded in the

3Menne (1938),Manchester (1968), and Krause (1995).

7 aftermath of major wars. German surplus equipment was sold to Latin American countries after World War I, for instance, and Soviet surplus equipment was after World War II sold to other communist states, as well as countries in the Middle East and Africa.4 The ample supply of arms, both new and secondhand arms, was matched by demand for them. From the village chiefs in 19th century Africa asking European travelers for firearms, to leaders of 20th century superpowers, modern arms are objects of desire. They bolster government’s power and prestige, and provide protection against internal rivals and external foes. Arms races ensure that demand is never satiated, and gaining access to the most modern arms technology often has priority over other goals. The combination of high demand and ample supply meant that modern arms diffused quickly from the technological frontier to less developed countries. Consider for instance Ethiopia. In 1896, the Ethiopian army routed an invading Italian force. Italy was not at the forefront of European development, but it was a modern state in the process of indus- trialization. Ethiopia, by contrast, was a feudal society resembling a medieval European kingdom. Despite Ethiopia’s economic backwardness, and despite being landlocked, the Ethiopian army under King Menelik had adopted modern infantry firearms and artillery, bought primarily from France and Russia. These included box magazine rifles, breech- loading artillery, and machine guns – the same types of arms as fielded by the Italian invaders.5 Fast forward one century. With the possible exception of the major cities, little had changed in terms of economic development in Ethiopia since the rule of King Menelik. GDP per capita was probably not much higher, and agriculture still accounted for about 90 percent of employment. Yet, the Ethiopian army at the turn of the Millennium would have marveled the late king Menelik with its fighter aircraft, attack helicopters, tanks, self-propelled artillery, armored fighting vehicles, and assault rifles. Ethiopia’s history is unique in many ways, but not in its access to sophisticated arms at an early level of economic development. On the contrary, Ethiopia is in this regard

4See Bisher (2016) on Latin America, and Krause (1995) on the Soviet sphere of influence. 5Pankhurst (1965), Caulk (1972), and Vandervort (1998). Not all of the Ethiopian soldiers were equipped with modern firearms, but our sources suggests that at least 50,000 men carried magazine rifles, and perhaps as many as 75,000.

8 similar to most countries off the technological frontier. Once a new military technology is invented and made operational by one country, it diffuses swiftly to other countries – including countries that are far less economically developed. Significant disparities did appear in the military sphere from time to time, as exemplified by the scramble for Africa, and the forced openings of China, Korea and Japan by the European great powers in the 19th century. But such examples of significant cross- national divergence in military technology are largely confined to the 19th century, and they are short-lived compared to the international divergence in economic development. At present, even the poorest countries in the world field more advanced (conventional) arms than the United States did during World War II.

3 Measuring military technology

Our focus is arms that governments can use to repress their own citizens, so we concentrate on six types of arms: small arms, machine guns, artillery, tanks, attack aircraft, and combat helicopters. We exclude military technologies that are predominantly used for external purposes, such as submarines, long range missiles, and nuclear weapons. Within the six categories, we collect data on 29 groundbreaking technologies that substantially improved on existing designs. We define a technology as groundbreaking if a) it is qualitatively different from its successor, b) it represents a discrete jump in effectiveness as measured by objective criteria such as the rate of fire, reliability, or range, and c) experts agree that the new technology had a significant impact on military history. An example of a groundbreaking technology is the breech-loading rifle, pioneered by the Prussians in the shape of the Dreyse needle gun. Fast reloading gave it a rate of fire 3-5 times higher than the contemporary muzzle-loading Miniébullet rifle. The breech-loading rifle could also be reloaded in prone position, and thereby reduce the risk of being hit by enemy fire. The Prussian victories against the Danish army in 1864 and the Austrian army in 1866 are often attributed to the superiority of the breech-loading rifles over the muzzle-loaders of Prussia’s adversaries.

9 To get a complete list of groundbreaking technologies, we consulted scholarly works and held repeated meetings with experts on military history and military technology.6 We were forced to drop a few of the resulting technologies because their diffusion is insufficiently documented in the sources we consulted. These technologies are smokeless powder, used in both firearms and artillery, and innovations in artillery shells (e.g., the shrapnel, and shells which exploded on impact). We do not expect these omissions to have any significant impact on our results. The speed of adoption of different technologies is highly correlated within a country, and the diffusion pattern of omitted technologies is likely to resemble the diffusion pattern of the technologies we observe in the data. Table 1 lists the technologies in our data set. A detailed description of the individual technologies can be found in Appendix A. Some of the technologies (the matchlock, the snaphaunce, the flintlock, and field artillery) were invented before our sample begins, but not adopted by all states at the start of our sample period. Figure 1 illustrates, for small arms, how each new technology on our list is an improvement of its predecessor in terms of their range and rate of fire (similar figures for machine guns and artillery can be found in the appendix. An alternative measure of effectiveness applicable to tanks and aircraft is also reported). Reliability and practicality also improved with the arrival of new technology, as exemplified by the percussion lock which greatly reduced misfire and dispensed with the need of carrying spare flints. The technological improvements visible in Figure 1 are productivity shocks: the real price of the most recent vintage infantry firearm has increased at a much slower pace than the lethality of firearms. Panel B of Figure 1 shows that a modern assault rifle only cost the US government two-and-a-half times as much as a flintlock rifle did in the first half of the 19th century. Mosk (2013) provides evidence to the same effect by estimating production costs based on input prices. We show in Appendix A that prices of the other

6Useful books on the history of military technology include Dupuy (1990), Zarzecki (2002), Carman (1955), Manucy (1994), and McNeill (2013). We are grateful to Ole L. Frantzen (military historian and former director of the The Royal Danish Arsenal Museum), Kjeld Galster (military historian and former career soldier), Karsten Skjold Petersen (director of The Royal Danish Arsenal Museum), Simon Papousek (head of the Danish Defence Library), and Brian Krøjgaard (Warrant Sergeant at the R&D Armour branch, Danish Army Combat & Fire Support Center) for helping us identify the relevant military technologies.

10 types of arms in our sample likewise have increased by far less than their lethality over the past two centuries. Scattered evidence collected by Hoﬀman (2011; 2015) indicates that the real cost of arms in Western Europe were falling already before our sample period: between the 13th and the 18th century, prices of small arms and artillery declined relative to prices of other manufactured goods such as spades and lathing nails. Beyond the falling cost of the actual arm, Onorato et al. (2014) show that military innovations in the 20th century have reduced wage costs by reducing the optimal size of armies.

Table 1: List of groundbreaking military technologies

Infantry ﬁrearms Machine guns Artillery Tanks Attack aircraft Combat helicopters

Matchlock musket Hand-cranked Field guns Early tank Early attack aircraft 1st gen. helicopter Snaphaunce Automatic Rifled artillery WWII tank WWII attack aircraft 2nd gen. helicopter Flintlock Steel tubes 1st gen. main battle tank 1st gen. jet fighter Percussion lock Breech-loading 2nd gen. main battle tank 2nd gen. jet fighter Miniébullet rifle Recoil mechanism 3rd gen. main battle tank 3rd gen. jet fighter Breech-loading rifle 4th gen. jet fighter Tubular magazine Box magazine Assault rifle

Figure 1: Eﬀectiveness and price of infantry ﬁrearms

Matchlock musket Flintlock

Snaphaunce Percussionlock Flintlock Minié bullet rifle Percussion lock

Minié Rifle Breechloading rifle

Breechloading rifle Tubular magazine rifle Tubular magazine rifle Box magazine rifle Box magazine rifle

Assault Rifle Assault rifle

0 50 100 150 200 0 100 200 300 400 Geometric mean of range and rate of fire Price (2010 USD) Panel A: Effectiveness Panel B: Price Notes: Panel A: the geometric mean of the range (in meters) and the rate of fire (in shots/minute) for infantry firearms. Data on range and rate of fire are for arms used by the Prussian/German army. Source: Zabecki (2014). Panel B: Prices are for guns procured by the U.S. government. See the online data documentation for sources and methods.

We collect data on when, or if, states started to use each of the groundbreaking technologies, i.e., on technology adoption at the extensive margin. We focus on technology adoption at the extensive margin both because of data availability, and on methodological grounds. Studying technology diﬀusion at the intensive margin would require us to ﬁnd

11 information on, say, the number of machine guns owned by a government in each and every year in our sample period. This is infeasible because the sources rarely mention quantities before well into the 20th century. Studying diffusion of military technologies at the extensive margin does provide us with the information and variation we need for our analysis, however. The qualitative evidence in our sources shows there is a close correspondence between the extensive and the intensive margin of adoption of military technology. The military is a single organization, and the decision to adopt a new arm usually involves replacing obsolete models. Not just because the new arm is better, but also because adopting a standard arm simplifies the logistics of supplying ammunition. Tsarist Russia, for instance, decided to adopt the Moisin-Nagant box magazine rifle in 1891. Five years later, two million copies of the rifle had been produced and all Russian soldiers were equipped with the new arm (Grant, 2007). The pattern is the same for more complex and expensive modern technologies in our sample, including fighter aircraft and tanks. Based on the SIPRI Arms Transfers Database database, we calculate that, on average, it takes five years from a country first purchase a new type of tank or aircraft from abroad until it has received 75% of the units of that technology it will ever buy. In the private sector, by contrast, adoption of new technologies is often piecemeal. A few firms may be early movers, but country-wide adoption happens gradually and takes longer because it is driven by differences in the speed of adoption across a large number of firms.7 The methodological argument for studying arms diffusion at the extensive margin is the following. Adoption of a military technology at extensive margin shows that a government has access to it. How many units of the technology it decides to acquire

7See Stoneman and Battisti (2010) and Comin and Mestieri (2014) for surveys of technology diffusion in the private sector. An informative example is the tractor, which did not fully replace the horse in US agriculture until around 1960 (Manuelli and Seshadri, 2014). By comparison, horses were almost entirely replaced by tanks and trucks in the 1920s, except for deployment in theaters of war where roads were absent, and the terrain too rugged for motorized vehicles. Similarly, windjammers were built and widely used by merchant fleets until the early 1900s, whereas no major naval battles involving sail ships were fought after the Crimean War in 1853. An earlier example, also from the navy, is copper sheathing of ships, which protects wooden hulls from molluscs and crustacea, thereby reducing maintenance and increasing speed. In 1779, the British Navy Board ordered to apply copper sheathing to the first ships of the line, and by 1886 the entire Royal Navy was copper sheathed (Knight, 1973). In comparison, just three percent of the merchant fleet registered by Lloyds of London had copper sheeting in 1786, and only 18 percent did by 1816 (Rees, 1971).

