Appendix A. Historical Trends in Ballistic Missile Proliferation

1960s: States with Ballistic Missiles

1970s: States with Ballistic Missiles

1980s: States with Ballistic Missiles

1990s: States with Ballistic Missiles

2000s: States with Ballistic Missiles

2010s: States with Ballistic Missiles

Country Missile 1st Tested IOC Source Retired Range (km) CEP (m) Afghanistan SS-1C (Scud-B; R-17; R-300) 1988 1988 SIPRI/TMB (2012) 2012 300 450 Afghanistan Frog-7b (500mm) 1988 1988 SIPRI/TMB (2012) 2012 70 400 Algeria SS-26 Stone (Iskander E) 2017 2017 TMB; SIPRI 280 70 Algeria Frog-7b (500mm) 1975 1975 SIPRI/TMB; nti.org 1982 70 400 Algeria Frog-3 (540mm) 1970 1970 SIPRI/TMB 1982 61 800 Argentina Condor II N/A N/Anti.org; globalsecurity.org; Carus1993 890 800 Argentina Alacran 1986nti.org; globalsecurity.org; Mistry & GopalaswamyND 1993 150 Argentina Condor I N/A N/Anti.org; globalsecurity.org; 1993 150 Armenia SS-1C (Scud-B; R-17; R-300) 1996 1996 SIPRI; TMB (1996) 300 450 Armenia SS-26 Stone (Iskander E) SIPRI; military-today.com; nationalinterest.com; TMB (2017)2016 2016 280 70 Armenia SS-21 Scarab B (Tochka U) 2013 2013TMB (2017, 2013); nti.org 120 95 Armenia Frog-7b (500mm) 1960 1960 TMB 1991 70 400 Azerbaijan SS-21 Scarab B (Tochka U) 2009 2009 TMB (2009, 2018) 120 95 Azerbaijan LORA (650mm) 2018 2018 eurasianet.org 300 10 Bahrain ATACMS Block 1A 2013 2013 SIPRI 300 10 Bahrain ATACMS Block 1 2002 2002 SIPRI 165 50 Belarus SS-25 Sickle 1992 1992 TMB 1992 10500 210 Belarus SS-1C (Scud-B; R-17; R-300) 1992 1992 TMB 300 450 Belarus SS-21 Scarab B (Tochka U) 1992 1992 TMB 120 95 Belarus Frog-7b (500mm) 1992 1992 TMB 2018 70 400 Belgium Lance (560mm) 1977 1977 TMB 1992 121 150 Belgium Honest John (M50) (760mm) 1962 1962 TMB 1978 48 230 Brazil VLS 1993 N/A Mistry & Gopalaswamy N/A 1000 Brazil ASTROS II SS-80 (300mm) 1983Rocket Artillery Reference Book 90 Brazil ASTROS II SS-60 (300mm) 1983Rocket Artillery Reference Book 60 Brazil Sonda-series 1965 N/A Mistry & Gopalaswamy N/A Brazil MB/EE-150, -350, -600, -1000 N/A N/A Mistry & Gopalaswamy N/A Bulgaria SS-23 (Spider) SIPRI; https://www.armscontrol.org/act/2002_07-08/bulgariajul_aug02; revolvy.com1986 1986 2002 480 70 Bulgaria SS-1C (Scud-B; R-17; R-300)SIPRI/TMB; https://www.armscontrol.org/act/2002_07-08/bulgariajul_aug021991 1991 2002 300 450 Bulgaria Frog-7a/b (500mm) SIPRI/TMB; https://www.armscontrol.org/act/2002_07-08/bulgariajul_aug021991 1991 2002 70 400 Bulgaria SS-1C (Scud-B; R-17; R-300) 1971 1971 SIPRI/TMB 1991 300 450 Bulgaria SS-1B (Scud-A; R-11; Elbrus) 1961 1961 SIPRI/TMB 1991 270 5160 Bulgaria Frog-7a/b (500mm) 1960 1960 SIPRI/TMB 1991 70 400 Canada Honest John (M50) (760mm) 1960 1960 TMB 1970 48 230 China 1059 (DF-1) 1960 1961 ausairpower.net; astronautix.com1975 590 2780 China DF-2 (CSS-1) 1962 1970 ausairpower.net; astronautix.com1979 1250 2780 China DF-3 (CSS-2) 1966 1971 ausairpower.net; astronautix.com; DOD report2002 2650 870 China DF-4 (CSS-3) 1969 1981 ausairpower.net; astronautix.com 4760 1190 China DF-5 (CSS-4) 1971 1981 ausairpower.net; astronautix.com 12000 800 China JL-1 (CSS-NX-3) 1981 1986 fas.org; nti.org; 2016 1000 700 China DF-21 (CSS-5) 1985 1991 ausairpower.net; astronautix.com; missilethreat.csis.org2150 700 China DF-15 (CSS-6/M-9) 1988 1990 ausairpower.net; astronautix.com 600 3200 China DF-11 (CSS-7/M-11) 1990 1992 ausairpower.net; astronautix.com 280 600 China DF-21A (CSS-5) 1991 1996 missilethreat.csis.org 1770 50 China DF-5A (CSS-4) 1993 1981 ausairpower.net; astronautix.com 13030 440 China DF-11A (CSS-7/M-11) 1997 1999 ausairpower.net; astronautix.com 600 150 China DF-31A (CSS-10) 1999 2007 ausairpower.net; astronautix.com 11000 150 China DF-31 (CSS-10) 1999 2006 ausairpower.net; astronautix.com; missilethreat.csis.org7900 300 China JL-2 (CSS-NX-4) 2002 2015 fas.org; nti.org; 8000 300 China DF-15B (CSS-6) 2003 2006 ausairpower.net; astronautix.com 800 10 China DF-15C (CSS-6) 2007 2007 ausairpower.net; astronautix.com 700 20 China SY-400 2008 military-today.com 300 50 China DF-21C (CSS-5) 2013 2006 missilethreat.csis.org 2150 50 China DF-21D (CSS-5) 2013 2006 missilethreat.csis.org 1550 20 China DF-41 (CSS-X-20) 2014 N/A ausairpower.net; astronautix.com 15000 100-500 China DF-5C 2017 N/A ausairpower.net; astronautix.com 13000 500 China DF-26 2017 2015 missilethreat.csis.org 4000 450 China DF-5B (CSS-4) N/A 2015 ausairpower.net; astronautix.com 12000 300 China DF-16 (CSS-11) 2011 missilethreat.csis.org 1000 10 China DF-15A (CSS-6) 1996 ausairpower.net; astronautix.com 900 45 China M-7 (CSS-8) 1986 ausairpower.net; astronautix.com; Kan CRS Report180 China B611 (CSS-11) 2004 ausairpower.net; astronautix.com 280 30-150 China DF-12 (M20) 2016 ausairpower.net; astronautix.com 280 30-70 Cuba SS-5 (Skean; R-14) 1962 1962 TMB; nti.org 1962 3700 1130 Cuba SS-4 (Sandal; R-12) 1962 1962 TMB; nti.org 1962 1500 5160 Cuba Frog-3/4/5 (540mm) 1961 1961 TMB; nti.org 1962 61 800 Cuba Frog-3 (540mm) 1962 1962 TMB; nti.org 1990 61 800 Czech Republic SS-1C (Scud-B; R-17; R-300) 1993 1993 TMB 1998 300 450 Czech Republic SS-21 Scarab B (Tochka U) 1993 1993 TMB 2005 120 95 Czech Republic Frog-7b (500mm) 1993 1993 TMB 2005 70 400 Czechoslovakia SS-23 (Spider) 1985 1985 SIPRI 1992 480 70 Czechoslovakia SS-1C (Scud-B; R-17; R-300) 1964 1964 SIPRI/TMB 1991 300 450 Czechoslovakia SS-1C (Scud-B; R-17; R-300) 1992 1992 SIPRI/TMB 1992 300 450 Czechoslovakia SS-1B (Scud-A; R-11; Elbrus) 1961 1961 SIPRI/TMB 1970 270 5160 Czechoslovakia SS-21 Scarab B (Tochka U) 1985 1985 SIPRI 1992 120 95 Czechoslovakia Frog-7a/b (500mm) 1963 1963 SIPRI/TMB 1991 70 400 Czechoslovakia Frog-7b (500mm) 1992 1992 TMB 1992 70 400 Denmark Honest John (M50) (760mm) 1961 1961 TMB 1977 48 230 East Germany SS-23 (Spider) 1985 1985 SIPRI 1991 480 70 East Germany SS-1C (Scud-B; R-17; R-300) 1964 1964 SIPRI/TMB 1991 300 450 East Germany SS-1B (Scud-A; R-11; Elbrus) 1962 1962 SIPRI 1991 270 5160 East Germany SS-21 Scarab B (Tochka U) 1983 1983 SIPRI 1991 120 95 East Germany Frog-7a/b (500mm) 1960 1960 TMB 1981 70 400 Egypt Al Kahir N/A N/A Mistry & Gopalaswamy 1969 600 Egypt Project T (Scud-B) 1988 1996 TMB; nti.org; NIE 5-91C 450 450 Egypt Al Zafir N/A N/A Mistry & Gopalaswamy 1969 370 Egypt SS-1C (Scud-B; R-17; R-300) 1973 1973 SIPRI/TMB 300 450 Egypt Sakr-30/80 (122mm-325mm) 1984 TMB; fas.org; Jane's Information Group 19942005 80 Egypt Frog-7b (500mm) 1971 1971 TMB 70 400 Egypt Frog-3 (540mm) 1968 1968 TMB 1972 61 800 France M-51 (SLBM) 2004 2010 fas.org 14000 350 France M-5 (SLBM) N/A ND fas.org ND 11000 350 France M-45 (SLBM) 1995 1997 missilethreat.csis.org 6000 500 France M-4B (SLBM) 1980 1985 fas.org 2010 5000 500 France M-4A (SLBM) 1980 1985 TMB; fas.org 2010 4000 500 France SSBS SS3 (IRBM) 1975 1982 revolvy.com; fas.org 1999 3500 France M2 (SLBM) 1973 1974 fas.org 1978 3200 France SSBS SS2 (IRBM) 1971 1971 astronautix.com 1984 3000 France M1 (SLBM) 1971 1971fas.org; missilethreat.csis.org1975 3000 5 France M-20 (SLBM) 1974 1977 fas.org 1991 3000 1000 France SSBS SS1 (IRBM) 1965 ND astronautix.com ND 2900 1000 France Hades 1988 NDrevolvy.com; astronautix.com1997 480 100 France Corporal 1955 1955 SIPRI 1966 121 France Pluton 1965 1974 missilethreat.csis.org 1993 120 150 France Honest John (M50) (760mm) 1961 1961 TMB 1974 48 230 Greece ATACMS Block 1 1998 1998 SIPRI 165 50 Greece Honest John (M50) (760mm) 1960 1960 TMB 1993 48 230 Hungary SS-1C (Scud-B; R-17; R-300) 1967 1967 SIPRI/TMB 1991 300 450 Hungary SS-1B (Scud-A; R-11; Elbrus) 1962 1962 SIPRI/TMB 1970 270 5160 Hungary Frog-7b (500mm) 1974 1974 TMB 1991 70 400 Hungary Frog-3 (540mm) 1960 1960 TMB 1991 61 800 Hungary SS-21 Scarab B (Tochka U) 1987 1987 SIPRI 1991 120 95 India SLV-3 1979 astronautix.com; Mistry & GopalaswamyN/A 1983 900 India Prithvi-I 1988military-today.com; missilethreat.csis.org; fas.org1994 150 300 India Agni-TD Carus; indiatoday.in; astronautix.com; Wisconsin Project1989 ND 1994 1200 India PSLV 1993 N/A Mistry & Gopalaswamy N/A 900 India Prithvi-II 1996military-today.com; missilethreat.csis.org; fas.org1996 250 500 India Agni-II 1999military-today.com; missilethreat.csis.org; fas.org2011 3500 40 India Danush (SLBM) 2000military-today.com; missilethreat.csis.org; fas.org2010 400 50 India GSLV 2001 N/A Mistry & Gopalaswamy N/A 36000 India Agni-I 2002military-today.com; missilethreat.csis.org; fas.org2004 1200 50 India Sagarika (SLBM) 2004 military-today.com; missilethreat.csis.orgN/A 750 India Agni-III 2006military-today.com; missilethreat.csis.org; fas.org2014 3200 40 India Shaurya (SLBM) 2008 military-today.com; missilethreat.csis.orgN/A 3500 20-30 India Agni-IV 2014military-today.com; missilethreat.csis.org; fas.orgN/A 4000 80 India Agni-V 2015 military-today.com; timesofindia.comN/A 8000 10 India Prahaar 2016military-today.com; missilethreat.csis.org; fas.orgN/A 150 10 India Prithvi-III military-today.com; missilethreat.csis.org; fas.orgN/A 350 75 Iran Shahab-1 (hwasong-5) 1985fas.org; missilethreat.csis.org; astronautix.com1985 330 450 Iran SS-1C (Scud-B; R-17; R-300) 1985 1985 fas.org; TMB; SIPRI 300 450 Iran Oghab (230 mm) 1985 1985 TMB; Mistry 40 Iran Tondor 69 (CSS-8; M-7) 1990 1990SIPRI; TMB; Kan CRS Report 180 Iran Scud-C (Hwasong 6) 1991 1991 SIPRI 500 700 Iran Shahab-3 (Zelzal-3) 1998fas.org; missilethreat.csis.org; astronautix.com2003 1300 2500 Iran Shahab-2/Scud-C 1998 1997fas.org; missilethreat.csis.org 500 700 Iran Fateh-110 2001 missilethreat.csis.org; nti.org; upenn2004 210 100 Iran Mushak-160 (Nazeat-10) (355mm)2001 1988 Mistry; nti.org 160 700 Iran Fateh-110B (Fateh-3) 2002 2010missilethreat.csis.org; upenn 300 250 Iran Fateh-100A (Fateh-2) 2002 2002 nti.org; upenn 200 100 Iran Ghadr 1 (Qadr) 2004 missilethreat.csis.org; astronautix.org2007 