12 depends on the circumstances, and, as with the number of soldiers, may reﬂect weakness rather than strength, or perhaps an imminent external threat. By studying the extensive margin, we thus get a cleaner measure of the technologically driven component of the repressive capacity of the state.

3.1 Data sources

The diffusion of arms after WWII is documented in existing databases, notably the SIPRI Arms Transfers Database and The Military Balance. Zarzecki (2002) has also been an invaluable source. These data sets do not contain information on small arms, however. Evidence is scattered in the interwar period, and even more so before WWI. We have therefore consulted numerous sources to compile a data set on the historical diffusion of military technologies. We get much information from primary historical sources, including declassified reports on foreign military capabilities delivered to the British, German, and American governments in the 19th century, and from various statistical yearbooks, such as Almanach de Gotha (issues 1840-1923), Stateman’s Yearbook (issues 1864-1923), and the League of Nations’ Armament Yearbook (1924-1940). We also obtain evidence from trade registers, from the archives of major arms producing firms (e.g., Krupp and Colt), and from 19th century newspapers and magazines. The scope of our project makes it infeasible to rely on primary sources only, so we supplement them with secondary sources, notably encyclopedias and scholarly works on military history, technology, and trade. In total, we have consulted more than 400 different sources, of which some are yearbooks or other periodicals with multiple issues. A full list of sources can be found in the online data documentation.

3.2 Data collection

We here brieﬂy outline how we collect and process the raw data. More details are provided in the online data documentation. For each of the 29 technologies listed in Table 1, we record whether the technology was used in a given year in a given state. By “used”, we

13 mean that it was part of the armament in the army or other branches of the government. A technology does not have to be universally adopted by the army to be coded in use. If, for instance, an elite corps of soldiers was equipped by breech-loading rifles while the ordinary infantry used muskets, we code the breech-loading rifle as in use. We do, however, have a minimum threshold for considering a technology in use: it should be adopted by units which could conceivably be called upon to fight. So early prototypes used on an experimental basis do not count, neither do test specimens supplied to governments by producers. To be concrete about how we collect data, we use the following four coding rules to determine if a technology is used in a given state in a given year:

1. A technology is coded as in use if sources explicitly state that a part of the armed forces or the police uses the technology, or has used it previously.

2. A technology is coded as in use if sources say that a superior technology within the same category is in use (e.g., we code the ﬂintlock musket as in use if the percussion cap musket is adopted).

3. A technology is coded as not in use if sources explicitly state that it is not currently in use, and if it has never been used, by the armed forces or the police.

4. An observation is set to missing if we do not have any of the information above.

We have more than one source for each observation in most cases. The sources usually agree on when a particular technology was used. On the rare occasions in which sources disagree, we give priority to primary sources, unless a secondary source explicitly states that new evidence has come to light. If it is unclear whether one source is more credible than the other, we record the earliest date at which both agree that a technology was in use (and likewise, the latest date where they agree a technology was not yet used). We set observations in years in which the two sources disagree to missing. Some states collapse in our sample, and either fragment or emerge as a new state. We link such cases using the capital city. If a state fragments into multiple new states, we consider the successor state containing the former capital as a continuation of the old

14 state. For instance, we consider Czechia to be a continuation of Czechoslovakia, whereas we consider Slovakia a new state. We use a similar procedure in the case of consolida- tion, such that Germany becomes a continuation of Prussia in our sample. Through this methodology, we end up with one state for each present country, which makes it possible to compare our data sets to other data sets deﬁned by current borders.

3.3 The ﬁnal data set

Our ﬁnal data set is a panel of 29 technologies observed every year since 1820 in all independent states. We code technology use as a binary variable taking the value one if a technology was in use. The panel consists of 596,443 data points, of which about four percent are missing observations. We can transform this three-dimensional data (state, technology, year) into a data set on when the technologies were adopted in each state. In this transformation of the data set, it contains 5,104 state-technology pairs (176 states x 29 technologies). For 91 percent of the pairs, we either know the exact year in which the technology was adopted by the state, whether it was adopted before our sample period, or whether it was not adopted at the end of our sample period. For the remaining pairs, we know a time interval longer than a year in which the technology must have been adopted. Despite that the interval in most cases is less than two decades, we consider these observations as missing in what follows.

4 The speed of diﬀusion

Figure 2 compares the evolution of average GDP per capita to the average number of groundbreaking military technologies adopted by, respectively, countries in the richest quartile, and countries in the poorest quartile.8 The income level of the poor quartile in 2010 was lower than the income level of the rich quartile in 1820, corresponding to more

8The sample is unbalanced as the number of independent countries has ﬂuctuated over time. The unbalancedness is not driving our results. The pattern is similar if we only include countries that have been independent over the entire period, although the income gap is somewhat smaller because sub-Saharan Africa is excluded.

15 than a 200-year lag in economic development. Poor countries, on average, had adopted 22 of the military technologies in our sample in 2010, something which the rich countries only achieved around 1960. At that time, the average GDP per capita among the rich countries was ten times higher as what the income level among the poor countries would be when they reached 22 technologies. In other words, the poor countries gained access to sophisticated armament at a much lower stage of economic development than the rich countries did. Figure 2 also shows that the gap between rich and poor countries’ adoption of new military technologies seems to have widened in the second half of the 20th century. Al- though the slower diﬀusion may be related to increased regulation of international arms transfers, a more likely explanation is that most of the post-1950 technologies in our sample are new types of tanks and aircraft, which are more expensive on a per-unit basis than the infantry ﬁrearms and the artillery of the 19th century. Moreover, decolonization and fragmentation of existing states mean that more countries are in the process of building an army from scratch.

Figure 2: GDP and military technology, richest and poorest countries 11 25 10 20 9 15 8 10 Avg. log GDP/capita Avg. # of military techs 7 5 6 1800 1850 1900 1950 2000 year

GDP/capita, rich 25% GDP/capita, poor 25% Military techs, rich 25% Military techs, poor 25%

Notes. Average value of GDP per capita and the average number of military technologies in use among countries in the richest and the poorest quartile, respectively. Unbalanced sample of independent countries.

16 GDP per capita is a function of technology use, both at the extensive margin and at the intensive margin, and other factors such as human capital and organizational efficiency. So, in Figure 3, we directly compare technology adoption at the extensive margin in the military to similarly measured technology adoption at the extensive margin in the civilian economy. The data for civilian technologies, taken from Comin and Mestieri (2018), cover technologies used in agriculture, manufacturing, transportation, and telecommunications. The original data set also covers adoption of medical technologies, but only in a limited number of OECD member countries, so we exclude them from Figure 3 to facilitate comparisons across the income spectrum. We follow Comin and Mestieri (2018) and measure the speed of diffusion at the extensive margin by adoption lags, i.e., the number of years since a technology was first adopted anywhere until the year in which the technology is adopted by a given country. Consider the first practical breech-loading rifle, the Dreyse needle gun. It was invented in Prussia, and after some years of testing, the Dreyse was adopted by the Prussian army in 1848. Sweden adopted the breech-loading rifle in 1867, so the Swedish adoption lag for this technology is 19 years. For both the military and the civilian technologies, we plot the average adoption lag across countries against the year when each technology was first used anywhere (Figure 3). The figure shows that average adoption lags for military technologies have no time trend, and mostly fall within 10 to 20 years. The diffusion of civilian technology was slower up to the second half of the 20th century, with adoption lags in the 30-60 year range. There is, however, as also noted by Comin and Mestieri (2014; 2018), a tendency for civilian technologies to diffuse faster over time. The individual technologies in Figure 3 are heterogeneous, and may for that reason alone have different speeds of adoption. High unit costs or transportation costs may, for instance, delay diffusion. However, there are no obvious systematic differences between military technologies and civilian technologies that can explain their different speeds of diffusion, besides that the two types of technologies have different purposes, and that military technologies are adopted by governments rather than firms or households. Infantry firearms are, for instance, relatively inexpensive and require almost no training to use.

17 The same is not true of artillery, tanks, and military aircraft, expensive technologies (per unit) that are not particularly easy to transport or operate. The same heterogeneity is present among the civilian technologies in the Comin and Mestieri (2018) sample, which ranges from cheap, easy-to-use technologies such as fertilizer or cell phones, to expensive, human capital intensive technologies for steel production.