1950 300 Iran Ghadr 2004 2007 missilethreat.csis.org 1950 300 Iran Khoramshahr *good example why not to use FFT2007 N/A missilethreat.csis.org 2000 1500 Iran Ashura (Sejjil-1) 2008 2012 missilethreat.csis.org 2000 Iran Qiam-1 2010 missilethreat.csis.org; astronautix.org2017 800 500 Iran Emad-1 2015 missilethreat.csis.org; astronautix.org2015 1700 500 Iran Fateh-313 (Badr-313) 2015Cordesman CSIS 2014 Report; washingtoninstitute.org2015 500 100 Iran Zolfaghar missilethreat.csis.org; washingtoninstitute.org; popularmechanics.com2016 2017 700 70 Iran Zelzal 2 2007Cordesman CSIS 2014 Report 200 Iran Zelzal 1 nti.org; Cordesman CSIS 2014 Report2007 125 500-1000 Iran Mushak-120 Mistry; nti.org; Cordesman Sept 20001986 120 Iran Fajr-5 (333mm) 1989Cordesman CSIS 2014 Report 80 Iran Fajr-3 (240mm) TMB; Cordesman CSIS 2014 Report1988 43 Iraq Tammuz 1 (SLV); Al Aabed 1989astronautix.com; fas.org; Cordesman & Wagner 1994N/A 2002 1500 5000 Iraq Condor II (BADR 2000) N/A fas.org; Cordesman & Wagner 1994N/A 1988 890 Iraq Al Abbas 1988astronautix.com; nti.org; Cordesman & Wanger 19941990 1991 800 3000 Iraq Al Hussein 1987astronautix.com; Cordesman & Wagner 19941988 1991 650 3300 Iraq SS-1C (Scud-B; R-17; R-300) 1975 1975 SIPRI/TMB 1991 300 450 Iraq Al Samoud I//II 2001nti.org; Cordesman "The Great Iraqi Missile Mystery"2001 2003 183 Iraq Ababil 50/100 (262mm-400 mm)fas.org; nti.org; Cordesman "The Great Iraqi Missile Mystery"1998 2001 3002 150 Iraq Al Fat'h globalsecurity.org; Cordesman "The Great Iraqi Missile Mystery"2000 2001 2003 161 150 Iraq Frog-7b (500mm) 1978 1978 SIPRI 2003 70 400 Iraq Laith 90 (550mm) 1980 1980Navias (1993); Carus (1992)2003 90 500 Israel Jericho-3 2008 missilethreat.csis.org; astronautix.com2011 6500 Israel Jericho-2 astronautix.com; missilethreat.csis.org; Mistry & Gopalaswamy1986 1989 1500 Israel Jericho-1 missilethreat.csis.org; TMB (2016); nti.org; cia declass report1965 1973 1995 420 1000 Israel LORA (650mm) 2003 2007nti.org; missilethreat.csis.org 300 10 Israel Lance (560mm) 1978 1978 fas.org; TMB; SIPRI 1997 121 150 Israel EXTRA (306mm) 2014 2014 military-today.com 150 10 Italy Jupiter 1961 1961 astronautix.com 1963 2400 970 Italy Lance (560mm) 1975 1975 TMB 1992 121 150 Italy Honest John (M50) (760mm) 1959 1959 TMB 1977 48 230 Kazakhstan SS-18 (Satan; RS-20V) 1978 1978 TMB 1992 11200 430 Kazakhstan SS-1C (Scud-B; R-17; R-300) 1964 1964 TMB 1992 300 450 Kazakhstan SS-1C (Scud-B; R-17; R-300) 1992 1992 nti.org; TMB 300 450 Kazakhstan EXTRA (306mm) 2008 2008 SIPRI 150 10 Kazakhstan SS-21 Scarab B (Tochka U) 1976 1976 TMB 1992 120 95 Kazakhstan SS-21 Scarab B (Tochka U) 1992 1992 nti.org 120 95 Kazakhstan Frog-7a (500mm) 1961 1961 TMB 1992 70 400 Kuwait Frog-7b (500mm) 1978 1978 TMB 1990 70 400 Libya Al Fatah (Itislat) N/A N/Anti.org; globalsecurity.org2003 950 Libya Condor II N/A N/A nti.org 1991 890 Libya SS-1C (Scud-B; R-17; R-300) 1976 1976 SIPRI/TMB (2016) 2016 300 450 Libya Frog-7b (500mm) 1978 1978 TMB (2014) 2014 70 400 Netherlands Lance (560mm) 1979 1979 TMB 1992 121 150 Netherlands Honest John (M50) (760mm) 1959 1959 TMB 1981 48 230 North Korea No-Dong 2B (Hwasong 10) 2016 N/Afas.org; missilethreat.csis.org 2500-4000 North Korea KN-22 (Hwasong 15) ICBM 2017 N/A missilethreat.csis.org 13000 North Korea KN-08 (Hwasong 13) N/A N/Amissilethreat.csis.org; fas.org 11500 North Korea Taepo Dong 2 2006 N/A missilethreat.csis.org 4000 North Korea KN-14 (Hwasong 14) 2017 N/Amilitary-today.com; fas.org 9500 North Korea Frog-7b (500mm) 1969 1969 fas.org 70 400 North Korea KN-17 (Hwasong 12) 2017 N/Afas.org; missilethreat.csis.org 3700 North Korea Taepo Dong 1 1998missilethreat.csis.org; Mistry & GopalaswamyN/A 1998 2000 4000 North Korea KN-15 (Pukkuksong 2) 2017 N/A missilethreat.csis.org 2000 North Korea Frog-3 (540mm) 1969 1969 fas.org 61 800 North Korea KN-11 (Pukkuksong 1) SLBM 2014 N/A missilethreat.csis.org 1200 North Korea SS-1C (Scud-B; R-17; R-300) 1981 1981 fas.org 300 450 North Korea Scud-C (Hwasong 6) 1984 1992missilethreat.csis.org; fas.org 500 700 North Korea KN-18 (Scud variant) 2017 N/A missilethreat.csis.org 450 North Korea Scud-B (Hwasong 5) fas.org; astronautix.com; missilethreat.csis.org; 2006 CNS Report1984 1986 340 450 North Korea Scud-D (Hwasong 9; Scud-ER) 1993 1994 missilethreat.csis.org 1000 3000 North Korea No-Dong 1/Hwasong-7 1998 1999fas.org; astronautix.com 1500 North Korea KN-02 (Toksa/SS-21) 2004 2006 missilethreat.csis.org 120 95 North Korea Musudan (BM-25) 2016 N/A missilethreat.csis.org 4000 Norway Honest John (M50) (760mm) 1959 1959 TMB 1965 48 230 Pakistan Hatf-1 1989 1992TMB; missilethreat.csis.org 70 2000 Pakistan DF-11 (CSS-7/M-11) 1992 SIPRI; nti.org; Mistry & Gopalaswamy1992 300 600 Pakistan Hatf-1A 1995 missilethreat.csis.org 100 1000 Pakistan Haft-5 (Ghauri-1) 1998 2003missilethreat.csis.org; nti.org 1500 2500 Pakistan Haft-4 (Shaheen-1) 1999 2003missilethreat.csis.org; nti.org 750 200 Pakistan Haft-3 (Ghaznavi) 1997 2004missilethreat.csis.org; nti.org 290 250 Pakistan Hatf-1B 2000 2004 missilethreat.csis.org 100 500 Pakistan Haft-2 (Abdali) 2005 missilethreat.csis.org 200 Pakistan Haft-6 (Shaheen-2) 2004 2014 missilethreat.csis.org 2500 350 Pakistan Haft-5A (Ghauri-2) 1999 N/Amissilethreat.csis.org; nti.org 1800 Pakistan Haft-9 (Nasr) 2013military-today.com; missilethreat.csis.org; fas.orgN/A 60 Pakistan Shaheen-3 2015 N/A missilethreat.csis.org 2750 Pakistan Ababeel 2017 N/A missilethreat.csis.org 2200 Poland SS-1C (Scud-B; R-17; R-300) 1964 1964 SIPRI/TMB 1991 300 450 Poland SS-1C (Scud-B; R-17; R-300) 1992 1992 TMB 2002 300 450 Poland SS-1B (Scud-A; R-11; Elbrus) 1961 1961 SIPRI/TMB 1970 270 5160 Poland SS-21 Scarab B (Tochka U) 1987 1987 SIPRI/TMB 1991 120 95 Poland SS-21 Scarab B (Tochka U) 2002 2002 TMB (2002; 2009) 2009 120 95 Poland SS-21 Scarab A 1976 1976 TMB 1991 70 150 Poland Frog-7a/b (500mm) 1992 1992 TMB 2002 70 400 Poland Frog-3/4/5 (540mm) 1960 1960 TMB 1991 61 800 Qatar SY-400 2017 2017 thediplomat.com 300 50 Romania SS-1C (Scud-B; R-17; R-300) 1974 1974 SIPRI/TMB 1991 300 450 Romania SS-1B (Scud-A; R-11; Elbrus) 1962 1962 SIPRI/TMB 1970 270 5160 Romania Frog-3/4/5 (540mm) 1971 1971 TMB 1991 61 800 Russia SS-1 (Scunner; R-1) 1948 1950fas.org; astronautix.com1964 270 6940 Russia SS-2 (Sibling; R-2) 1949 1951fas.org; astronautix.com1962 550 6940 Russia SS-3 (Shyster; R-5M) 1955 1955astronautix.com; fas.org1983 1190 5160 Russia SS-3 (Shyster; R-5) 1953 1955astronautix.com' fas.org1983 1190 1400 Russia Frog-1 (612mm) 1953 fas.org; astronautix.com; Phillips (2017)1955 1965 26 700 Russia Frog-2 (324mm) 1953 fas.org; astronautix.com; Phillips (2017)1955 1965 17 770 Russia SS-1B (Scud-A; R-11; Elbrus) 1953 1955fas.org; astronautix.com1965 270 5160 Russia SS-6 (Sapwood; R-7) 1957fas.org; astronautix.com; missilethreat.csis.org1959 1968 8000 3390 Russia SS-4 (Sandal; R-12) 1957 1959fas.org; astronautix.com1987 1500 5160 Russia SS-5 (Skean; R-14) 1960 1960fas.org; astronautix.com1971 3700 1130 Russia SS-1C (Scud-B; R-17; R-300) 1959 missilethreat.csis.org; astronautix.com1964 300 450 Russia Frog-4 (540mm) 1960 fas.org; astronautix.com; Phillips (2017)1960 1965 61 800 Russia Frog-5 (540mm) 1960 fas.org; astronautix.com; Phillips (2017)1960 1965 61 800 Russia SS-6 (Sapwood; R-7U) 1959 1961 astronautix.com 1967 9500 3390 Russia SS-7 (Saddler; R-16) 1960 1962 astronautix.com 1972 13000 2700 Russia SS-7 (Saddler; R-16U) 1961 1963 astronautix.com 1978 13000 2700 Russia SS-4 (Sandal; R-12U) 1961 1963fas.org; astronautix.com1990 2150 2400 Russia SS-5 (Skean; R-14U) 1962 1964 astronautix.com 1969 4000 1130 Russia Frog-3 (540mm) 1960 fas.org; astronautix.com; Phillips (2017)1960 1965 61 800 Russia Frog-7a (500mm) 1961 fas.org; astronautix.com; Phillips (2017)1964 70 650 Russia Frog-7b (500mm) 1961 fas.org; astronautix.com; Phillips (2017)1968 75 650 Russia SS-11 (Sego; RS-10) 1965 1966fas.org; astronautix.com1984 11000 1400 Russia SS-9 (Scarp; R-36) 1963 1967fas.org; astronautix.com1978 10200 1300 Russia R-27 Zyb mod 1 (SS-N-6 Serb) 1967 missilethreat.csis.org; astronautix.org1968 1993 2000 1610 Russia SS-1D (Scud-C) 1965 missilethreat.csis.org; astronautix.com1965 600 700 Russia SS-13 (Savage; RT-2) 1966 1969fas.org; astronautix.com1976 9600 2000 Russia SS-12 (Scaleboard; 9K76) 1969fas.org; astronautix.com1987 890 810 Russia SS-15 (Scrooge; RT-20P) 1967 1970fas.org; astronautix.com1970 7000 4000 Russia SS-14 (Scamp; RT-15) 1965 1970fas.org; astronautix.com1970 2500 2260 Russia R-27 Zyb mod 2 (SS-N-6 Serb) 1973 missilethreat.csis.org 1996 3600 1850 Russia R-27 Zyb mod 3 (SS-N-6 Serb) 1973 missilethreat.csis.org 1996 2980 1850 Russia SS-18 (Satan; R-36; RS-20A) 1971 1974fas.org; astronautix.com 11200 430 Russia SS-19 (Stiletto; RS-18) 1972 1974fas.org; astronautix.com 9650 260 Russia SS-17 (Spanker; RS-16) 1971 1975fas.org; astronautix.com1988 10320 450 Russia SS-20 (Saber; RSD-10) 1974 1976fas.org; astronautix.com1987 5000 550 Russia SS-21 Scarab A 1970fas.org; astronautix.com; IHS Jane's Weapons: Strategic1975 70 150 Russia SS-23 (Spider) 1974 1976fas.org; astronautix.com1987 480 70 Russia SS-16 (Sinner; RT-21) 1972 1977fas.org; astronautix.com1988 10500 1640 Russia R-29 Volna mod 1 (SS-N-Stingray) 1975 astronautix.com; missilethreat.csis.org1977 6500 1400 Russia R-29 Volna mod 2 (SS-N-Stingray) 1976 astronautix.com; missilethreat.csis.org1979 7980 900 Russia R-29 Volna mod 3 (SS-N-Stingray) 1977 astronautix.com; missilethreat.csis.org1979 6500 900 Russia SS-21 Scarab C 1974fas.org; astronautix.com; IHS Jane's Weapons: StrategicND N/A 185 95 Russia SS-23 (Spider) 1974 1980 astronautix.com 1987 480 70 Russia SS-22 (Scaleboard B; SS-22) 1984fas.org; astronautix.com1987 900 230 Russia SS-25 (Sickle; RS-12M Topol 1981 1985fas.org; astronautix.com 10500 210 Russia R29 Shtil (SS-N-23 Skiff) 1981 astronautix.com; missilethreat.csis.org1986 8310 900 Russia SS-24 (Scalpel; RT-23) 1982 1988fas.org; astronautix.com1990 10110 210 Russia SS-1E (Scud-D; R-17 VTO) 1979 missilethreat.csis.org; astronautix.comND ND 700 50 Russia SS-21 Scarab B (Tochka U) 1984fas.org; astronautix.com; IHS Jane's Weapons: Strategic1989 120 95 Russia SS-27 (Topol-M; RS-12M2) 1994 1997fas.org; astronautix.com 10110 350 Russia SS-26 Stone (Iskander E) 1995 2007fas.org; astronautix.com 280 70 Russia SS-26 Stone (Iskander M) 1995 2007fas.org; astronautix.com 500 50 Russia RSM-56 Bulava (SS-N-32) 2004 astronautix.com; missilethreat.csis.org2007 10500 300 Russia SS-27 (Yars; RS-24) 2007 2010fas.org; astronautix.com 12000 250 Saudi Arabia DF-3 (CSS-2) 1987 1987 SIPRI 2650 870 SFR Yugoslavia/FRY/SerbiaFrog-7b (500mm) 1977 1977 TMB 2008 70 400 Slovakia SS-23 (Spider) 1993 1993 PISM 2000 480 70 Slovakia SS-1C (Scud-B; R-17; R-300) 1993 1993 TMB (2001) 2001 300 450 Slovakia SS-21 Scarab B (Tochka U) 1993 1993 TMB (2002) 2002 120 95 Slovakia Frog-7b (500mm) 1993 1993 TMB (2002) 2002 70 400 South Africa RSA-4 N/A ND nti.org 1993 4000 South Africa RSA-3 SLV N/A ND nti.org 1993 1800 South Africa RSA-2 1989 ND nti.org 1993 1450 South Africa RSA-1 1989 ND nti.org 1993 1100 South Korea NHK-2B (Hyonmu-2B) military-today.com; TMB (2014); nti.org (see fn #20); missilethreat.csis.org2009 2009 800 30-70 South Korea NHK-2C (Hyonmu-2C) 2017 2018 missilethreat.csis.org 800 5 South Korea NHK-2A (Hyonmu-2A) 1999 2008nti.org; missilethreat.csis.org 300 30-70 South Korea ATACMS Block 1A 2004 2004 SIPRI 300 10 South Korea KSLV-1 2009 N/A Mistry & Gopalaswamy N/A 300 South Korea NHK-2 (Hyonmu-2) 1985 1987missilethreat.csis.org; nti.org 250 South Korea NHK-1 (Hyonmu-1) 1978 NDmissilethreat.csis.org; nti.orgND 180 South Korea ATACMS Block 1 1999 1999 SIPRI 165 560 South Korea KSR-2 (Sounding Rocket) 1997 Mistry & Gopalaswamy; astronautix.comN/A N/A 160 South Korea KSR-3 (Sounding Rocket) 2002 Mistry & Gopalaswamy; astronautix.comN/A N/A 80 South Korea KSR-1 (Sounding Rocket) 1993 Mistry & Gopalaswamy; astronautix.comN/A N/A 75 South Korea Honest John (M50) (760mm) 1958 1958TMB; Kristensen & Norris1977 48 230 South Korea Lance (560mm) 1977 1977 Kristensen & Norris 1991 121 150 Syria SS-1 E (Scud-D) 2000 2000 SIPRI 700 50 Syria SS-1 D (Scud-C) 1991 1991 SIPRI/TMB 600 700 Syria SS-1C (Scud-B; R-17; R-300) 1973 1973 SIPRI/TMB 300 450 Syria Fateh 110 (CSS-8) 2008 2008 SIPRI 300 Syria SS-26 Stone (Iskander E) 2016 2016 nationalinterest.com 280 70 Syria SS-21 Scarab B (Tochka U) 1983 1983 SIPRI/TMB 120 95 Syria Frog-7b (500mm) 1973 1973 TMB 70 400 Syria Frog-3 (540mm) 1969 1969 SIPRI 61 800 Taiwan Tien Ma (Sky Horse) N/A N/A nti.org; fas.org 950 50 Taiwan SR-1 (Sounding Rocket) 1998 Mistry & Gopalaswamy; astronautix.comN/A N/A 250 Taiwan Ching Feng (Green Bee) N/A nti.org; fas.org; Mistry & GopalaswamyN/A 130 150 Taiwan Tien Chi (Sky Spear) 1997 2001 missilethreat.csis.org 120 Turkey Jupiter 1961 1961 astronautix.com 1963 2400 970 Turkey J-600T Yildirim IV N/A revolvy.com; cdainstitute.com; Jane'sN/A 1500 150 Turkey J-600T Yildirim III N/A revolvy.com; cdainstitute.com; Jane'sN/A 900 150 Turkey J-600T Yildirim II N/A N/A TMB 300 150 Turkey B611 (CSS-11) 2002 2002 SIPRI; TMB 280 30-150 Turkey Khan (M20; Bora) 2017 military-today.com 280 30-50 Turkey ATACMS Block 1 1998 1998 SIPRI 165 50 Turkey J-600T Yildirim (Thunderbolt) 2001 TMB 150 150 Turkey Honest John (M50) (760mm) 1961 1961 TMB 1995 48 230 Turkmenistan SS-1C (Scud-B; R-17; R-300) 1992 1992 TMB 300 450 UAE SS-1C (Scud-B; R-17; R-300) 1989 SIPRI; Declass CIA Report NIE 5/91C/II1989 300 450 UAE ATACMS Block 1A 2013 2013 SIPRI 300 10 Ukraine SS-24 (Scalpel; RT-24) 1987 1987 TMB (2001) 2001 10110 210 Ukraine SS-19 (Stiletto; RS-18) 1975 1975 TMB (2001) 2001 9650 260 Ukraine SS-1C (Scud-B; R-17; R-300) 1964 1964 TMB 1992 300 450 Ukraine SS-1C (Scud-B; R-17; R-300) 1992 1992 TMB 2016 300 450 Ukraine SS-1B (Scud-A; R-11; Elbrus) 1957 1957 TMB 1965 270 5160 Ukraine SS-21 Scarab B (Tochka U) 1989 1989 TMB 1992 120 95 Ukraine SS-21 Scarab B (Tochka U) 1992 1992 TMB 120 95 Ukraine Frog-7b (500mm) 1992 1992 TMB 2016 70 400 United Kingdom Trident II D-5 1994 1994fas.org; astronautix.com 11100 90 United Kingdom Polaris A3 1964 1964fas.org; astronautix.com1994 4600 940 United Kingdom Thor 1958 fas.org; Journal of International History1958 1963 2400 970 United Kingdom Corporal 1955 1955 SIPRI 1965 139 United Kingdom Lance (560mm) 1976 1976 TMB 1992 121 150 United Kingdom Honest John (M50) (760mm) 1960 1960 TMB 1977 48 230 United States RTV-G-1 Corporal SR (305mm) 1945Directory of U.S. Military Rockets and MissilesN/A 1947 80 United States RV-A-10 1947Directory of U.S. Military Rockets and MissilesN/A 1953 109 United States Corporal E TD 1947Directory of U.S. Military Rockets and MissilesN/A 1950 120 United States Hermes II (cruise missile) Directory of U.S. Military Rockets and Missiles; astronautix.com1947 N/A 1950 500 United States RTV-G-4 Bumper SR 1948Directory of U.S. Military Rockets and MissilesN/A 1950 393 United States Hermes A-1 (RV-A-5) Directory of U.S. Military Rockets and Missiles; astronautix.com1950 N/A 1951 65 United States Honest John (M31) (320mm) 1951 1951fas.org; astronautix.com1969 25 United States Corporal 1952 1953fas.org; astronautix.com1964 139 United States Redstone 1953 1958fas.org; astronautix.com1967 325 300 United States Hermes A-3 1953Directory of U.S. Military Rockets and MissilesN/A 1954 240 60 United States Honest John (M50) (760mm) 1955 1955fas.org; astronautix.com1986 48 230 United States Sergeant 1956 1962fas.org; astronautix.com1977 139 United States Little John (320mm) 1956 1960fas.org; astronautix.com1969 18 United States Atlas 1957 1959fas.org; astronautix.com1964 14480 1400 United States Thor/Jupiter 1957 1959fas.org; astronautix.com1963 2400 970 United States Titan I 1959 1962fas.org; astronautix.com1965 11300 2020 United States Polaris A1 1959 1960fas.org; astronautix.com1965 1900 900 United States Minuteman I 1960 1962fas.org; astronautix.com1974 9700 2580 United States Polaris A2 1960 1962fas.org; astronautix.com1974 2800 2900 United States Pershing I 1960 1962fas.org; astronautix.com1991 740 150 United States Titan II 1962 1963fas.org; astronautix.com1976 11690 1300 United States Polaris A3 1962 1964fas.org; astronautix.com1987 4600 940 United States Minuteman II 1964 1966fas.org; astronautix.com; TMB1971 12600 370 United States Lance (560mm) 1965 1972fas.org; astronautix.com1992 121 150 United States Minuteman III 1968 1970fas.org; astronautix.com; TMB 12600 280 United States Poseidon C-3 1968 1971fas.org; astronautix.com1990 4650 530 United States Trident I C-4 1977 1979fas.org; astronautix.com2005 7400 380 United States Pershing II 1977 1983fas.org; astronautix.com1988 1700 30 United States ATACMS Block 1 1985 1991fas.org; astronautix.com2009 165 50 United States Trident II D-5 1987 1990fas.org; astronautix.com 11100 90 United States ATACMS Block 1A 1995 1998fas.org; astronautix.com 300 10 United States ATACMS Block IA Unitary 2001 2002fas.org; astronautix.com; nti.org 300 10 United States ATACMS Block II ND missilethreat.csis.org 2002 140 Vietnam Scud-C (Hwasong 6) 1998 1998 SIPRI 500 700 Vietnam SS-1C (Scud-B; R-17; R-300) 1981 SIPRI; Declass CIA Report NIE 5/91C/II1981 300 450 Vietnam EXTRA (306mm) 2014 2014 SIPRI 150 10 West Germany Pershing II 1983 missilethreat.csis.org; astronautix.com1983 1988 1700 30 West Germany Pershing I 1970 1970 SIPRI/TMB 1982 740 150 West Germany Redstone 1964 missilethreat.csis.org; astronautix.com1964 1964 325 300 West Germany Sergeant 1962 1962 SIPRI/TMB 1976 139 West Germany Lance (560mm) 1978 1978 TMB 1992 121 150 West Germany Honest John (M50) (760mm) 1963 1963 TMB 1978 48 230 Yemen Scud-B (Hwasong 5) 2001 2001SIPRI; Jane's IHS "Yemen" 330 450 Yemen SS-1C (Scud-B; R-17; R-300) 1995 1995 SIPRI/TMB 300 450 Yemen SS-21 Scarab B (Tochka U) 1995 1995 SIPRI/TMB 120 95 Yemen Frog-7b (500mm) 1995 1995 TMB 70 400 Yemen Burkan-1 2016Jane's IHS "Yemen"; Taleblu longwarjournal.org2016 500 Yemen Burkan-2 2017Jane's IHS "Yemen"; Taleblu longwarjournal.org2017 700 Yemen Burkan-2H 2017Jane's IHS "Yemen"; Taleblu longwarjournal.org2017 800 500 Yemen Qaher-1 2015Jane's IHS "Yemen"; Taleblu longwarjournal.org2015 250 Yemen Qaher-2M 2017Jane's IHS "Yemen"; Taleblu longwarjournal.org2017 400 Yemen (North) SS-21 Scarab B (Tochka U) 1988 1988 SIPRI/TMB 1994 120 95 Yemen (South) SS-1C (Scud-B; R-17; R-300) 1979 1979 SIPRI/TMB 1994 300 450 Yemen (South) Frog-7b (500mm) 1979 1979 TMB 1994 70 400 Third Reich V-2 1943astronautix.com; Dungan "V-2: A Combat History"1944 1945 320 6000 Payload (kg) Propellant Motor Type Status D-K 985 Liquid Inertial HE R N 450 Solid Spin HE R N 480 Solid Inertial HE O N 450 Solid Spin HE R N 1200 Solid Spin HE R N 500 Solid Inertial HE R N 400 Solid Inertial HE R N 400 Solid Inertial HE R N 985 Liquid Inertial HE O N 480 Solid Inertial HE O N 482 Solid Inertial HE O N 450 Solid Spin HE, C, N R Y 482 Solid Inertial HE O N 400 Solid Inertial HE O N 160 Solid Inertial HE O N 560 Solid Inertial HE O N 1000 Solid Inertial N R Y 985 Liquid Inertial HE O N 482 Solid Inertial HE O N 450 Solid Spin HE R N 211 Solid Inertial HE, N R Y 680 Solid Spin HE, N R Y Satelite N 180 Solid Spin HE O N 212 Solid Spin HE O N HE R N HE R N 452 Solid Inertial HE, N R N 985 Liquid Inertial HE, C, N R N 450 Solid Spin HE, C, N R N 985 Liquid Inertial HE, C, N R Y 690 Liquid Inertial HE, C, N R Y 450 Solid Spin HE, C, N R Y 680 Solid Spin N R Y 950 Liquid Inertial HE R N 1500 Liquid Inertial HE R N 2150 Liquid Inertial N R N 2190 