Figure 3: Average Adoption Lags for Military and Civilian Technologies

60 c1c2 c5

c10

c7 c11c12 c6 40

c13

c3 c16 c4 c9 c15 c8 m4 m6 m13 20 m1 Average adoption lag m5 m17 m22 m3 m12c14 m18 m21 c19c18 m7m8 m16 m23m24 m9 m14m15 m19c17m20 m2 m11 m10 0 1800 1850 1900 1950 2000 Year of first use anywhere

Military Civilian

Notes. Adoption lags are the number of years it takes a country to adopt a technology measured from the year the technology was first used (commercially or by a military) anywhere in the world. This definition differs slightly from Comin and Mestieri (2018), who measure adoption lags relative to the date of invention. For instance, instead of using 1903, the year of the Wright brothers’ first flight, as the basis for calculating the adoption lag for commercial aviation, we use 1914, the year of the first scheduled commercial flight. We do so to make the data on civilian technologies comparable to our data on military technologies. By implication, the adoption lags for civilian technologies are shorter than the ones Comin and Mestieri (2018) report. Our data contain 50 more countries than the Comin and Mestieri (2018) data., so to further ensure comparability, we exclude countries not in the Comin and Mestieri (2018) data when calculating adoption lags for military technologies. The resulting sample consists of 126 countries, but the sample sizes for the individual technologies are smaller due to missing observations. The samples for the individual technologies are fairly representative in terms of geographical location and income levels, allowing comparisons across technologies to be made. The civilian technologies from Comin and Mestieri (2018) are: 1) railways (freight), 2) railways (passengers), 3) telegraph, 4) mail, 5) steel, 6) telephone, 7) electricity, 8) cars, 9) trucks, 10) tractors, 11) aviation (mail), 12) aviation (passengers), 13) electric furnace (steel), 14) fertilizer, 15) harvester, 16) synthetic fibers, 17) oxygen furnace, 18) PCs, 19) cell phones.

18 5 Military technology and democratization

In this section, we provide empirical evidence that the quick diﬀusion of military technologies has impeded democratization, particularly in low and middle income countries where economic modernization has not kept pace. The basic framework for the analysis is neatly summarized in Levitsky and Way’s analogy to the tale of the three little pigs:

“... imagine that the pigs are autocratic incumbents, their houses are their regimes, and the wolf represents pro-democracy movements. The wolf huffs and puffs at all three houses, but the impact of his huffing and puffing varies across cases: Whereas the houses of straw and sticks quickly collapse, the house of bricks remains intact. The key to explaining these outcomes lies not in the wolf’s abilities or strategies but in differences in the strength of the houses.” Levitsky and Way (2010, p. 54).

Advances in military technologies correspond to better building materials in the story, making autocrats more resilient to the huﬃng and puﬃng of pro-democracy movements, which are empowered by economic development. In our empirical analysis, we therefore model democracy as the outcome of a race between military and economic development, i.e., by having military technology and GDP per capita as independent variables in our regressions.

5.1 Empirical implementation and data

Our empirical strategy is based on panel data methods widely used in the empirical literature on democracy and democratization. We augment standard empirical models with a simple measure of countries’ military technology sophistication: the number of military technologies a country has adopted in a given year. Formally, this measure is PJ deﬁned as mtechit = j=1 dijt, where dijt is a dummy that equals one whenever country i in year t has adopted technology j, and J is number of technologies in our sample. We code mtechit as missing if we lack information on one or more of the J technologies in the

19 country and year in question. Given that we include year fixed effects in our regressions, mtechit could be thought of as measuring the distance to the technological frontier. The main dependent variable in our analysis is democracy, either in levels, or in first differences corresponding to democratic transitions. Of the many available measures of democracy, only the polity2 index from the Polity IV project (Marshall and Jaggers, 2002) and the dichotomous Boix et al. (2013) indicator extend to the 19th century.9 The two measures of democracy are based on relatively similar definitions of what democracy is. The main difference between them is that the Boix et al. (2013) indicator requires that at least half of adult men has the right to vote, whereas the polity2 index has no such minimum requirement. Unsurprisingly, the two democracy indicators are highly correlated. We use the Boix et al. (2013) indicator in our analysis because it covers more countries, but we obtain similar results using the polity2 index. Our main control variable is GDP per capita (logged) from the Maddison Project Database, version 2018.10 Figure 4 shows the data coverage in our sample period of our three main variables: democracy, military technology, and GDP per capita. With some exceptions in the early period, the Boix et al. (2013) democracy indicator covers all states in existence, so the upward trend in the number of countries with observations is largely a consequence of state formation and decolonization. Data on military technology and GDP do not cover all states in the Boix et al. (2013) data set in the early part of the sample, but after 1950, we have almost complete coverage of both variables. Our baseline sample, the dashed black line in Figure 4, consists of countries and years in which we observe all three variables.

We estimate two types of regressions. In the ﬁrst, we simply regress the democracy indicator on lagged values of our military technology index and log GDP per capita:

9The Boix et al. (2013) data set is an updated and extended version of the Przeworski et al. (2000) democracy data set, which has also been widely used in the literature. We use version 3.0 of the Boix et al. (2013) data set in this paper. 10Bolt et al. (2018). Coverage is patchy in the 19th century, so we linearly interpolate log GDP per capita in years with missing observations. We get similar results if we use GDP data from Penn World Tables (Feenstra et al., 2015) or the Barro-Ursua Macroeconomic Data (Barro and Ursua, 2010).

20 Figure 4: Data coverage 200 150 100 Number of countries 50 0 1800 1850 1900 1950 2000 Year

Democracy data GDP data Military tech data Combined

democracyit = β1mtechit−1 + β2lngdpcit−1 + β3democracyit−1 + αi + δt + uit (1)

Because the Boix et al. (2013) measure of democracy is a dummy variable, the regression is a linear probability model of being democratic. We condition on country fixed effects to eliminate unobserved country characteristics, and on time fixed effects to remove any spurious correlation between military technology and the democracy indicator originating in common time trends. Furthermore, we include lagged values of democracy in the regression to remove the confounding effect of institutional persistence. By including lagged democracy, we are implicitly estimating the effect of military technology on regime changes. Similar regressions (without our military technology index) can be found in Acemoglu et al. (2008, 2009), Boix (2011), Murtin and Wacziarg (2014), and Heid et al. (2012). A lagged dependent variable in a fixed effect model induces Nickell (1981) bias, so we estimate Equation 1 by the system GMM approach of Blundell and Bond (1998) as well as by OLS. The second type of regression we estimate is a linear probability model of democratization:

21 democratizationit = β1mtechit−1 + β2lngdpcit−1 + αi + δt + uit, (2)

where democratizationit is a dummy variable taking the value 1 in years where a country transitions from autocracy to democracy, and zero otherwise. We only include autocracies in the sample, so countries leave the sample after democratization. Equation 2 is analogous to the specification used by Belloc et al. (2016) to analyze transitions to self-government in Italian city-states, except that self-government is an absorbing state in their study. Democracy is evidently not an absorbing state, so we allow countries to enter the sample again if democracy collapses, and include autocracy-spell fixed effects in our baseline regression such that the unit of analysis effectively is an autocratic regime rather than a country. We estimate Equation 1 with a period length of five years, which is the norm in the literature, but we estimate Equation 2 with a period length of one year because it allows us to study short run dynamics around wars, and because some spells of autocracy are shorter than five years. The main results are similar if we use five-year periods when estimating Equation 2. Both empirical specifications suffer from potential endogeneity. Not because autocrats tend to spend more on their military than democratic leaders: we correct for that possibility in Equation 1 by including lagged democracy, and in Equation 2 by including only autocracies in the sample. But endogeneity in the form of reverse causality may be an issue if the timing of technology adoption depends on the strength of the regime. Weak autocrats facing strong opposition in favor of democracy may try to entrench themselves by investing in new military technology, in which case a military build-up may be a sign of an increased probability of regime change. Adoption of new technology may also be a sign of an immediate war, which, if lost, may topple the current regime and replace it with a democracy (e.g., Japan after Word War II). In such cases, we would underestimate the negative effect of military technology on the likelihood of democratization. Conversely, only strong, stable regimes may be able to divert the resources necessary to equip their

22 military with the most modern equipment, in which case we would overestimate the effect of military technology. Ex ante, it is unclear which bias dominates, and consequently how reverse causality affects our results. Another form of endogeneity is omitted variable bias. Perhaps, for instance, a sudden discovery of an oil field allows an autocratic regime to spend more on both military technology and on public goods that keep the public content with the regime. Many of the papers testing the modernization hypothesis face similar endogeneity problems: they have national income on the right-hand side of their regression, but income may also depend on the regime type. As a first step, we follow the modernization literature and deal with possible endogeneity by lagging the explanatory variables, and, in the case of Equation (1), by using internal instruments based on the lag structure in the data. Additionally, in Section 5.4, we construct an external instrument for the military technology index based on the spatial and temporal diffusion of the 29 technologies in our sample. Our instrument is related to gravity-type instruments used in the context of international trade (e.g., Frankel and Romer, 1999), but rather than using geographical distances to estimate trade volumes, we combine geographical distances with the speed at which technologies diffuse to estimate the probability that an invention is adopted by a given country in a given year.

5.2 Main results

In Table 2, we report empirical estimates of Equation 1 in which we regress a dummy for democracy on our military technology index. Column 1 shows the simple relationship between democracy, lagged democracy, and military technology, conditioning on time fixed effects. The point estimate on military technology is insignificant, but the apparent absence of any statistical relationship between democracy and military sophistication is driven by an important omitted variable: national income. Although the diffusion of military technologies is faster than the diffusion of economic development, there is still a tendency for richer countries to be closer to the military frontier than poor countries.