Liquid Inertial N O N 3000 Liquid Inertial N O N 600 Solid Inertial N R N 600 Solid Inertial HE, N O N 750 Solid Inertial HE, C, N O N 500 Solid Inertial HE, C, N O N 600 Solid Inertial N O N 3190 Liquid Inertial N O N 500 Solid Inertial HE, N O N 700 Solid Inertial N O N 700 Solid Inertial N O N 2800 Solid Inertial N O N 750 Solid Inertial HE, C, N O N 600 Solid Inertial HE, C, N D N 500 Solid Inertial HE O N 2000 Solid Inertial HE O N 600 Solid Inertial HE O N 2500 Solid Inertial N D N 3200 Liquid Inertial N T/D N 1200 Solid Inertial HE, N O N 3200 Liquid Inertial N O N 1000 Solid Inertial HE, N O N 750 Solid Inertial HE, C, N O N 190 Solid Inertial HE O N 300 Solid Inertial HE O N 480 Solid Inertial HE O N 680 Liquid Inertial N R Y 390 Liquid Inertial HE, C, N R Y 1200 Solid Spin HE, C, N R Y 1200 Solid Spin HE R N 985 Liquid Inertial HE, C, N R N 482 Solid Inertial HE, N R N 450 Solid Spin HE R N 452 Solid Inertial HE R N 985 Liquid Inertial HE, C, N R Y 985 Liquid Inertial HE, C, N R N 690 Liquid Inertial HE, C, N R Y 482 Solid Inertial HE, N R Y 450 Solid Spin HE, C, N R Y 450 Solid Spin HE R N 680 Solid Spin HE R Y 452 Solid Inertial HE R N 985 Liquid Inertial HE, C, N R Y 690 Liquid Inertial HE, C, N R Y 482 Solid Inertial HE, N R Y 450 Solid Spin HE, C, N R Y 800 HE R N 985 Liquid Inertial HE O N 500 HE R N 985 Liquid Inertial HE O N 200 Solid Spin HE O N 450 Solid Spin HE O N 1200 Solid Spin HE R N 200 Solid Inertial N O N 200 Solid Inertial N N/A N Solid Inertial N O N 1700 Solid Inertial N R N 1700 Solid Inertial N R N 1000 Solid Inertial N R N Solid Inertial N R N 1200 Solid Inertial N R N Solid Inertial N R N 1700 Solid Inertial N R N 1360 Solid Inertial N R N 500 Solid Inertial HE, N R N 680 Liquid Spin N R Y 500 Solid Inertial HE, N R N 680 Solid Spin HE, N R Y 560 Solid Inertial HE O N 680 Solid Spin HE, N R Y 985 Liquid Inertial HE, C, N R Y 690 Liquid Inertial HE, C, N R Y 450 Solid Spin HE R Y 1200 Solid Spin HE R Y 482 Solid Inertial HE, N R Y 42 Solid Satelite R N 1000 Liquid Inertial HE, C, N O N 907 Solid/Liquid Inertial HE, N R N 1000 Solid/Liquid Satelite N 750 Liquid Inertial HE, N O N 1000 Solid Inertial HE, N O N 1000 Liquid Inertial HE, C, N O N 2000 Solid/Liquid Satelite N 1000 Solid/Liquid Inertial HE, N O N 800 Solid Inertial HE, N T/D N 2000 Solid Inertial N O N 1000 Solid Inertial HE, N T/D N 800 Solid Inertial N T/D N 1500 Solid Inertial N T/D N 200 Solid T/D N 1000 Solid Inertial HE, N D N 700 Liquid Inertial HE, C, B O N 985 Liquid Inertial HE O N Solid Spin HE O N 190 Solid Inertial HE O N 600 Liquid Inertial HE O N 760 Liquid Inertial HE O N 770 Liquid Inertial HE O N 500 Solid Inertial HE, C O N 250 Solid Spin HE O N 480 Solid Inertial HE, C O N 600 Solid Inertial HE, C O N 800 Liquid/Solid Inertial HE T/D N 800 Liquid/Solid Inertial HE, C, N O N 1800 Liquid Inertial HE T/D N 1000 Solid Inertial HE O N 750 Liquid Inertial HE O N 750 Liquid Inertial HE O N Solid Inertial HE O N 500 Solid Inertial HE O N 600 Solid Inertial HE O N 600 Solid Inertial HE O N 190 Solid Spin HE O N 90 Solid Spin HE O N 42 Solid Spin HE O N 750 Liquid Inertial HE R N 500 Solid Inertial HE R N 200 Liquid Inertial HE R N 250 Liquid Inertial HE, C, B R N 985 Liquid Inertial HE, C R N 300 Liquid Inertial HE R N 280 Liquid Inertial HE R N 300 Solid Inertial HE R N 450 Solid Spin HE R N 400 Solid Spin HE, C R N 750 Solid Inertial HE, C, N O N 1500 Solid Inertial HE, C, N O N 650 Solid Inertial HE, C, N R N 440 Solid Inertial HE O N 211 Liquid Inertial HE, N R (In Store) N 120 Solid Inertial HE O N 1000 Liquid Inertial N R Y 211 Liquid Inertial HE, N R Y 680 Solid Spin HE, N R Y 7250 Liquid Inertial N R Y 985 Liquid Inertial HE, C R Y 985 Liquid Inertial HE R Y 120 Solid Inertial HE O N 690 Solid Inertial HE R Y 482 Solid Inertial HE O N 450 Solid Inertial HE, C, N R Y 450 Solid Spin HE R N 500 Liquid Inertial HE T/D N 500 Solid Inertial HE R N 985 Liquid Inertial HE, C R N 450 Solid Spin HE R N 211 Liquid Inertial HE, N R Y 680 Solid Spin HE, N R Y 1200 Liquid Inertial HE, N T/D N Liquid Inertial HE, N T/D N 500 Liquid Inertial HE, N T/D N 1500 Liquid Inertial HE, N T/D N Liquid Inertial HE, N T/D N 450 Solid Spin HE, N O N 650 Liquid Inertial HE, N T/D N 750 Liquid Inertial HE, N R N Liquid Inertial HE, N T/D N 1200 Solid Spin HE O N Liquid Inertial HE, N T/D N 985 Liquid Inertial HE, C, N O N 770 Liquid Inertial HE, C, N O N Liquid Inertial HE, N T/D N 985 Liquid Inertial HE, C, N O N 500 Liquid Inertial HE, C, N O N 1158 Liquid Inertial HE, C, N O N 482 Solid Inertial HE, C, N O N 500 Liquid Inertial HE, C, N T/D N 680 Solid Spin HE R Y 500 Solid Spin HE O N 500 Solid Inertial HE O N 500 Solid Spin HE O N 700 Liquid Inertial HE, C, N O N 700 Solid Inertial HE, C, N O N 700 Solid Inertial HE, N O N 500 Solid Inertial HE O N 250 Solid Inertial HE O N 700 Solid Inertial HE, C, N O N 750 Liquid Inertial HE, C, N T/D N 400 Solid Spin HE, N T/D N 1200 Solid Inertial HE, N T/D N Solid Inertial HE, N T/D N 985 Liquid Inertial HE R Y 985 Liquid Inertial HE R N 690 Liquid Inertial HE, C, N R Y 482 Solid Inertial HE, N R Y 482 Solid Inertial HE R N 680 Solid Inertial HE, N R Y 450 Solid Spin HE R N 1200 Solid Spin HE, C, N R Y 500 Solid Inertial HE O N 985 Liquid Inertial HE R Y 690 Liquid Inertial HE, C, N R Y 1200 Solid Spin HE, C, N R (In Store) Y 547 Liquid Inertial HE R N 570 Radio Command LiquidLiquid Rad Source R N 1425 Liquid Inertial R, N R N 1425 Liquid Inertial HE R N 1200 Solid Spin HE, N R N 1200 Solid Spin HE R N 690 Liquid Inertial HE, C, N R N 5370 Liquid Inertial N R N 390 Liquid Inertial N R (INF) N 680 Liquid Inertial N R (INF) N 985 Liquid Inertial HE, C, N O N 1200 Solid Spin C R N 1200 Solid Spin N R N 5400 Liquid Inertial N R N 1475 Liquid Inertial N R N 1475 Liquid Inertial N R N 1600 Liquid Inertial N R (INF) N 680 Liquid Inertial N R N 1200 Solid Spin HE R N 450 Solid Spin HE, C, N O N 450 Solid Spin HE O N 770 Liquid Inertial N R (START) N 5825 Liquid Inertial N R N 1000 Liquid Inertial N R N 600 Liquid Inertial HE, N R N 600 Solid Inertial N R N 526 Liquid Inertial N R (INF) N 545 Solid Inertial N R N 500 Solid Inertial N R N 1000 Liquid Inertial N R N 840 Liquid Inertial N R N 7250 Liquid Inertial N O N 4350 Liquid Inertial N O N 2550 Liquid Inertial N R N 1740 Solid Inertial N R (INF) N 482 Solid Inertial HE, C, N O N 452 Solid Inertial HE, N R (INF) N 540 Solid Inertial N R N 1300 Liquid Inertial N O N 860 Liquid Inertial N O N 1300 Liquid Inertial N O N 482 Solid Inertial HE, N O N 452 Solid Inertial N O N 450 Liquid Inertial N R (INF) N 1000 Solid Inertial N O N 1810 Liquid Inertial N O N 4050 Solid Inertial N R N 985 Liquid Inertial HE, C, N O N 482 Solid Inertial HE, C, N O N 1200 Solid Inertial N O N 480 Solid Inertial HE O N 700 Solid Inertial HE, N O N 1150 Solid Inertial N O N 1200 Solid Inertial N O N 2150 Solid Inertial HE O N 450 Solid Spin HE R N 452 Solid Inertial HE R N 985 Liquid Inertial HE R N 482 Solid Inertial HE R N 450 Solid Spin HE R N 700 Solid Inertial Satelite R N 340 Solid Inertial Satelite R N 1500 Solid Inertial HE, N R N 1500 Solid Inertial HE, N R N 500 Solid Inertial HE O N Solid Inertial HE O N 500 Solid Inertial HE O N 160 Solid Inertial HE O N 100 Solid/Liquid Satelite N 490 Solid Inertial HE O N 500 Solid Inertial HE R N 50 Solid Inertial HE O N 150 Solid Satelite N 150 Solid Satelite N 150 Solid Satelite N 680 Solid Spin N R Y 211 Liquid Inertial HE, N R Y 985 Liquid Inertial HE O N 600 Liquid Inertial HE O N 985 Liquid Inertial HE O N 500 Solid Inertial HE O N 480 Solid Inertial HE O Y 482 Solid Inertial HE O N 450 Solid Spin HE O N 1200 Solid Spin HE R N 350 Solid Inertial HE R N Satelite N 210 Liquid Inertial HE R N 200 Solid Inertial HE O N 1000 Liquid Inertial N R Y 480 Solid Inertial HE R/D N 480 Solid Inertial HE R/D N 480 Solid Inertial HE O N 300 Solid Inertial HE O N 470 Solid Inertial HE O N 560 Solid Inertial HE O N 480 Solid Inertial HE O N 680 Solid Spin HE, N R Y 985 Liquid Inertial HE O N 985 Liquid Inertial HE O N 160 Solid Inertial HE O N 4050 Solid Inertial N R Y 4350 Liquid Inertial N R Y 985 Liquid Inertial HE, N R Y 985 Liquid Inertial HE R N 690 Liquid Inertial HE R Y 482 Solid Inertial HE R Y 482 Solid Inertial HE O N 450 Solid Spin HE R N 1690 Solid Inertial N O N 760 Solid Inertial N R N 1000 Liquid Inertial N R Y 680 Liquid Spin N R Y 211 Liquid Inertial HE, N R Y 680 Solid Spin HE, N R Y 11 Solid Spin SR R N Solid Spin TV R N 227 Liquid Spin TV R N 230 Liquid R N Liquid Spin SR R N Liquid Spin TV R N 680 Solid Spin HE, N R N 680 Liquid Spin HE, N R N 3580 Liquid Spin N R N 450 Liquid Inertial TV R N 680 Solid Spin HE, N R N 820 Solid Inertial HE, N R N 110 Solid Spin HE, N R N 1400 Liquid Inertial N R N 1000 Liquid Inertial N R N 1400 Liquid Inertial N R N 500 Solid Inertial N R N 450 Solid Inertial N R N 500 Solid Inertial N R N 290 Solid Inertial N R (INF) N 3100 Liquid Inertial N R N 760 Solid Inertial N R N 680 Solid Inertial N R N 211 Liquid Inertial HE, N R N 1000 Solid Inertial N O N 1630 Solid Inertial N R N 1317 Solid Inertial N R N Solid Inertial N R N 560 Solid Inertial HE R N 1690 Solid Inertial N O N 160 Solid Inertial HE O N 247 Solid Inertial HE O N 268 Solid Inertial HE R N 600 Solid Inertial HE O N 985 Liquid Inertial HE O N 120 Solid Inertial HE O N Solid Inertial N R Y 290 Solid Inertial N R Y 3580 Liquid Spin N R Y 820 Solid Spin N R Y 211 Liquid Inertial HE, N R Y 680 Solid Spin HE, N R Y 985 Solid Inertial HE O N 985 Liquid Inertial HE O N 482 Solid Inertial HE O N 450 Solid Spin HE O N 500 Solid Spin HE O N Solid Spin HE O N Mettler & Reiter Country_Missile Coding Blanc Country_Missile Coding ID Country Year Retire Range ID 2 US 1951 500 2

1957 5000 1959 12000

40 Cuba 40 160 Argentina 160 200 UK 1958 5000 200 1994 12000 220 France 1974 500 220 1980 5000 1985 12000 265 German Democratic Republic 265 290 Poland 290

315 Czechoslovakia 315 316 Czech Republic 316

317 Slovakia 1993 2000 120 317

345 Yugoslavia 345 350 Greece 1996 165 350 355 Bulgaria 355 365 Russia 1947 500 365 1955 2000 1957 12000

369 Ukraine 1992 300 369 370 Belarus 1992 300 370 371 Armenia 1994 300 371 373 Azerbaijan 373 560 South Africa 1987 1993 1100 560 615 Algeria 615

620 Libya 1976 300 620 630 Iran 1985 550 630 2003 1300 640 Turkey 1996 165 640

645 Iraq 1974 2007 160 645

651 Egypt 1968 300 651

652 Syria 1974 300 652

666 Israel 1972 1500 666

670 Saudi Arabia 1986 2600 670 678 Yemen, Arab Republic1988 300 678

680 Yemen, People's Republic1980 1990 300 680 690 Kuwait 690 692 Bahrain 2001 165 692

694 Qatar 694 696 UAE 696

700 Afghanistan 1988 2007 300 700 701 Turkmenistan 1992 300 701 705 Kazakhstan 1992 300 705 710 China 1956 500 710 1966 2000 1972 5000 1978 12000

713 Taiwan 1983 300 713 731 North Korea 1969 300 731 1993 1300 732 South Korea 1978 300 732

750 India 1994 250 750 2004 2000

770 Pakistan 1992 300 770 1998 1300

816 Vietnam 1999 300 816 Blanc Country_Missile Coding Country FFT Year IOC Year Retire Range Missile US 1947 ND 1950 120 Corporal TD 1952 1953 1964 139 Corporal 1956 1962 1977 139 Sergeant 1965 1972 1992 121 Lance 1985 1991 2009 165 ATACMS Block 1 1995 1998 300 ATACMS Block 1A Cuba 1962 1962 1990 61 FROG-3 Argentina 1986 ND 1993 150 Alacran UK

France 1965 1974 1993 120 Pluton 1988 ND 1997 480 Hades

German Dem Rep1985 1985 1991 480 SS-23 Spider Poland 1992 1992 2002 300 SS-1C (Scud-B; R-17; R-300) 2002 2002 2009 120 SS-21 Scarab B (Tochka U) Czechoslovakia1985 1985 1992 480 SS-23 Spider Czech Republic1993 1993 1998 300 SS-1C (Scud-B; R-17; R-300) 1993 1993 2005 120 SS-21 Scarab B (Tochka U) Slovakia 1993 1993 2000 480 SS-23 Spider 1993 1993 2001 300 SS-1C (Scud-B; R-17; R-300) 1993 1993 2002 120 SS-21 Scarab B (Tochka U) Yugoslavia 1977 1977 2008 70 FROG-7 Greece 1998 1998 165 ATACMS Block 1 Bulgaria 1986 2002 480 SS-23 Spider Russia 1948 1950 1964 270 SS-1 (Scunner; R-1) 1953 1955 1983 1190 SS-3 (Shyster, R-5) 1965 1965 600 SS-1 D (Scud-C) 1979 ND 700 SS-1 E (Scud-D) Ukraine 1992 1992 300 SS-1C (Scud-B; R-17; R-300) Belarus 1992 1992 300 SS-1C (Scud-B; R-17; R-300) Armenia 1996 1996 300 SS-1C (Scud-B; R-17; R-300) Azerbaijan 2009 2009 120 SS-21 Scarab B (Tochka U) South Africa 1989 ND 1993 1450 RSA-2 Algeria 1970 1970 1982 61 FROG-3 1975 1975 1982 70 FROG-7 2017 2017 280 SS-26 Stone (Iskander E) Libya 1976 1976 300 SS-1C (Scud-B; R-17; R-300) Iran 1985 1985 330 Shahab-1/Hwasong-5 1991 1991 500 Scud-C (Hwasong 6/Shabab 2) 1998 2003 1300 Shahab-3 (Zelzal-3) 2004 2007 1950 Ghadr 1 (Qadr) 2008 2012 2000 Ashura (Sejjil-1) Turkey 1998 1998 165 ATACMS Block 1 2002 2002 280 B611 (CH-SS-9 mod 1) Iraq 1975 1975 1991 300 SS-1C (Scud-B; R-17; R-300) 1987 1988 1991 650 Al Hussein 1988 1990 1991 800 Al Abbas 1978 1978 2004 70 FROG-7 2001 2001 2004 183 Al Samoud I/II Egypt 1969 1969 61 FROG-3 1971 1971 70 FROG-7 1973 1973 300 SS-1C (Scud-B; R-17; R-300) 1988 1996 450 Project T (Scud-B Variant) Syria 1969 1969 61 FROG-3 1973 1973 300 SS-1C (Scud-B; R-17; R-300) 1991 1991 600 SS-1 D (Scud-C) 2000 2000 700 SS-1 E (Scud-D) Israel 1965 1973 1995 420 Jericho I 1986 1989 1450 Jericho II 2008 2011 6500 Jericho III Saudi Arabia 1987 1987 2650 DF-3 (CSS-2) Yemen, Arab Republic1988 1988 1994 120 SS-21 Scarab B (Tochka U) 1995 1995 300 SS-1C (Scud-B; R-17; R-300) 2001 2001 330 Scud-B/Hwasong-5 2016 2016 500 Burkan-1 2017 2017 800 Burkan-2H Yemen, People's Republic1979 1979 1994 300 SS-1C (Scud-B; R-17; R-300) Kuwait 1978 1978 1990 70 FROG-7 Bahrain 2002 2002 165 ATACMS Block 1 2013 2013 300 ATACMS Block 1A Qatar 2017 2017 300 SY-400 UAE 1989 1989 300 SS-1C (Scud-B; R-17; R-300) 2013 2013 300 ATACMS Block 1A Afghanistan 1988 1988 2012 300 SS-1C (Scud-B; R-17; R-300) Turkmenistan1992 1992 300 SS-1C (Scud-B; R-17; R-300) Kazakhstan 1992 1992 300 SS-1C (Scud-B; R-17; R-300) China 1960 1961 1975 590 1059 (DF-1) 1962 1970 1979 1250 DF-2 (CSS-1) 1985 1991 2150 DF-21 (CSS-5) 1988 1990 600 DF-15 (CSS-9/M-9) 2017 2015 4000 DF-26 Taiwan 1997 2001 120 Tien Chi North Korea 1969 1969 70 FROG-7 1981 1981 300 SS-1C (Scud-B; R-17; R-300) 1984 1992 500 Scud-C (Hwasong 6) 1993 1994 1000 Scud-D (Hwasong 7) 1998 1999 1500 No-Dong 1 1998 ND 2000 Taepo Dong 1 2006 ND 4000 Taepo Dong 2 South Korea 1978 ND 180 NHK-1 (Hyonmu-1) 1985 1987 250 NHK-2 (Hyonmu-2) 2004 2004 300 ATACMS Block 1A 2009 2009 800 NHK-2B (Hyonmu-2B) India 1988 1994 150 Prithvi-I 1989 ND 1200 Agni-TD 1996 1996 250 Prithvi-II 1999 2011 3500 Agni-II 2002 2004 1200 Agni-I Pakistan 1989 1992 70 Haft-1 1992 1992 300 DF-11 (CSS-7/M-11) 1998 2003 1500 Haft-5 (Ghauri-1) 1999 ND 1800 Haft-5A (Ghauri-2) 2004 2014 2500 Haft-6 (Shaheen-2) Vietnam 1981 1981 300 SS-1C (Scud-B; R-17; R-300) 1998 1998 500 Scud-C (Hwasong 6) BSRBM: 40km to 149km SRBM: 150 km to 999km MRBM: 1000km to 2999km IRBM: 3000km to 5000km IC/SLBM: 5001km+ Appendix D. Probit Model Results - NATO Forward Deployed Missiles as "Possession"

Model 6: Remove nuclear-only missiles, FFT, alter lower range threshold to 40 km, with better Year and Range data, and fixed Nuk dummy variable probit chall sqrtcmindist contig cincparity majpowa jointdem ab_IR_BSRBM_FFT_ a nukadum_df ab_IR_BSRBM_FFT_b nukbdum_df pcyrs pcyrs2 pcyrs3, robust cluster (id)

Iteration 0: log pseudolikelihood = -3405.7614 Iteration 1: log pseudolikelihood = -2728.2585 Iteration 2: log pseudolikelihood = -2493.3224 Iteration 3: log pseudolikelihood = -2470.9747 Iteration 4: log pseudolikelihood = -2470.4341 Iteration 5: log pseudolikelihood = -2470.433 Iteration 6: log pseudolikelihood = -2470.433

Probit regression Number of obs = 1,295,374 Wald chi2(12) = 1021.12 Prob > chi2 = 0.0000 Log pseudolikelihood = -2470.433 Pseudo R2 = 0.2746

(Std. Err. adjusted for 37,430 clusters in id) ------| Robust chall | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------sqrtcmindist | -.0142217 .0015439 -9.21 0.000 -.0172477 -.0111957 contig | .3747737 .0756204 4.96 0.000 .2265605 .5229869 cincparity | .1660992 .070632 2.35 0.019 .027663 .3045354 majpowa | .2150307 .1194294 1.80 0.072 -.0190466 .449108 jointdem | -.1827102 .0607248 -3.01 0.003 -.3017285 -.0636918 ab_IR_BSRBM_IOC_a | .0764621 .0833703 0.92 0.359 -.0869406 .2398648 nukadum_df | .506457 .1218394 4.16 0.000 .2676561 .7452579 ab_IR_BSRBM_IOC_b | -.1649818 .0930229 -1.77 0.076 -.3473032 .0173397 nukbdum_df | .5096807 .0896949 5.68 0.000 .333882 .6854794 pcyrs | -.0597433 .0085436 -6.99 0.000 -.0764884 -.0429982 pcyrs2 | .0015649 .0003786 4.13 0.000 .0008229 .0023069 pcyrs3 | -.0000132 4.61e-06 -2.87 0.004 -.0000222 -4.19e-06 _cons | -2.486283 .0852808 -29.15 0.000 -2.653431 -2.319136 ------Model 7: Remove nuclear-only missiles, IOC, alter lower range threshold to 40 km, with better Year and Range data, and fixed Nuk dummy variable probit chall sqrtcmindist contig cincparity majpowa jointdem ab_IR_BSRBM_IOC_a nukadum_df ab_IR_BSRBM_IOC_b nukbdum_df pcyrs pcyrs2 pcyrs3, robust cluster (id)