23 Rich countries are also more likely to be democracies, a correlation that is picked up by the point estimate on military technology in column 1. So, as expected, and in accordance with our theory, the point estimate becomes negative and significant when we include log GDP per capita as a control variable in column 2. Consistent with the modernization hypothesis, the estimated coefficient of GDP per capita is positive and significant at the 1%-level. In column 3, we include country fixed effects to remove unobserved country characteristics. The importance of military technology for democracy increases relative to the importance of income as a result.11 To eliminate possible Nickell (1981) bias, we use the system GMM estimator of Blundell and Bond (1998) to estimate the parameters in column 4.12 Again, we find a significant negative association between our (lagged) index of military technology and democracy, so our results in column 3 are not driven by Nickel bias. Moreover, the GMM estimates are less prone to endogeneity bias because the GMM procedure uses further lags of the explanatory variables as internal instruments. The specifications in columns 1-4 implicitly assume that the negative association between military technology and the likelihood of transitions from autocracy to democracy is exactly mirrored by a positive association between military technology and transitions from democracy to autocracy. This assumption turns out to be wrong. In columns 5 and 6, we split the sample into countries that were autocracies in the previous period, and countries that were democracies in the previous period. Our measure of democracy is dichotomous, so these regressions correspond to Equation 2. There is a negative and statistical significant association between lagged military technology and the likelihood of transitioning to democracy (column 5), whereas there is no association between lagged military technology and democratic reversals (column 6). The upshot is that sophisti-

11Including country fixed effects makes the coefficient on military technology statistically significant when GDP/capita is omitted (not shown in the table). We nevertheless include GDP per capita throughout to account for the modernization hypothesis.. 12We use two step system GMM with Windmeijer (2005) standard errors and use second lags as internal instruments for the lagged explanatory variables. We follow Roodman (2009) and collapse the instrument matrix to avoid overfitting of the first stage regression. Nickell (1981) bias is a problem in panels that are short in the time dimension. Our sample is long for the countries that were independent the entire sample period, but the unbalancedness of the panel means that many countries are only present for 30-40 years.

24 Table 2: Military Technology and Democracy

(1) (2) (3) (4) (5) (6)

Dependent variable: Democracy dummy

Military tech. index (lagged) -0.05 -0.58*** -1.29*** -1.33** -1.82*** -0.60 (0.19) (0.20) (0.42) (0.54) (0.49) (1.03) Log GDP per capita (lagged) 0.05*** 0.06** 0.08*** 0.08*** 0.01 (0.01) (0.02) (0.02) (0.01) (0.06) Democracy (lagged) 0.84*** 079*** 0.57*** 0.67*** (0.01) (0.02) (0.03) (0.05)

Estimator OLS OLS OLS GMMa OLS OLS Time FE Y Y Y Y Y Y Country FE N N Y Y Y Y Sample Full Full Full Full Autoc. Democ. Countries 158 158 158 158 131 103 Observations 2,205 2,205 2,205 2,205 1,419 786

Notes: Robust standard errors clustered at the country-level in parentheses. Coefficients and standard errors on military technology are multiplied by 100. All specifications use five-year intervals between observations. aTwo step system GMM with Windmeijer (2005) standard errors and second lags as internal instruments. The instrument matrix is collapsed to avoid over-fitting. ***, **, and * significant at the 1, 5, and 10 percent levels, respectively cated military technology is associated with autocracy in columns 2-4 of Table 2 because it reduces the likelihood of going from autocracy to democracy, and not the other way around. In the remainder of this paper we therefore zoom in on the relationship between military technology and democratization using Equation 2.

Before we do so, however, a small aside on the empirical evidence for the modernization hypothesis is warranted. Acemoglu et al. (2008, 2009) find no link between GDP per capita and democracy in the period 1960-2000 when accounting for country fixed effects. Boix (2011) shows that GDP per capita matters for democracy when the sample period is extended. Heid et al. (2012) show that GDP per capita becomes significant in the Acemoglu et al. (2008) sample when estimating the regressions with system GMM rather than the Arellano and Bond (1991) estimator, which fare poorly in finite samples. We use both longer samples and system GMM, and that explains why we, contrary to Acemoglu et al. (2008, 2009), find significant effects of GDP per capita on democracy. Moreover, military technology is positively correlated with GDP per capita, so by including it in our analysis, we remove a downward omitted variable bias to the estimated effect of GDP per capita. The point estimates on GDP per capita are between a fourth and a third lower

25 when we omit military technology from columns 2-5 in Table 2, corresponding to the size of this omitted variable bias. In Table 3, we estimate the association between military technology and the probability of democratization in autocracies. Column 1 is analogous to column 5 in Table 2, except that we now estimate Equation 2 with annual data. The point estimates are therefore five times smaller than in Table 2, where they represent the probability of democratization within a five-year period. Countries leave the sample after they democratize, but some countries revert back to autocracy and consequently appear in the sample multiple times. In Column 2 of Table 3, we include a fixed effect for each such appearance in the sample, thereby making each spell of autocracy the unit of analysis rather than the country. The additional fixed effects reduce the point estimate on military technology slightly, but it is still negative and statistically significant. The benefit of this change is that we now allow unobserved fundamentals to differ between, say, Imperial Germany and Nazi Germany. The drawback is that we may not have sufficient time variation in military technology to estimate its effect on the likelihood of democratization in short-lived autocracies, effectively reducing the sample size. Adding autocracy spell fixed effects does not change that democratizations in polit- ically volatile countries are over-represented in the sample. Argentina, for instance, has democratized five times, according to the Boix et al. (2013) data. The periods of democracy following the democratizations in 1958, 1963, and 1973 all lasted less than five years before the army intervened and took power. These, and the many other examples of aborted experiments with democracy in our sample, raise the question if the democratically elected regimes are the true holders of power in such cases. So, in column 3, we only consider a country democratic when a democratic regime lasts at least ten years. We refer to transitions that lead to such democracy spells as durable democratizations. The point estimate on military technology does not change much when durable democratization is the outcome, but it is more precisely estimated. We take the analysis one step further in column 4 where we only consider democratizations that have never been reversed. Al-

26 Table 3: Democratization

(1) (2) (3) (4)

Democratization Durable Unreversed Dependent variable: (all) democratization democratization

Military tech. index (lagged) -0.35*** -0.25** -0.26*** -0.36*** (0.11) (0.10) (0.07) (0.08) Log GDP per capita (lagged) 1.28*** 0.92** 1.22*** 0.66* (0.46) (0.44) (0.36) (0.39)

Time FE Y Y Y Y Country FE Y n/a n/a Y Autocracy spell FE N Y Y n/a Observations 7,154 7,154 7,345 7,948 Countries / autocracy spells 132 190 160 132

Notes. Linear probability models with annual data. Coefficients and standard errors are multiplied by 100. In columns 1 and 2, we report the likelihood of democratization in autocracies. After democratization, countries leave sample. In Column 3, we do not count democratic transitions as such if the subsequent period of democracy lasts less than ten years. In column 4, we only count democratic transitions that have not been reversed. Robust standard errors clustered at the country level in parentheses. ***, **, and * significant at the 1, 5, and 10 percent level, respectively. though they may be so in the future, we consider this definition of democratization a proxy for well-entrenched democracy of the sort found in Western Europe. In the case of unreversed democratization, the point estimate on military technology becomes numerically larger. One interpretation of this finding, consistent with the theoretical model in Acemoglu et al. (2010), is that generals with relatively advanced military technology at their disposal may allow experiments with democracy as long as they keep their power and privileges, whereas they will oppose true democratization in which a democratically elected government obtain full control of the armed forces.

In the remainder of our analysis, we use the specification with all democratizations and autocracy-spell fixed effects, reported in Column 2 of Table 3, as baseline. This amounts to a cautious approach, as we find higher, or as high, significance levels in all the regressions we report below if we use any of the three other specifications in Table 3 as baseline. Before turning to why there is a negative association between military technology and democracy, and the question of causality, we first re-estimate our baseline regression in a number of sub-samples. Columns 1-4 of Table 4, respectively, exclude Africa, the Ameri- cas, Asia and Oceania, and Europe from the sample. In all cases, the coefficient of military technology stays negative and significant, although the relationship to democratization is

27 somewhat weaker when the Americas are excluded. This could suggest that the relationship between military technology and political regimes are especially pronounced there, but a simpler explanation is that half of the instances of democratization in our sample are found in the Americas, and omitting them disproportionately reduces the eﬀective sample size. In Column 5, we exclude small arms from the analysis, as they are harder to keep out of rebel hands than artillery, tanks, and aircraft. The point estimate is unchanged compared to our baseline, indicating that while rebels use small arms, autocratic governments on average gain from advances in small arms technology. Otherwise, the point estimate would have increased.

Table 4: Robustness to sampling

(1) (2) (3) (4) (5)

Dependent variable: Democratization

Military tech. index (lagged) -0.30** -0.18* -0.29* -0.22** -0.25** (0.14) (0.10) (0.15) (0.10) (0.11) Log GDP per capita (lagged) 1.62*** 0.77 0.97 0.44 0.93** (0.53) (0.47) (0.54) (0.48) (0.45)

Time FE Y Y Y Y Y Autocracy spell FE Y Y Y Y Y Sub-sample Ex. Africa Ex. Americas Ex. Asia Ex. Europe Ex. small arms Observations 5,160 5,824 4,818 5,687 7,163 Autocracy spells 139 150 142 166 199

Notes. Linear probability models of the likelihood of democratization in a sample of autocracies. Robust standard errors clustered at the country level in parenthesis. Coeﬃcients and standard errors are multiplied by 100. Asia includes Oceania. ***, **, and * signiﬁcant at the 1, 5, and 10 percent level, respectively.

5.3 Mechanisms

Why do we observe a negative association between adopting military technology and the likelihood of democratizing? In this section, we ﬁrst look at external warfare, then we proceed to internal resistance against the regime, and lastly we investigate whether military technology just makes autocracies more stable by reducing the probability that the regime is overthrown irrespective of whether it is replace by a democracy or another form of autocracy.

28 Table 5: Democratization and warfare

(1) (2) (3) (4)

Dependent variable: Democratization

Military tech. index (lagged) -0.27*** -0.25*** -0.25** -0.24** (0.10) (0.10) (0.10) (0.11) Log GDP per capita (lagged) 0.97** 0.95** 0.89* 0.88 (0.45) (0.45) (0.45) (0.62) Interstate war (dummy, lagged)a 1.07 2.61** (0.83) (1.17) Interstate war (dummy)a -2.82*** (0.92) Militarized disputes (dummy, lagged)a -0.24 (0.48) Militarized disputes (dummy)a 0.05 (0.52) Military expenditure/capita (logs, lagged)a -0.16 (0.41) Military personnel/capita (lagged)a -13.65 (41.85)

Time FE Y Y Y Y Autocracy spell FE Y Y Y Y Observations 7,221 7,221 7,221 5,779 Autocracy spells 199 199 199 192

Notes. Linear probability models of the likelihood of democratization in a sample of autocracies. Robust standard errors clustered at the country level in parenthesis. Coeﬃcients and standard errors are multiplied by 100. ***, **, and * signiﬁcant at the 1, 5, and 10 percent level, respectively. aSource: Correlates of War.

5.3.1 Warfare

In Table 5, column 1, we add to our baseline regressions a dummy for whether a country was engaged in external warfare in the previous year. Data on wars are from the Correlates of War v4.0 database.13 The coefficient on lagged warfare is insignificant, and the coefficient on military technology is unchanged. This apparent non-result masks an interesting dynamic: when we in column 2 include a dummy for whether a country currently is engaged in warfare, the coefficient on lagged warfare become significantly positive, whereas the coefficient of current warfare is significant and negative. The two coefficients are of the same magnitude, so being at war does not change the likelihood of democratization, but emerging from a war significantly increases the likelihood of democratization. One reason is that the winners of a war occasionally impose a democratic regime on the losers. West Germany and Japan after World War II are probably the most well-known examples (failed attempts to impose democracy on losers of a war also come to mind). Popular

13Sarkees and Wayman (2010). Wars are deﬁned as armed conﬂict between two (or more) states that involves armed forces on both sides, sustained combat, and more than 1000 fatalities within a year.

29 anger with the war, or with losing the war, may also topple the incumbent ruler, paving the way for democracy. Such anger preceded the democratic transitions in France after the Franco-Prussian War, in Germany after World War I, in Italy after Word War II, and in Serbia after the NATO bombardment. Despite the effect of emerging from a war, the coefficient on military technology is unchanged when we control for warfare in our regressions, indicating that external warfare does not explain why military technology affects the likelihood of democratization. International power struggles may lead to arms races and military tensions short of full-blown wars that may affect the likelihood of regime change. In column 3, we include dummies for current and lagged militarized disputes, as defined by the Correlates of War Militarized Interstate Disputes v4.2 data set.14 Both are insignificant, so it appears that it takes a war before international power struggles topple a regime. In column 4 we control for two measures of resources devoted to the military: the log of military expenditure per capita, and military personnel per capita. Both variables are taken from the Correlates of War National Material Capabilities (v5.0) database.15 Neither the financial resources nor the human labor devoted to the army are associated with the likelihood of democratization. What matters is the technology available to the military.

5.3.2 Popular resistance

In Table 6, we investigate how military technology interacts with popular resistance against the regime. If the military is called upon to repress internal dissent, we should observe that popular resistance against the regime is less successful when the military is more technologically advanced. We obtain data on instances of popular resistance from the NAVCO database.16 The data distinguish between nonviolent and violent resistance. Examples of nonviolent resistance are sit-ins, protests, boycotts, civil disobedience, and

14Palmer et al. (2015). Militarized interstate disputes are deﬁned as threats, display, or use of force short of actual war. 15Singer et al. (1972) and Singer (1988). 16The Nonviolent and Violent Campaigns and Outcomes Data Project, version 2.0. Chenoweth and Lewis (2013).

30 Table 6: Popular resistance

(1) (2) (3) (4) (5) (6)

Success, Frequency, Success, Frequency, Dependent variable: Democratization violent violent nonviolent nonviolent resistance resistance resistance resistance

Military tech. (lagged) -0.31*** -9.92*** -0.91** -1.32 -1.16 (0.12) (3.68) (0.35) (1.20) (0.81) Military tech. (lagged) X no resistance -0.18 (0.12) Military tech.(lagged) X nonviolent resistance 0.02 (0.20) Military tech. (lagged) X violent resistance -1.06* (0.27) Nonviolent resistance (dummy) -0.03 (0.05) Violent resistance (dummy) 0.32** (0.15) Log GDP per capita (lagged) 0.49 1.27** -8.22 -11.10 -5.66 -5.41** (0.54) (0.57) (12.24) (10.73) (4.04) (2.71)

Year FE Y Y N Y N Y Decade FE n/a n/a Y n/a Y n/a Autocracy spell FE Y Y n/a Y n/a Y Observations (country-years) 4,513 4,513 n/a 4,513 n/a 4,513 Observations (instances of resistance) n/a n/a 205 n/a 652 n/a Autocracy spells 172 172 n/a 172 n/a 172

Notes. Linear probability models with annual data. Resistance (violent or nonviolent) is a dummy for whether there has been popular resistance against the current regime in the past three years. Success is defined as the resistance being successful or partly successful in obtaining concessions from the government. Coefficients and standard errors are multiplied by 100. Robust standard errors clustered at the country level in parentheses. ***, **, and * significant at the 1, 5, and 10 percent level, respectively. Source of resistance data: NAVCO version 2.0, Chenoweth and Lewis (2013). strikes. Violent resistance comprises both armed insurgencies and popular protests which have lead to a substantial number of casualties. The database covers years after 1945, so to facilitate comparison, we report the results of our baseline regression estimated in this shorter sample in column 1. In column 2 of Table 6, we add to that regression a dummy for whether there has been violent resistance against the current regime in the past three years, as well as a similar dummy for whether there has been nonviolent resistance against the regime (nonviolent resistance is coded to zero if there is violent resistance). Moreover, we interact the military technology index with dummies for violent resistance, nonviolent resistance, and no resistance. The results show that nonviolent resistance against the regime does not increase the likelihood of democratization. Violent resistance, by contrast, increases the likelihood of democratization by 32 percentage points in countries that possesses none of the military technologies in our sample. The coefficient of the interaction between mili-

31 tary technology and violent resistance is -1.06, meaning that for each additional military technology a country has adopted, the impact of violent resistance falls by 1.06 percentage points. The most advanced autocracies today possess all the 29 military technologies in our sample, which, according to our estimates, reduces to almost zero the likelihood that democratization follows a violent resistance. The aim of a violent resistance is not always democratization, it could also be other kinds of institutional reform, or demand for specific policies such as reinstatement of fuel subsidies. In Table 6, column 3, we change the unit of observation to instances of violent resistance, and estimate the association between our military technology index and the likelihood that a resistance movement, according to the NAVCO coding, successfully obtained its goals.17 The results show that sophisticated military technology is associated with a significantly lower probability of success, suggesting that military technology is a barrier to democratization exactly because the military is used by autocrats to repress dissent. If that is the case, we should also expect violent resistance to be less frequent when the military is equipped with sophisticated arms because people correctly anticipates that the probability of success is small. Indeed, this is what we find in column 4. Here, we return to the country as the unit of analysis, and regress a dummy for violent resistance on our military technology index. The coefficient on military technology is negative and significant, meaning that the frequency of violent resistance is lower in countries with more advanced military arms. A reduction in violent opposition against autocratic regimes amounts to an additional channel through which military technology affects the likelihood of democratization. In columns 5-6, we repeat the analysis from columns 3-4, but for nonviolent resistance. The coefficients are insignificant, but they have the same sign as for violent resistance. While the insignificance could indicate a weaker effect of military technology on nonviolent resistance than on violent resistance, it could also be that the effect on nonviolent resistance is harder to detect because nonviolent resistance that threatens the regime often

17We do not include country and year fixed effects in the regressions because in many years and countries we only observe one, or very few, instances of resistance. Instead, we include decade fixed effects to eliminate potential time trends.

32 Table 7: Regime change in autocracies

(1) (2) (3)

Forced Irregular Autocracy-autocracy Dependent variable: leader exitsa leader exitb regime changec

Military tech. index (lagged) -016 -0.28 -0.27 (0.25) (0.25) (0.26) Log GDP per capita (lagged) -0.60 0.89 -0.71 (1.05) (0.96) (1.10)

Year FE Y Y Y Autocracy spell FE Y Y Y Observations 5,801 6,086 4,129 Autocracy spells 189 193 148

Notes. Linear probability models with annual data. Coeﬃcients and standard errors are multiplied by 100. Robust standard errors clustered at the country level in parentheses. ***, **, and * signiﬁcant at the 1, 5, and 10 percent level, respectively. aSource: Przeworski et al. (2013). bSource: Goemans et al. (2009). cSource: Geddes et al. (2014). turn violent as the regime cracks down on protesters.