Iteration 0: log pseudolikelihood = -3405.7614 Iteration 1: log pseudolikelihood = -2729.2549 Iteration 2: log pseudolikelihood = -2492.3813 Iteration 3: log pseudolikelihood = -2469.3662 Iteration 4: log pseudolikelihood = -2468.8108 Iteration 5: log pseudolikelihood = -2468.8096 Iteration 6: log pseudolikelihood = -2468.8096

Probit regression Number of obs = 1,295,374 Wald chi2(12) = 1020.71 Prob > chi2 = 0.0000 Log pseudolikelihood = -2468.8096 Pseudo R2 = 0.2751

(Std. Err. adjusted for 37,430 clusters in id) ------| Robust chall | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------sqrtcmindist | -.0143143 .0015397 -9.30 0.000 -.0173321 -.0112964 contig | .3790028 .0759645 4.99 0.000 .2301152 .5278904 cincparity | .1673498 .070958 2.36 0.018 .0282746 .306425 majpowa | .2083683 .1203089 1.73 0.083 -.0274328 .4441693 jointdem | -.1837159 .0609644 -3.01 0.003 -.3032039 -.064228 ab_IR_BSRBM_IOC_a | .0671068 .0807311 0.83 0.406 -.0911233 .2253368 nukadum_df | .5211151 .1167438 4.46 0.000 .2923014 .7499288 ab_IR_BSRBM_IOC_b | -.2092318 .0986082 -2.12 0.034 -.4025003 -.0159634 nukbdum_df | .5180563 .0874679 5.92 0.000 .3466224 .6894901 pcyrs | -.0596127 .008525 -6.99 0.000 -.0763214 -.042904 pcyrs2 | .0015607 .0003775 4.13 0.000 .0008208 .0023006 pcyrs3 | -.0000132 4.59e-06 -2.87 0.004 -.0000222 -4.18e-06 _cons | -2.484517 .084937 -29.25 0.000 -2.65099 -2.318043 ------Appendix E. Model 1 - Conventional Probit Model Results

Model 1: Mettler & Reiter's original model probit chall sqrtcmindist contig cincparity majpowa jointdem abmrange nukadum bbmrange nukbdum pcyrs pcyrs2 pcyrs3, robust cluster (id)

Iteration 0: log pseudolikelihood = -3405.7614 Iteration 1: log pseudolikelihood = -2707.6646 Iteration 2: log pseudolikelihood = -2484.1884 Iteration 3: log pseudolikelihood = -2462.4366 Iteration 4: log pseudolikelihood = -2461.9656 Iteration 5: log pseudolikelihood = -2461.9649 Iteration 6: log pseudolikelihood = -2461.9649

Probit regression Number of obs = 1,295,374 Wald chi2(12) = 1132.81 Prob > chi2 = 0.0000 Log pseudolikelihood = -2461.9649 Pseudo R2 = 0.2771

(Std. Err. adjusted for 37,430 clusters in id) ------| Robust chall | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------sqrtcmindist | -.0139785 .0015226 -9.18 0.000 -.0169627 -.0109944 contig | .3567866 .0743905 4.80 0.000 .210984 .5025893 cincparity | .1527595 .070976 2.15 0.031 .013649 .29187 majpowa | .0972648 .1321221 0.74 0.462 -.1616898 .3562193 jointdem | -.1718074 .0619154 -2.77 0.006 -.2931593 -.0504556 abmrange | .3532511 .0777996 4.54 0.000 .2007667 .5057355 nukadum | .3584715 .1374433 2.61 0.009 .0890877 .6278553 bbmrange | -.2314552 .0983227 -2.35 0.019 -.4241642 -.0387461 nukbdum | .5890561 .0951303 6.19 0.000 .4026042 .775508 pcyrs | -.0601987 .0086025 -7.00 0.000 -.0770593 -.043338 pcyrs2 | .0016016 .0003799 4.22 0.000 .0008569 .0023463 pcyrs3 | -.0000139 4.62e-06 -3.00 0.003 -.0000229 -4.82e-06 _cons | -2.488266 .0854386 -29.12 0.000 -2.655722 -2.320809 ------

Model 2: Mettler & Reiter's original model (FFT, 150km, no range maximum) with better Year and Range data, and fixed Nuk dummy variable probit chall sqrtcmindist contig cincparity majpowa jointdem ab_MR_IR_150_FFT_a nukadum_df ab_MR_IR_150_FFT_b nukbdum_df pcyrs pcyrs2 pcyrs3, robust cluster (id)

Iteration 0: log pseudolikelihood = -3405.7614 Iteration 1: log pseudolikelihood = -2714.7751 Iteration 2: log pseudolikelihood = -2488.3394 Iteration 3: log pseudolikelihood = -2466.3965 Iteration 4: log pseudolikelihood = -2465.9201 Iteration 5: log pseudolikelihood = -2465.9195 Iteration 6: log pseudolikelihood = -2465.9195

Probit regression Number of obs = 1,295,374 Wald chi2(12) = 1080.76 Prob > chi2 = 0.0000 Log pseudolikelihood = -2465.9195 Pseudo R2 = 0.2760

(Std. Err. adjusted for 37,430 clusters in id) contig | .3609424 .0742583 4.86 0.000 .2153988 .5064861 cincparity | .1599645 .0707501 2.26 0.024 .0212968 .2986321 majpowa | .1513119 .1265445 1.20 0.232 -.0967107 .3993345 jointdem | -.1780591 .0599478 -2.97 0.003 -.2955546 -.0605636 ab_MR_IR_150_FFT_a | .2472434 .0853842 2.90 0.004 .0798934 .4145934 nukadum_df | .3947415 .1364128 2.89 0.004 .1273774 .6621056 ab_MR_IR_150_FFT_b | -.1379418 .095902 -1.44 0.150 -.3259062 .0500227 nukbdum_df | .5237587 .0941407 5.56 0.000 .3392464 .7082711 pcyrs | -.0591959 .0085661 -6.91 0.000 -.0759852 -.0424066 pcyrs2 | .0015424 .0003787 4.07 0.000 .0008002 .0022846 pcyrs3 | -.0000131 4.61e-06 -2.84 0.005 -.0000221 -4.05e-06 _cons | -2.499162 .0854934 -29.23 0.000 -2.666726 -2.331598 ------

Model 3: Mettler & Reiter's original model (150km, no range maximum) but with IOC, better Year and Range data, and fixed Nuk dummy variable probit chall sqrtcmindist contig cincparity majpowa jointdem ab_MR_IR_150_IOC_a nukadum_df ab_MR_IR_150_IOC_b nukbdum_df pcyrs pcyrs2 pcyrs3, robust cluster (id)

Iteration 0: log pseudolikelihood = -3405.7614 Iteration 1: log pseudolikelihood = -2717.2867 Iteration 2: log pseudolikelihood = -2489.6646 Iteration 3: log pseudolikelihood = -2467.1581 Iteration 4: log pseudolikelihood = -2466.6776 Iteration 5: log pseudolikelihood = -2466.6768 Iteration 6: log pseudolikelihood = -2466.6768

Probit regression Number of obs = 1,295,374 Wald chi2(12) = 1074.93 Prob > chi2 = 0.0000 Log pseudolikelihood = -2466.6768 Pseudo R2 = 0.2757

(Std. Err. adjusted for 37,430 clusters in id) ------| Robust chall | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------sqrtcmindist | -.0139568 .001531 -9.12 0.000 -.0169575 -.0109562 contig | .3609393 .0741338 4.87 0.000 .2156397 .5062389 cincparity | .1589961 .0708728 2.24 0.025 .020088 .2979043 majpowa | .1471608 .1318491 1.12 0.264 -.1112588 .4055804 jointdem | -.1782351 .0600156 -2.97 0.003 -.2958634 -.0606067 ab_MR_IR_150_IOC_a | .2283698 .0827778 2.76 0.006 .0661283 .3906113 nukadum_df | .4269692 .1304049 3.27 0.001 .1713804 .682558 ab_MR_IR_150_IOC_b | -.1382224 .0955191 -1.45 0.148 -.3254365 .0489917 nukbdum_df | .5143128 .0870428 5.91 0.000 .343712 .6849135 pcyrs | -.0592581 .0085589 -6.92 0.000 -.0760333 -.0424829 pcyrs2 | .0015431 .0003784 4.08 0.000 .0008014 .0022848 pcyrs3 | -.0000131 4.61e-06 -2.84 0.005 -.0000221 -4.05e-06 _cons | -2.495132 .0850499 -29.34 0.000 -2.661827 -2.328437 ------

Model 4: Remove nuclear-only missiles, remain consistent with original model (FFT, 150 km) with better Year and Range data, and fixed Nuk dummy variable probit chall sqrtcmindist contig cincparity majpowa jointdem ab_IR_150_FFT_a nukadum_df ab_IR_150_FFT_b nukbdum_df pcyrs pcyrs2 pcyrs3, robust cluster (id)

Iteration 0: log pseudolikelihood = -3405.7614 Iteration 6: log pseudolikelihood = -2469.4407

Probit regression Number of obs = 1,295,374 Wald chi2(12) = 1036.50 Prob > chi2 = 0.0000 Log pseudolikelihood = -2469.4407 Pseudo R2 = 0.2749

(Std. Err. adjusted for 37,430 clusters in id) ------| Robust chall | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------sqrtcmindist | -.0142609 .0015361 -9.28 0.000 -.0172715 -.0112502 contig | .3761026 .0762194 4.93 0.000 .2267154 .5254898 cincparity | .1619219 .0708904 2.28 0.022 .0229792 .3008645 majpowa | .2103845 .1197005 1.76 0.079 -.0242243 .4449932 jointdem | -.191891 .0613043 -3.13 0.002 -.3120452 -.0717369 ab_IR_150_FFT_a | .0743021 .0886957 0.84 0.402 -.0995384 .2481426 nukadum_df | .5130611 .1220934 4.20 0.000 .2737625 .7523598 ab_IR_150_FFT_b | -.2044159 .1010058 -2.02 0.043 -.4023836 -.0064483 nukbdum_df | .5196422 .0853714 6.09 0.000 .3523174 .686967 pcyrs | -.0599066 .0085086 -7.04 0.000 -.0765832 -.0432299 pcyrs2 | .0015621 .0003763 4.15 0.000 .0008246 .0022995 pcyrs3 | -.0000131 4.58e-06 -2.86 0.004 -.0000221 -4.13e-06 _cons | -2.48187 .0854436 -29.05 0.000 -2.649337 -2.314404 ------

Model 5: Remove nuclear-only missiles, remain consistent with original model (150 km), but IOC, better Year and Range data, and fixed Nuk dummy variable probit chall sqrtcmindist contig cincparity majpowa jointdem ab_IR_150_IOC_a nukadum_df ab_IR_150_IOC_b nukbdum_df pcyrs pcyrs2 pcyrs3, robust cluster (id)

Iteration 0: log pseudolikelihood = -3405.7614 Iteration 1: log pseudolikelihood = -2724.4451 Iteration 2: log pseudolikelihood = -2492.2856 Iteration 3: log pseudolikelihood = -2468.5468 Iteration 4: log pseudolikelihood = -2467.9732 Iteration 5: log pseudolikelihood = -2467.9721 Iteration 6: log pseudolikelihood = -2467.9721

Probit regression Number of obs = 1,295,374 Wald chi2(12) = 1036.68 Prob > chi2 = 0.0000 Log pseudolikelihood = -2467.9721 Pseudo R2 = 0.2754

(Std. Err. adjusted for 37,430 clusters in id) ------| Robust chall | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------sqrtcmindist | -.0141869 .0015237 -9.31 0.000 -.0171732 -.0112006 contig | .3759983 .0762446 4.93 0.000 .2265615 .525435 cincparity | .1626457 .0708922 2.29 0.022 .0236995 .3015918 majpowa | .2045122 .120907 1.69 0.091 -.0324612 .4414856 jointdem | -.1908532 .0615898 -3.10 0.002 -.3115669 -.0701395 ab_IR_150_IOC_a | .1087397 .0849728 1.28 0.201 -.0578039 .2752833 nukadum_df | .511425 .1180073 4.33 0.000 .2801349 .7427151 ab_IR_150_IOC_b | -.2387917 .1026741 -2.33 0.020 -.4400292 -.0375542 nukbdum_df | .5140411 .0824382 6.24 0.000 .3524651 .675617 Model 6: Remove nuclear-only missiles, FFT, alter lower range threshold to 40 km, with better Year and Range data, and fixed Nuk dummy variable probit chall sqrtcmindist contig cincparity majpowa jointdem ab_IR_BSRBM_FFT_a nukadum_df ab_IR_BSRBM_FFT_b nukbdum_df pcyrs pcyrs2 pcyrs3, robust cluster (id)

Iteration 0: log pseudolikelihood = -3405.7614 Iteration 1: log pseudolikelihood = -2727.6133 Iteration 2: log pseudolikelihood = -2493.7438 Iteration 3: log pseudolikelihood = -2470.6815 Iteration 4: log pseudolikelihood = -2470.1033 Iteration 5: log pseudolikelihood = -2470.1021 Iteration 6: log pseudolikelihood = -2470.1021

Probit regression Number of obs = 1,295,374 Wald chi2(12) = 1024.53 Prob > chi2 = 0.0000 Log pseudolikelihood = -2470.1021 Pseudo R2 = 0.2747