5.3.3 Regime stability

In Table 7 we change perspective and ask whether military technology helps autocratic leaders facing other threats to their regimes than democratization. Perhaps military technology just makes autocracies more stable, reducing the probability of democratization as well as the probability of transitions from one autocratic regime to another (e.g, from monarchy to military dictatorship). To test this possibility, we first focus on the removal of autocratic leaders. The PIPE data set by Przeworski et al. (2013) provides data on leaders removed from office by domestic actors using force. The database covers our entire sample period, but slightly fewer countries. The Goemans et al. (2009) Archigos database records the date and manner of exit of 3,400 political leaders across the world since 1875. A leader exit is coded as irregular if the leader was removed involuntarily and against the standing order. In columns 1 and 2 of Table 7, respectively, we use the Przeworski et al. (2013) and Goemans et al. (2009) data sets to estimate the association between military technology and the probability that an autocratic leader is replaced by another autocratic leader by force or other irregular means. Although the point estimates are negative, indicating a lower turnover of autocratic leaders, they are statistically insignificant. A potential ex-

33 planation is that changes of leader do not always mean the end of an autocratic regime: internal coups may replace one leader with another while keeping the underlying institutions in place. To check this possibility, we use data on replacement of one autocratic regime by another from Geddes et al. (2014) in column 3.18 Again we find a negative but insignificant point estimate on military technology. Because of the largely similar samples, the insignificant association between military technology and leader exits in columns 1 and 2 in Table 7 should be compared to our statistically significant baseline estimate for democratization in Table 3. The Geddes et al. (2014) sample only covers the postwar period, so the insignificant coefficient of military technology in column 3 should be compared to the statistically significant estimate for democratization in Table 6, column 1.19 Perhaps this is what we should expect. After all, most of the irregular leader exits in autocracies are orchestrated by the military or internal forces in the regime, and not by popular protest (Svolik 2012, p. 5). Yet, given that the point estimates in Table 7 have the same magnitude as the corresponding point estimate in the case of democratization, we cannot reject that that military technology, on average, affects autocracy-autocracy transitions in the way as it affects democratic transitions. But even if that is the case, the association is statistically much weaker.

5.4 Instrumental variable analysis

In this section, we present the results of our instrumental variable analysis. We use the global diﬀusion patterns of the military technologies in our sample to obtain spatial variation in our instrument, and the diﬀerent timing of the invention of new technologies provides time variation. While our instrument falls short of a clean natural experiment,

18The Geddes et al. (2014) data also contains information on democratization, but the timing of these does not always coincide with the Boix et al. 2013 data we use in our baseline. One reason for the different timing of democratization in the two data sources is that the Geddes et al. 2014 data code provisional governments tasked with handling the democratic transition as a separate class of government. Another reason is that the two sources do not always agree on whether a given regime is a democracy. We use fixed effects based on autocracy spells as coded in the Geddes et al. 2014 in column 3 in which we use data from that source. In columns 1 and 2, we use Boix et al. 2013 to code autocracy spells. 19Alternatively, we can compare the estimate for autocracy-autocracy transitions to that of democratization using the coding of democratization from the Geddes et al. (2014) data. Here we find a point estimate of military technology of -0.28 with a p-value of 0.03.

34 we regard our instrumental variable regressions as a useful supplement to the results presented in the sections above. To see how our instrument works, consider ﬁrst the adoption rate deﬁned as the number of users (countries) of a technology m as a fraction of the N potential users:

N 1 X adoption rate = use m,t N j,m,t j=1 where usej,m,t is a dummy measuring whether technology m is used in country j at time t. Plotted against time, the adoption rate yields the technology diﬀusion curve, sometimes referred to as the s-curve because it often resembles an s.20 The adoption rate is the probability that a randomly chosen country has adopted the technology. While the adoption rate is exogenous to the individual country, it is unsuitable as an instrumental variable because it does not vary across countries. To obtain exogenous variation across countries, we calculate a weighted adoption rate for each country i in which technology use in other countries is weighted by the bilateral distances to account for the fact that cross-border exchange of technology, like trade in goods, is more frequent when countries are geographically close. We also weight by the log of population size because bigger countries are more likely to be the home of arms producers. Mathematically, the weighted adoption rate, as seen from country i, is given by:

  X usej,m,t ln (popj) X distanceij weighted adoption ratei,m,t =   , (3) distancei,j ln (popj) jJi,m jJi,m where distancei,j is the distance between country i and country j measured in kilometers

21 obtained from from the CEPII GeoDist database. Population size, popj, is kept constant such that population growth does not show up as time variation in the measured adoption rate. Ji is the subset of countries that we include in the calculation of the weighted adop-

20See Griliches (1957) for a seminal contribution, and Comin and Mestieri (2014) for a survey of the literature. 21Mayer and Zignago (2011). The database contains multiple measures of bilateral distances. We consider the population weighted distance between countries, calculated as the population-weighted average of bilateral distances between the biggest cities in any two countries, as the most appropriate for our analysis. We obtain similar results using alternative measures of bilateral distances such as distances between captial cities.

35 tion rate for country i. Obviously, Ji should not include country i itself. We also exclude countries adjacent to i because they may decide to adopt new military technologies in response to regime developments in country i.22 If, for instance, country i is an aggressive autocracy, neighbors are likely to adopt military technology as a defensive measure. The regime of country i is unlikely to have a large impact on technology adoption further afield, except perhaps in the cases of the United States and the European great powers (France, Germany/Prussia, Russia/USSR and the United Kingdom). We exclude these countries from our IV regressions for that reason (our results are almost unchanged if we include them), but they are still included when we construct the instrument.23 Lastly, to ensure that the time path of the weighted adoption rate is unaffected by, e.g., decolonization, we balance the sample of countries used to calculate the weighted adoption rate for technology m in country i. The sample of countries used in the calculation of the weighted adoption rate may still vary across technologies and countries, but does not vary over time for a given m and i. Panel A of Figure 5 provides two examples of weighted adoption rates for three countries situated in different parts of the world. As expected, the diffusion patterns have some resemblance to the s-shape often found in the literature on technology diffusion, although the s-shapes are rather compressed due to the rapid diffusion of military technology. The figure also shows that the weighted adoption rate is higher for Denmark than for Peru and Thailand because Denmark is geographically closer to the countries on the military technology frontier. As the unweighted adoption rate, the weighted adoption rate is bounded between 0 and 1, and can be thought of as a probability measure for whether country i has adopted technology n at time t. To construct our instrumental variable for the actual index of military technology in country i, we can simply sum across weighted adoption rates for

22Data on neighbors are from the Correlates of War Direct Contiguity v3.2 data set (Douglas et al., 2002). We define adjacent as having a common border, or being less than 24 miles distant across water. 23Because these countries are excluded from our main analysis, because they are the technology leaders, and because regime changes in their smaller neighbors are unlikely to affect the development of military technology in these countries, we include them in Ji despite being neighbors of country i. This slightly increases the strength of the instrument by improving the first stage fit for countries like Belgium and the Netherlands, but does not otherwise affect our results.

36 all M military technologies:

M X IVi,t = weighted adoption ratei,m,t. (4) m=1

As an illustration of our instrument, Panel B of Figure 5 compares IVi,t to the actual military technology index for Denmark. There is a close fit, suggesting that our instrument is relevant. Because the instrument is based on geography and technology adoption of non-adjacent countries, it is not susceptible to reverse causality in the same way as actual technology adoption. Instead, as it often is the case with instruments based on geography, a violation of the exclusion restriction is the biggest threat to identification. Our instrument follows the same logic as the gravity model for international trade, so the exclusion restriction is violated if trade fosters democratization, and if the time trend in trade coincides with the diffusion of new military technologies. Similarly, the exclusion restriction will be violated if democracy spreads through the same geographical channels as trade, and if waves of democratization coincide with upticks in international trade. To eliminate such potential violations of the exclusion restriction in our 2SLS regressions, we control for trade, proxied by distance-weighted GDP levels of other countries in the spirit of the gravity model, and for distance to democracies. Any static effects of geographical location are accounted for by the autocracy-spell fixed effects in our regressions. Table 8 reports the results of our instrumental variable analysis. The Kleibergen-Paap F-statistic, reported at the bottom of the table, shows that our instrument is reasonably strong across specifications. In column 1, we estimate our baseline regression using 2SLS without controls for violations of the exclusion restriction. The 2SLS estimate of the coefficient of military technology is numerically larger than the OLS counterpart, but so are the standard errors, so the OLS estimate is within the 95% confidence interval of the 2SLS estimate. Both the OLS estimate and the 2SLS estimate are possibly affected by omitted variables, and not necessarily in the same way. Omitted variables that are country- specific, such as a sudden discovery of natural resources, would bias the OLS estimate, but not the 2SLS estimates. Besides the elimination of reverse causality, robustness to

37 Figure 5: Examples of weighted adoption rates and the instrument 1 Automatic machine gun 25 .8 20 .6 First generation

jet fighter 15 .4 Weighted adoption rate 10 Military technology index .2 5 0 1850 1900 1950 2000 1800 1850 1900 1950 2000 Year Year

Denmark Peru Thailand Actual Instrument

Panel A: Weighted adoption rates Panel B: Instrument (Denmark)