(Std. Err. adjusted for 37,430 clusters in id) ------| Robust chall | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------sqrtcmindist | -.0142247 .0015306 -9.29 0.000 -.0172247 -.0112247 contig | .3735991 .0763833 4.89 0.000 .2238906 .5233076 cincparity | .1641944 .0708622 2.32 0.020 .025307 .3030819 majpowa | .212802 .1200266 1.77 0.076 -.0224458 .4480498 jointdem | -.1881058 .0607946 -3.09 0.002 -.307261 -.0689507 ab_IR_BSRBM_FFT_a | .08124 .0862965 0.94 0.346 -.0878979 .250378 nukadum_df | .5061633 .1227236 4.12 0.000 .2656294 .7466972 ab_IR_BSRBM_FFT_b | -.1794503 .0951575 -1.89 0.059 -.3659556 .0070551 nukbdum_df | .5145589 .0877618 5.86 0.000 .3425489 .6865689 pcyrs | -.0597592 .008534 -7.00 0.000 -.0764856 -.0430328 pcyrs2 | .0015582 .0003774 4.13 0.000 .0008186 .0022978 pcyrs3 | -.0000131 4.59e-06 -2.85 0.004 -.0000221 -4.09e-06 _cons | -2.484255 .0851659 -29.17 0.000 -2.651177 -2.317333 ------

Model 7: Remove nuclear-only missiles, IOC, alter lower range threshold to 40 km, with better Year and Range data, and fixed Nuk dummy variable probit chall sqrtcmindist contig cincparity majpowa jointdem ab_IR_BSRBM_IOC_a nukadum_df ab_IR_BSRBM_IOC_b nukbdum_df pcyrs pcyrs2 pcyrs3, robust cluster (id)

Iteration 0: log pseudolikelihood = -3405.7614 Iteration 1: log pseudolikelihood = -2726.6159 Iteration 2: log pseudolikelihood = -2492.226 Iteration 3: log pseudolikelihood = -2468.7835 Iteration 4: log pseudolikelihood = -2468.2125 Iteration 5: log pseudolikelihood = -2468.2114 Iteration 6: log pseudolikelihood = -2468.2114

Probit regression Number of obs = 1,295,374 Wald chi2(12) = 1027.25 Prob > chi2 = 0.0000 Log pseudolikelihood = -2468.2114 Pseudo R2 = 0.2753

(Std. Err. adjusted for 37,430 clusters in id) contig | .3748001 .0762408 4.92 0.000 .2253709 .5242293 cincparity | .1643426 .0710658 2.31 0.021 .0250562 .3036289 majpowa | .2038262 .1213009 1.68 0.093 -.0339192 .4415715 jointdem | -.1883468 .0608559 -3.09 0.002 -.3076222 -.0690714 ab_IR_BSRBM_IOC_a | .1025983 .0820776 1.25 0.211 -.0582708 .2634674 nukadum_df | .5105406 .1180228 4.33 0.000 .2792201 .7418612 ab_IR_BSRBM_IOC_b | -.2244224 .0956244 -2.35 0.019 -.4118427 -.037002 nukbdum_df | .5177162 .0849269 6.10 0.000 .3512624 .6841699 pcyrs | -.0597481 .0085323 -7.00 0.000 -.0764711 -.0430251 pcyrs2 | .0015573 .0003773 4.13 0.000 .0008179 .0022967 pcyrs3 | -.0000131 4.59e-06 -2.85 0.004 -.000022 -4.08e-06 _cons | -2.484558 .0849643 -29.24 0.000 -2.651085 -2.318031 ------

Mettler & Reiter: tab statea if abmrange==1

COW country | code for | state A | Freq. Percent Cum. ------+------2 | 7,750 21.18 21.18 200 | 4,959 13.55 34.73 220 | 4,472 12.22 46.95 317 | 48 0.13 47.08 350 | 72 0.20 47.27 365 | 8,375 22.88 70.16 369 | 191 0.52 70.68 370 | 112 0.31 70.98 371 | 84 0.23 71.21 560 | 66 0.18 71.39 620 | 224 0.61 72.01 630 | 427 1.17 73.17 640 | 132 0.36 73.53 645 | 204 0.56 74.09 651 | 261 0.71 74.80 652 | 300 0.82 75.62 666 | 752 2.05 77.68 670 | 1,394 3.81 81.49 678 | 92 0.25 81.74 680 | 72 0.20 81.94 692 | 14 0.04 81.97 700 | 148 0.40 82.38 701 | 112 0.31 82.68 705 | 128 0.35 83.03 710 | 5,446 14.88 97.92 713 | 75 0.20 98.12 731 | 147 0.40 98.52 732 | 60 0.16 98.69 750 | 220 0.60 99.29 770 | 216 0.59 99.88 816 | 45 0.12 100.00 ------+------Total | 36,598 100.00

tab statea if ab_IR_BSRBM_IOC_a ==1 220 | 200 1.89 4.13 265 | 96 0.91 5.04 290 | 203 1.92 6.96 315 | 139 1.32 8.28 316 | 89 0.84 9.12 317 | 138 1.31 10.43 345 | 218 2.06 12.49 350 | 60 0.57 13.06 355 | 194 1.84 14.90 365 | 2,105 19.94 34.83 369 | 191 1.81 36.64 370 | 112 1.06 37.70 371 | 72 0.68 38.38 615 | 78 0.74 39.12 620 | 224 2.12 41.24 630 | 442 4.19 45.43 640 | 134 1.27 46.70 645 | 243 2.30 49.00 651 | 282 2.67 51.67 652 | 373 3.53 55.20 666 | 560 5.30 60.51 670 | 1,342 12.71 73.22 678 | 111 1.05 74.27 680 | 72 0.68 74.95 690 | 39 0.37 75.32 692 | 12 0.11 75.43 696 | 95 0.90 76.33 700 | 148 1.40 77.73 701 | 112 1.06 78.80 705 | 128 1.21 80.01 710 | 1,413 13.38 93.39 731 | 156 1.48 94.87 732 | 42 0.40 95.26 750 | 184 1.74 97.01 770 | 171 1.62 98.63 816 | 145 1.37 100.00 ------+------Total | 10,559 100.00

Looking at whether the BM possession variable is highly correlated with the Maj Power variable and Nuclear WS dummy variable: (1) M&R - nukadum is correlated at .72 (sig to .05); majpowa is correlated at .71 (sig to .05) & majpowa and nuka are highly correlated at .78 (2) When I recode the abmrange variable using a more rigorously applied coding method, the two variables are still significantly, highly correlated with the key IV (3) When I impose the 4000km range maximum, expand the lower threshold to 40km: - whether I use M&R or J&G's dummy variable for nuclear weapon possession, the dummy variable is correlated at .18 - majpowa is correlated at .16 pwcorr year abmrange nukadum majpowa, star(0.05)sig

| year abmrange nukadum majpowa ------+------year | 1.0000 | | abmrange | 0.0386* 1.0000 majpowa | -0.0157* 0.7090* 0.7777* 1.0000 | 0.0000 0.0000 0.0000

pwcorr year ab_IR_BSRBM_IOC_a nukadum_df majpowa, star(0.05)sig

| year ab_IR_.. nukadu~f majpowa ------+------year | 1.0000 | | ab_IR_BS~C_a | 0.0298* 1.0000 | 0.0000 | nukadum_df | 0.0143* 0.1814* 1.0000 | 0.0000 0.0000 | majpowa | -0.0157* 0.1574* 0.7507* 1.0000 | 0.0000 0.0000 0.0000 |

Same as above, tabulating the data two ways to see what the numbers are when chall==1 and BM ==1, M&R have 128 cases, better coding produces 115 cases, but as soon as the range maximum is imposed, the number of cases shrinks to 81/78, depending on FFT versus IOC (varying 150km and 40km does not change the number of cases).

. tab chall abmrange, col chi2

=1 if | statea | challenges | in that | year, | using | Reiter | stateabm, coded 0 if 2010 | rangea

Pearson chi2(1) = 1.5e+03 Pr = 0.000

. tab chall ab_IR_BSRBM_IOC_a, col chi2

=1 if | statea | challenges | in that | year, | using | =1 States in Range_48 | 99.98 99.19 | 99.97 ------+------+------1 | 288 86 | 374 | 0.02 0.81 | 0.03 ------+------+------Total | 1,394,225 10,559 | 1,404,784 | 100.00 100.00 | 100.00

Pearson chi2(1) = 2.5e+03 Pr = 0.000

Appendix F. Model 1 - Average Marginal Effects

=" (1) (2) (3) (4) (5) (6) (7) =" chall chall chall chall chall chall chall

chall =" =" =" =" =" =" =" sqrtcmindist -0.0139*** -0.0139*** -0.0140*** -0.0143*** -0.0142*** -0.0142*** -0.0142*** =" (-9.10) (-9.05) (-9.12) (-9.28) (-9.31) (-9.29) (-9.32) contig 0.356*** 0.361*** 0.361*** 0.376*** 0.376*** 0.374*** 0.375*** =" (4.82) (4.86) (4.87) (4.93) (4.93) (4.89) (4.92) cincparity 0.162* 0.160* 0.159* 0.162* 0.163* 0.164* 0.164* =" (2.29) (2.26) (2.24) (2.28) (2.29) (2.32) (2.31) majpowa 0.0916 0.151 0.147 0.210 0.205 0.213 0.204 =" (0.73) (1.20) (1.12) (1.76) (1.69) (1.77) (1.68) jointdem -0.173** -0.178** -0.178** -0.192** -0.191** -0.188** -0.188**

=" (-2.86) (-2.97) (-2.97) (-3.13) (-3.10) (-3.09) (-3.09)

abmrange 0.344*** =" =" =" =" =" =" =" (4.40) =" =" =" =" =" =" nukadum_df 0.375** 0.395** 0.427** 0.513*** 0.511*** 0.506*** 0.511*** =" (2.86) (2.89) (3.27) (4.20) (4.33) (4.12) (4.33) bbmrange -0.211* =" =" =" =" =" ="

=" (-2.24) =" =" =" =" =" =" nukbdum_df 0.561*** 0.524*** 0.514*** 0.520*** 0.514*** 0.515*** 0.518*** =" (6.52) (5.56) (5.91) (6.09) (6.24) (5.86) (6.10) pcyrs -0.0591*** -0.0592*** -0.0593*** -0.0599*** -0.0599*** -0.0598*** -0.0597***

=" (-6.86) (-6.91) (-6.92) (-7.04) (-7.03) (-7.00) (-7.00) pcyrs2 0.00154*** 0.00154*** 0.00154*** 0.00156*** 0.00156*** 0.00156*** 0.00156*** =" (4.05) (4.07) (4.08) (4.15) (4.14) (4.13) (4.13) pcyrs3 -0.0000132** -0.0000131** -0.0000131** -0.0000131** -0.0000131** -0.0000131** -0.0000131** =" (-2.84) (-2.84) (-2.84) (-2.86) (-2.85) (-2.85) (-2.85) ab_MR_IR_150_FFT_a =" 0.247** =" =" =" =" =" =" =" (2.90) =" =" =" =" =" ab_MR_IR_150_FFT_b =" -0.138 =" =" =" =" =" =" =" (-1.44) =" =" =" =" ="

1 ab_MR_IR_150_IOC_a =" =" 0.228** =" =" =" =" =" =" =" (2.76) =" =" =" =" ab_MR_IR_150_IOC_b =" =" -0.138 =" =" =" =" =" =" =" (-1.45) =" =" =" =" ab_IR_150_FFT_a =" =" =" 0.0743 =" =" =" =" =" =" =" (0.84) =" =" =" ab_IR_150_FFT_b =" =" =" -0.204* =" =" ="

=" =" =" =" (-2.02) =" =" =" ab_IR_150_IOC_a =" =" =" =" 0.109 =" =" =" =" =" =" =" (1.28) =" =" ab_IR_150_IOC_b =" =" =" =" -0.239* =" =" =" =" =" =" =" (-2.33) =" =" ab_IR_BSRBM_FFT_a =" =" =" =" =" 0.0812 =" =" =" =" =" =" =" (0.94) =" ab_IR_BSRBM_FFT_b =" =" =" =" =" -0.179 =" =" =" =" =" =" =" (-1.89) ="

ab_IR_BSRBM_IOC_a =" =" =" =" =" =" 0.103

=" =" =" =" =" =" =" (1.25) ab_IR_BSRBM_IOC_b =" =" =" =" =" =" -0.224*

=" =" =" =" =" =" =" (-2.35)

_cons -2.503*** -2.499*** -2.495*** -2.482*** -2.485*** -2.484*** -2.485*** =" (-29.39) (-29.23) (-29.34) (-29.05) (-29.25) (-29.17) (-29.24)

N 1295374 1295374 1295374 1295374 1295374 1295374 1295374 t statistics in parentheses ="* p<0.05 ** p<0.01 *** p<0.001"

2 Appendix G. Model 1 - Conventional Probit Model - Clarify

Model 1: Mettler & Reiter original estsimp probit chall sqrtcmindist contig cincparity majpowa jointdem nukadum_df nukbdum_df abmrange bbmrange abmbbm pcyrs pcyrs2 pcyrs3, robust cluster (id)