Notes: Panel A: Weighted adoption rates, corresponding to Equation (3). Panel B: The instrument for military technology, corresponding to Equation (4), and the actual military technology index for Denmark. country-specific omitted variables may explain why the 2SLS estimate is larger than the OLS estimate. In column 2, we consider a potential violation of the exclusion restriction coming from international trade. We do not have trade data covering the entire sample period, so we proxy for possibilities for international trade by including the distance-weighted GDP of other countries as a control variable. Distance-weighted GDP is essentially a reduced form gravity model: the possibilities of international trade is higher if you are situated close to major economies (using distance-weighted GDP is also preferable on methodological grounds as actual trade is endogenous to regime changes). Distance-weighted GDP is insignificant in our regressions, suggesting that the geographical component of international trade is unimportant for democratization when its effect on GDP per capita is controlled for. The point estimate for military technology is consequently unchanged. In column 3 of Table 8, we control for distance to democracies to allow for the possibility that political regimes spread along the same geographical channels as military technology. The distance to democracies variable is constructed in the same way as we construct distance to users of military technology in Equation 3. Being closer to democracies significantly increases the likelihood of democratization. The point estimates on military technology declines somewhat in magnitude and significance, suggesting that

38 Table 8: Instrumental variable regressions (2SLS)

(1) (2) (3) (4) (5)

Durable Unreversed Dependent variable: Democratization democratization democratization

Military tech. index (lagged) -1.37** -1.38** -1.08* -0.88** -0.69** (0.60) (0.60) (0.57) (0.44) (0.35) Log GDP per capita (lagged) 1.57*** 1.36** 1.37** 1.61*** 1.06** (0.364 (0.61) (0.61) (0.51) (0.46) Distance to economic activity 0.32 -0.44 -0.89 -0.15 (proxy for international trade) (0.95) (0.88) (0.95) (0.67) Distance to democracies 14.48*** 10.06** 4.58 (5.53) (4.34) (3.53)

Time FE Y Y Y Y Y Autocracy spell FE Y Y Y Y Y Observations 6,910 6,910 6,910 7,090 7,561 Autocracy spells 183 183 183 154 128 Kleibergen-Paap 15.40 17.36 13.38 14.92 14.71

Notes. Linear probability models with annual data using the instrumental variable described in the text. Coefficients and standard errors on military technology are multiplied by 100. In columns 1 to 3, we report the likelihood of democratization in autocracies. After democratization, countries leave sample. In column 4, we do not count democratic transitions as such if the subsequent period of democracy lasts less than ten years. In column 5, we only count democratic transitions that have not been reversed. Robust standard errors clustered at the country level in parentheses. ***, **, and * significant at the 1, 5, and 10 percent level, respectively. countries in the vicinity of autocracies both adopt military technology faster and are less likely to become democratic. However, the coefficient remains relatively large and is still close to being significant at the five percent test level (p = 0.57). Moreover, replicating the specification in column 3 with durable democratization (column 4) and unreversed democratization (column 5) as dependent variables, we obtain coefficients of military technology with p-values comfortably below the 5 percent test level. In the case of unreversed democratization, the coefficient on distance to democracies is indistinguishable from zero, indicating that geographical spill-overs of political institutions are a weaker determinant of lasting democracy than military technology. All in all, we find that the evidence in Table 8 favors a causal interpretation of the negative association between military technology and democracy.

39 6 Concluding remarks

We have in this paper shown that military technologies spread faster across borders than other technologies, and much faster than economic development. We have also shown that it matters for domestic politics. Whereas economic modernization is conducive to democratization, access to sophisticated military technology makes it cheaper for autocratic leaders to repress their subjects. Our results offer an explanation for why many countries outside Western Europe and its off-shoots have been modernizing economically for decades without moving toward liberal democracy. The relatively fast diffusion of military technology means that rulers outside Europe had access to powerful means of coercion at earlier stages of development than the Europeans did, and they were – and are – consequently able to withstand democratic pressure following economic modernization. So, even if we do find evidence for the modernization hypothesis, our paper provides a more pessimistic view on future political development than what the modernization hypothesis would suggest: some countries may never become democracies if technology increases the repressive capacity of their rulers faster than economic growth empowers their populations. While our analysis focuses on political institutions, it also shed new light on the Great Divergence, i.e., the rising global inequality from the Industrial Revolution until the end of the 20th century. The arguably most successful export of the European Industrial Revolution was arms, and our results show that the diffusion of modern arms has delayed democratization in developing countries. Papers by Papaioannou and Siourounis (2008), Persson and Tabellini (2009), and Acemoglu et al. (2019) have shown that democracy is conducive to economic growth, so any such delay in democratization would have amplified the divergence of income levels.

40 A Groundbreaking military technologies

This appendix describes the technologies in our data set. The technologies are selected based on our reading of the literature and on discussions with experts in the ﬁeld. For postwar tanks and aircraft, we rely on the classiﬁcation scheme in Zarzecki (2002).

A.1 Qualitative description of technologies

Matchlock musket. First practical hand-held firearm. Gradually evolved from hand cannons towards the end of the 15th century. Origin uncertain, but most likely Italy or Spain. Widely used in Europe from early 16th century. Snaphaunce. Improved firing mechanism that replaced the match with a primitive version of the later flintlock. Reduced misfire, increased rate of fire and made it easier to fire from horseback. Invented in Western Europe in the late 16th century. A similar technology, known as the miquelet lock, was simultaneously invented in Spain, and widely used in the Mediterranean until the 19th century. We code the snaphaunce and the miquelet lock as the same technology. Flintlock. Improved firing mechanism. Reduced misfire and increased rate of fire. Invented in the first quarter of the 17th century, possibly in Normandy. Percussion lock. Improved firing mechanism. Dramatically reduced misfire. Patented by Reverend Alexander Forsyth in 1807 (Scotland, Britain). The separate percussion cap was invented c. 1814. The percussion lock was not adopted by European armies until the 1830s. Minie rifle. The key innovation was the conical bullet with expanding base, which made rifling of firearms practical. Loading of rifles was made easier by greased grooves around the bullets’ base. The base of the bullet was hollow, so upon firing, the skirts of the bullet expanded to fit the rifling. Before the Miniérifle and its revolutionary bullet, loading of rifles was too slow and cumbersome to be practical for regular soldiers. The accuracy and range of rifles were superior to smoothbore muskets, rifles quickly replaced muskets after the invention of the conical bullet. The Miniérifle was patented by Capt

41 Claud-Etienne Miniéof the French army in 1849, and rapidly diffused to other European powers. Breech-loading rifle. The first practical breech-loading rifle was the Dreyse needle gun, invented in Prussia in the late 1830s. It was nominally accepted in service in Prussia in 1841, but not issued to soldiers until 1848. The breech-loading rifle did not diffuse widely until its superiority was demonstrated in Prussia’s military victories against Denmark and Austria in the 1860s. Breech-loading substantially increased the rate of fire, and breech- loading rifles could be loaded in prone position. Tubular magazine rifle. The first repeating rifles, which could fire multiple shots before reloading. Several models were developed independently in the United States. The first successful versions include the Spencer and the Henry rifles, which both appeared in 1860. Box magazine rifle: A more practical repeating rifle. The attachable box magazine facilitated a faster rate of fire. James Paris Lee patented the first box magazine in 1879 in the United States. Similar magazines were developed in the 1880s in Austria (Mannlicher), Norway (Krag-Jorgenson), and Germany (Mauser). Assault rifle. Selective firing rifle, giving a single soldier the fire power of a machine gun. The most notorious example is the AK-47, invented in the Soviet Union. The AK-47, however, builds on the Sturmgewehr 44, developed in Nazi Germany towards the end of World War II, which we regard as the first assault rifle. Hand-cranked machine gun. The first machine gun. The firing mechanism is oper- ated by manually turning a handle. The first hand-cranked machine guns were developed independently in Belgium (Montigny mitrailleuse) and in the United States (The Gatling gun and the Agar “coffee mill” machine gun) in the early 1960s. Automatic machine gun. The automatic firing mechanism increased the rate of fire 3-4 times compared to the hand-cranked versions. The first automatic machine gun was invented by Hiram Maxim in 1884, but did not appear in European armies until around 1890. Smoothbore field gun. The first piece of artillery small enough to move around

42 on the battlefield. Charles VIII of France put the first mobile field artillery into action during his 1494 invasion of Italy. Diffused rapidly in Europe and Asia afterwards, but were practically unknown in most of sub-Saharan Africa until the continent was colonized in the late 19th century. Rifled artillery. Rifling improved range and accuracy, and made it possible to use shells that exploded on impact. The first notable appearance were the guns used in the British bombardment of Sebastopol in the Crimean war. Rifled field guns appeared in the late 1850s in Britain, France and Prussia, and became widespread in the rest of Europe in the 1860s. Steel tubes (artillery). Steel tubes greatly improved the durability of artillery, making larger loads possible. The first steel cannons were brought to the market by the German firm Krupp in 1864. Breech-loading artillery. Breech loading made loading faster and more practical. Breech-loading cannons have been around for centuries, but were generally not very effective due to two problems. An opening in the breech made artillery less durable and reduced the loads that could be fired. Moreover, openings in the breech could not be sealed properly, which reduced muzzle velocity for a given load. Two separate technologies solved these problems. One was the Krupp sliding breech gun presented at the 1873 World’s Fair, and sold to numerous countries afterwards. The other was the interrupted screw, invented by the Frenchman Charles Ragon de Bange in 1877. Recoil mechanism for artillery. Hydraulic mechanism for absorbing recoil. Allowed for a substantially faster rate of fire because the artillery no longer had to be re-aimed before each shot. There were some early attempts to add recoil absorption to artillery in the 1880s and 1890s, but the first practical artillery piece with effective recoil absorption was the French Canone Modele 75 from 1897. Early tank. Developed during World War I. The first tank to appear on battlefields was the British Mark I in 1916, followed by the French FT 17 in 1917, and the German A7V in 1918. WWII era tank. Tanks did evolve somewhat in the interwar period, but the Soviet

43 T-34 medium tank marked a new era of tank warfare. The T-34, introduced in 1940, featured heavy sloped armor, a high-velocity gun and a powerful engine. It outclassed the German tanks at the East front, and prompted both Germany and the major powers on the allied side to develop comparable tanks. We code postwar light tanks in this category as well, as they have similar effectiveness as measured by the SIPRI TIV. First generation main battle tank. Had larger guns, heavier armor and wider tracks than their WWII predecessors. The first of this class of tank was the Soviet T- 54/55, which appeared in 1948. Second generation main battle tank. Had more powerful engines, improved armor, guns, transmission mechanisms, and sophisticated aiming and detection systems. The first tank of the second generation was the American M-60, which appeared in 1960. Third generation main battle tank. Improved armor based on composite materials rather than steel, and advanced fire control systems. The first third generation main battle tank was the West German Leopard 2, which appeared in 1980. Early attack aircraft. The first air attacks were conducted by pilots in small civilian planes lobbing grenades or firing pistols at enemies. Purpose-built attack aircraft arrived shortly after, during World War I. We code early attack aircraft as adopted if a country has either aircraft with onboard machine guns, or heavy bombers. WWII era attack aircraft. A new generation of attack aircraft appeared in the 1930s. Contrary to their predecessors, they were monoplanes, had metal frames, retracting landing gear, and V-12 liquid-cooled engines. These innovations improved speed and range dramatically, and allowed them to carry heavy weaponry. The first aircraft to fulfill these requirements were the Soviet Polikarpov I-16, introduced in 1934. Other notable models include the Messerschmitt Bf109 (Germany, 1937) and the Spitfire (U.K., 1938). First generation jet fighters. First attack aircraft to have jet engines. The first jet fighters to be introduced were the German Messerschmitt Me 262 and the British Gloster Meteor, both introduced in 1944. Note that Zarzecki (2002) does not include these early models in his classification scheme. Second generation jet fighter. Able to maintain supersonic speeds. Improved

44 ground attack capabilities, and air-to-air missiles. The American F-100 was the first to be introduced, in 1954. Third generation jet fighter. Better engines, radars, and navigation systems. The first fighters to feature variable geometry airfoils. The Soviet MiG-21, introduced in 1960, was the first of its generation. Fourth generation jet fighters. Increased maneuverability, improved radars, precision- guided munitions, and improved navigation. The American F-14, introduced in 1972, was the first of its generation. First generation attack helicopters. The first dedicated attack helicopters. The first was the American AH-1, introduced in 1967. Second generation attack helicopters. More powerful engines and weapon systems. Night/all-weather fighting capabilities. The first was the American AH-64, introduced in 1986.

A.2 Eﬀectiveness

Figure 1 shows that successive generations of technology have increased the effectiveness of artillery (Panel A) and machine guns (Panel B). A similar figure for small arms is reported in the main text (1). In all cases, effectiveness is calculated as the geometric mean of effective range and rate of fire. An alternative way of measuring effectiveness is the Total Lethality Index (TLI), developed by the Historical Evaluation and Research Organization (HERO) for the U.S. army. TLI measures how many soldiers a weapon theoretically can kill in an hour under the assumption that soldiers are evenly distributed on a flat surface with a density of one per four square feet. The calculation is based on the rate of fire, targets per shot, the survival probability when hit, range, accuracy, and reliability. For aircraft and tanks, the calculation also involves speed and armor. See HERO (1964) for further details. Table A1 shows TLIs for the subset of the historical technologies in our sample for which HERO (1964) provides data. In addition, the table shows TLIs calculated by Dupuy (1990) under slightly different assumptions. The two data

45 sources agree that each of the technologies represent a substantial increase in lethality of a particular type of weapon. Using the same methodology, Hogg (1993) computes TLIs for a number of speciﬁc models of aircraft and tanks from the postwar period. We average across the models belonging in each of the generations of weapons we have in our technology sample. The results are reported in Table A2. Again, we observe substantial increases in lethality.

A.3 Prices

The price data for small arms reported in Figure 1 in the main text is an expectation in the sense that we do not have similarly consistent data on prices for artillery, aircraft and tanks. Instead, we have to rely on scattered information summarized below. The underlying data are described in the online data documentation. Artillery: Our sources show that only steel tubes substantially increased cost per artillery piece, perhaps by as much as fifty percent (e.g., Holley, 1865). However, steel also increased the durability of artillery, so the real price increase is smaller if measured by the price per shot. Rifled artillery is no more expensive to make than smooth bore artillery, and smooth bore artillery can be converted to rifled artillery at very low costs. For instance, during the American Civil War, The Union converted smoothbores for 1.87- 2.50 dollars at a time when new artillery pieces cost several hundred dollars (Holley, 1865). The transition to breech loading did not increase cost, either. The accounting books of Krupp shows that the revenue per gun sold did not increase in the 1870s, during the period when the firm transitioned to producing breechloaders (calculations based on Kirchner, 1982). It is harder to assess prices of recoil-less artillery compared to older breechloaders. In the early 1900s, when the technology was brought to market, individual artillery pieces were bundled with shells, ammunition wagons, harnesses for horses, spare parts, and so on when sold by the arms manufacturers. The bundles vary from deal to deal, and the observed amount paid for an order of a given number of artillery pieces therefore varies wildly in the data we have collected. It has not been possible to calculate prices during

46 the transition to recoilless artillery. However, none of our sources mentions that recoilless artillery were more expensive to produce than the earlier generation of breechloaders. Machine guns: Our sources show that the U.S. government paid less for automatic machine guns in 1919 than it paid for Gatling guns in the 1860s and 1870s. However, European governments appears, on average, to have paid slightly more for automatic machine guns around year 1900, when the technology was relatively new, than they did for Gatling guns in the 1860s and 1870s. Fighters: The cost of producing ﬁghter during World War I appears to have been about one fourth of the cost in World War II in the UK and in Germany, and the introduction of jet engines immediately after World War II also appears to have increased production costs. For the Cold War period, we rely on the SIPRI Trend Indicator Value (TIV), reproduced in Table A2. The TIV is an estimate of production cost assigned to individual models of a ﬁghter (E.g., the F-16). We average across all models in each generation. Helicopters: See the TIVs reproduced in Table A2. Tanks: Our sources suggests that the UK and US governments paid the same for a tank during World War I than during World War II. For the postwar generations of main battle tanks, we rely on the TIVs reproduced in Table A2.

A.4 Lethality at lower prices

Our effectiveness measures and price data are not sufficiently comparable across time and types of technologies that we can construct a general index of price adjusted effectiveness. However, we do see a strong tendency of what Carl Mosk (2013) calls lethality at lower prices. Not that unit prices have fallen in real terms, in general they have been increasing over time. But the evidence presented in the two previous subsections of this appendix shows that their lethality has increased at a faster rate than prices, and often faster by an order of magnitude. The financial cost of inflicting violence has therefore fallen. Additionally, fewer men are needed to inflict the same amount of violence, reducing cost

47 Field gun

Hand−cranked Rifled artiller

Steel tubes

Breechloading Automatic

Recoil mechanism

0 50 100 150 200 0 500 1,000 Geometric mean of range and rate of fire Geometric mean of range and rate of fire

Panel A: Artillery Panel B: Machine guns

Figure 1: Effectiveness Notes: The figure shows the geometric mean of the range (in metres) and the rate of fire (in shots/minute) for infantry firearms. Data on range and rate of fire are for arms used by the Prussian/German army. Source: Zabecki (2014). even further. There are exceptions to this rule. The column labeled TLI/TIV in Table A2 shows that third generation main battle tanks appears to be more expensive than the previous generations even when accounting for their increased lethality. One reason is that third generation MBTs were never mass produced in the same way as their predecessors, and cost overruns were rampant in their development. See the special issue of Defense and Peace Economics on defense inflation (Hartley et al. 2016) for a discussion of these points. The rather modest rise in TLI/TIV associated with the fourth generation fighter aircraft has a similar explanation. However, these relatively new technologies are not widespread in our sample, and our results are robust to leaving them out. One should also keep in mind that the TLI only measures certain aspects of lethality. The TLI does not include maneuverability, computer guided munitions, and other aspects that are clearly relevant for modern tanks and fighter aircraft.

48 Table A1: Total Lethality Index (TLI)

TLI (HERO, 1964) TLI (Dupuy, 1990) Small arms: Matchlock musket 19 19 Flintlock musket 49 43 Miniébullet rifle 154 102 Breech-loading rifle 229 153 Box magazine rifle 777 495

Artillery: Field gun (Gribeauval) 3,982 940 Recoilless artillery 349,834 386,530

Aircraft: WWI ﬁghter 229,480 31,909 WWII ﬁghter 3,037,900 1,245,789

Tanks: WWI tank 69,496 34,634 WWII tank 2,300,299 935,458

Notes: TLI is the total lethality index. See text for an explanation.

Table A2: Total Lethality Index (TLI) and SIPRI estimated costs

TLI (Hogg, 1993) Cost (SIPRI TIV) TLI/TIV Aircraft: Third gen. jet ﬁghter 1,542,283 13.6 113,061 Fourth gen. jet ﬁghter 3,693,889 26.3 140,645

Helicopters: First gen. attack helicopter 400,000 5.2 76,923 Second gen. attack helicopter 1,473,000 14 105,214

Tanks: First gen. MBT 1,862,604 1.3 1,463,736 Second gen. MBT 3,735,903 2.0 1,873,572 Third gen MBT. 5,219,390 3.4 1,529,492

Notes: TLI is the total lethality index, and TIV is the SIPRI trend indicator value. See text for an explanation of these concepts.

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