Iteration 0: log pseudolikelihood = -3405.7614 Iteration 1: log pseudolikelihood = -2706.0427 Iteration 2: log pseudolikelihood = -2511.7254 Iteration 3: log pseudolikelihood = -2464.5819 Iteration 4: log pseudolikelihood = -2457.9174 Iteration 5: log pseudolikelihood = -2457.6985 Iteration 6: log pseudolikelihood = -2457.6982

Probit regression Number of obs = 1295374 Wald chi2(13) = 1205.82 Prob > chi2 = 0.0000 Log pseudolikelihood = -2457.6982 Pseudo R2 = 0.2784

(Std. Err. adjusted for 37430 clusters in id) ------| Robust chall | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------sqrtcmindist | -.0138689 .0015332 -9.05 0.000 -.0168739 -.0108639 contig | .3566609 .0741536 4.81 0.000 .2113224 .5019993 cincparity | .1762643 .0712122 2.48 0.013 .0366909 .3158377 majpowa | .0825971 .1279295 0.65 0.519 -.1681402 .3333343 jointdem | -.1736191 .0608468 -2.85 0.004 -.2928766 -.0543615 nukadum_df | .3760793 .1317306 2.85 0.004 .1178921 .6342666 nukbdum_df | .5540494 .0860447 6.44 0.000 .3854048 .7226939 abmrange | .3699643 .0848099 4.36 0.000 .20374 .5361886 bbmrange | -.1613376 .1142384 -1.41 0.158 -.3852408 .0625656 abmbbm | -.1061554 .1440719 -0.74 0.461 -.388531 .1762203 pcyrs | -.0592959 .0086087 -6.89 0.000 -.0761686 -.0424232 pcyrs2 | .0015492 .0003803 4.07 0.000 .0008038 .0022946 pcyrs3 | -.0000132 4.63e-06 -2.86 0.004 -.0000223 -4.16e-06 _cons | -2.510245 .0871845 -28.79 0.000 -2.681123 -2.339366 ------

Simulating main parameters. Please wait.... % of simulations completed: 7% 14% 21% 28% 35% 42% 50% 57% 64% 71% 78% 85% 92% 100%

Number of simulations : 1000 Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9 b10 b11 b12 b13 b14

. setx sqrtcmindist mean contig 0 cincparity mean majpowa 0 jointdem 0 nukadum_df 0 nukbdum_df 0 abmrange 0 bbmrange 0 abmbbm 0 pcyrs mean pcyrs2 mean pcyrs3 mean

. simqi, prval(1)

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------Pr(chall=1) | .0000207 4.83e-06 .0000124 .0000308

. setx sqrtcmindist mean contig 0 cincparity mean majpowa 0 jointdem 0 nukadum_df 0 nukbdum_df 0 abmrange 1 bbmrange 0 abmbbm 0 pcyrs mean pcyrs2 mean pcyrs3 mean

. simqi, prval(1) . simqi, fd(prval(1)) changex(abmrange 0 1)

First Difference: abmrange 0 1

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------dPr(chall = 1) | .0000833 .0000451 .0000244 .0002021

. setx sqrtcmindist mean contig 0 cincparity mean majpowa 0 jointdem 0 nukadum_df 0 nukbdum_df 0 abmrange 0 bbmrange 1 abmbbm 0 pcyrs mean pcyrs2 mean pcyrs3 mean

. simqi, prval(1)

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------Pr(chall=1) | .0000119 7.96e-06 2.80e-06 .0000315

. simqi, fd(prval(1)) changex(bbmrange 0 1)

First Difference: bbmrange 0 1

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------dPr(chall = 1) | -8.81e-06 6.43e-06 -.0000185 6.41e-06

. setx sqrtcmindist mean contig 0 cincparity mean majpowa 0 jointdem 0 nukadum_df 0 nukbdum_df 0 abmrange 1 bbmrange 1 abmbbm 1 pcyrs mean pcyrs2 mean pcyrs3 mean

. simqi, prval(1)

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------Pr(chall=1) | .0000375 .0000241 8.72e-06 .0000997

. simqi, fd(prval(1)) changex(abmrange 0 1 bbmrange 0 1 abmbbm 0 1)

First Difference: abmrange 0 1 bbmrange 0 1 abmbbm 0 1

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------dPr(chall = 1) | .0000168 .0000223 -9.03e-06 .0000735

Model 2: Mettler & Reiter fixed . estsimp probit chall sqrtcmindist contig cincparity majpowa jointdem nukadum_df nukbdum_df ab_MR_IR_150_FFT_a ab_MR_IR_150_FFT_b abmbbm pcyrs pcyrs2 pcyrs3, robust cluster (id)

Iteration 0: log pseudolikelihood = -3405.7614 Iteration 1: log pseudolikelihood = -2715.2324 Iteration 2: log pseudolikelihood = -2519.495 Iteration 3: log pseudolikelihood = -2472.0943 Iteration 4: log pseudolikelihood = -2465.3689 Iteration 5: log pseudolikelihood = -2465.1466 Iteration 6: log pseudolikelihood = -2465.1463

Probit regression Number of obs = 1295374 Wald chi2(13) = 1107.61 Prob > chi2 = 0.0000 Log pseudolikelihood = -2465.1463 Pseudo R2 = 0.2762 sqrtcmindist | -.0138796 .0015464 -8.98 0.000 -.0169104 -.0108488 contig | .3623706 .0744891 4.86 0.000 .2163748 .5083665 cincparity | .1784295 .0721445 2.47 0.013 .0370288 .3198301 majpowa | .1418176 .127542 1.11 0.266 -.1081602 .3917953 jointdem | -.1795368 .0601364 -2.99 0.003 -.297402 -.0616716 nukadum_df | .3945944 .1373029 2.87 0.004 .1254857 .6637031 nukbdum_df | .5142792 .0948983 5.42 0.000 .3282819 .7002765 ab_MR_IR~T_a | .2817484 .0937517 3.01 0.003 .0979984 .4654985 ab_MR_IR~T_b | -.0785924 .1055354 -0.74 0.456 -.285438 .1282532 abmbbmFFT | -.1327501 .1270697 -1.04 0.296 -.3818022 .116302 pcyrs | -.0594479 .0085835 -6.93 0.000 -.0762713 -.0426245 pcyrs2 | .0015456 .0003784 4.08 0.000 .000804 .0022872 pcyrs3 | -.0000131 4.60e-06 -2.85 0.004 -.0000221 -4.08e-06 _cons | -2.508633 .0870945 -28.80 0.000 -2.679335 -2.337931 ------

Simulating main parameters. Please wait.... % of simulations completed: 7% 14% 21% 28% 35% 42% 50% 57% 64% 71% 78% 85% 92% 100%

Number of simulations : 1000 Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9 b10 b11 b12 b13 b14

. setx sqrtcmindist mean contig 0 cincparity mean majpowa 0 jointdem 0 nukadum_df 0 nukbdum_df 0 ab_MR_IR_150_FFT_a 0 ab_MR_IR_150_FFT_b 0 abmbbmFFT 0 pcyrs mean pcyrs2 mean pcyrs3 mean

. simqi, prval(1)

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------Pr(chall=1) | .0000206 5.21e-06 .0000119 .0000327

. setx sqrtcmindist mean contig 0 cincparity mean majpowa 0 jointdem 0 nukadum_df 0 nukbdum_df 0 ab_MR_IR_150_FFT_a 1 ab_MR_IR_150_FFT_b 0 abmbbmFFT 0 pcyrs mean pcyrs2 mean pcyrs3 mean

. simqi, prval(1)

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------Pr(chall=1) | .0000735 .0000373 .0000234 .0001636

. simqi, fd(prval(1)) changex(ab_MR_IR_150_FFT_a 0 1)

First Difference: ab_MR_IR_150_FFT_a 0 1

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------dPr(chall = 1) | .0000528 .0000345 9.36e-06 .0001399

. setx sqrtcmindist mean contig 0 cincparity mean majpowa 0 jointdem 0 nukadum_df 0 nukbdum_df 0 ab_MR_IR_150_FFT_a 0 ab_MR_IR_150_FFT_b 1 abmbbmFFT 0 pcyrs mean pcyrs2 mean pcyrs3 mean

. simqi, prval(1)

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------Pr(chall=1) | .0000168 .0000102 4.28e-06 .0000429 ------+------dPr(chall = 1) | -3.78e-06 7.99e-06 -.0000153 .0000163

. setx sqrtcmindist mean contig 0 cincparity mean majpowa 0 jointdem 0 nukadum_df 0 nukbdum_df 0 ab_MR_IR_150_FFT_a 1 ab_MR_IR_150_FFT_b 1 abmbbmFFT 1 pcyrs mean pcyrs2 mean pcyrs3 mean

. simqi, prval(1)

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------Pr(chall=1) | .000034 .0000239 7.06e-06 .0000983

. simqi, fd(prval(1)) changex(ab_MR_IR_150_FFT_a 0 1 ab_MR_IR_150_FFT_b 0 1 abmbbmFFT 0 1)

First Difference: ab_MR_IR_150_FFT_a 0 1 ab_MR_IR_150_FFT_b 0 1 abmbbmFFT 0 1

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------dPr(chall = 1) | .0000133 .0000216 -.0000113 .0000719

Model 3: Mettler & Reiter fixed but IOC

. estsimp probit chall sqrtcmindist contig cincparity majpowa jointdem nukadum_df nukbdum_df ab_MR_IR_150_IOC_a ab_MR_IR_150_IOC_b abmbbmIOC pcyrs pcyrs2 pcyrs3, robust cluster (id)

Iteration 0: log pseudolikelihood = -3405.7614 Iteration 1: log pseudolikelihood = -2716.871 Iteration 2: log pseudolikelihood = -2520.607 Iteration 3: log pseudolikelihood = -2473.1869 Iteration 4: log pseudolikelihood = -2466.4598 Iteration 5: log pseudolikelihood = -2466.2377 Iteration 6: log pseudolikelihood = -2466.2373

Probit regression Number of obs = 1295374 Wald chi2(13) = 1134.45 Prob > chi2 = 0.0000 Log pseudolikelihood = -2466.2373 Pseudo R2 = 0.2759

(Std. Err. adjusted for 37430 clusters in id) ------| Robust chall | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------sqrtcmindist | -.0139421 .0015381 -9.06 0.000 -.0169567 -.0109276 contig | .3622246 .0744175 4.87 0.000 .2163689 .5080803 cincparity | .1718797 .0702375 2.45 0.014 .0342167 .3095427 majpowa | .1385676 .1339563 1.03 0.301 -.1239819 .4011172 jointdem | -.1796696 .0602564 -2.98 0.003 -.2977699 -.0615693 nukadum_df | .4289256 .1312903 3.27 0.001 .1716014 .6862498 nukbdum_df | .5087325 .0873373 5.82 0.000 .3375544 .6799105 ab_MR_IR~C_a | .2541246 .0917269 2.77 0.006 .0743432 .4339061 ab_MR_IR~C_b | -.0922812 .11311 -0.82 0.415 -.3139728 .1294103 abmbbmIOC | -.1039877 .1447197 -0.72 0.472 -.3876332 .1796578 pcyrs | -.0595256 .0085779 -6.94 0.000 -.0763381 -.0427132 pcyrs2 | .0015478 .0003783 4.09 0.000 .0008064 .0022892 pcyrs3 | -.0000131 4.60e-06 -2.85 0.004 -.0000221 -4.10e-06 _cons | -2.500745 .0863642 -28.96 0.000 -2.670016 -2.331475 ------Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9 b10 b11 b12 b13 b14

. setx sqrtcmindist mean contig 0 cincparity mean majpowa 0 jointdem 0 nukadum_df 0 nukbdum_df 0 ab_MR_IR_150_IOC_a 0 ab > _MR_IR_150_IOC_b 0 abmbbmIOC 0 pcyrs mean pcyrs2 mean pcyrs3 mean

. simqi, prval(1)

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------Pr(chall=1) | .0000206 4.82e-06 .0000124 .0000308

. setx sqrtcmindist mean contig 0 cincparity mean majpowa 0 jointdem 0 nukadum_df 0 nukbdum_df 0 ab_MR_IR_150_IOC_a 1 ab > _MR_IR_150_IOC_b 0 abmbbmIOC 0 pcyrs mean pcyrs2 mean pcyrs3 mean

. simqi, prval(1)

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------Pr(chall=1) | .0000645 .0000302 .0000229 .0001372

. simqi, fd(prval(1)) changex(ab_MR_IR_150_IOC_a 0 1)

First Difference: ab_MR_IR_150_IOC_a 0 1

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------dPr(chall = 1) | .000044 .0000275 7.30e-06 .0001098

. setx sqrtcmindist mean contig 0 cincparity mean majpowa 0 jointdem 0 nukadum_df 0 nukbdum_df 0 ab_MR_IR_150_IOC_a 0 ab > _MR_IR_150_IOC_b 1 abmbbmIOC 0 pcyrs mean pcyrs2 mean pcyrs3 mean

. simqi, prval(1)

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------Pr(chall=1) | .0000158 .0000101 3.72e-06 .000043

. simqi, fd(prval(1)) changex(ab_MR_IR_150_IOC_b 0 1)

First Difference: ab_MR_IR_150_IOC_b 0 1

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------dPr(chall = 1) | -4.81e-06 8.20e-06 -.0000156 .000018

. setx sqrtcmindist mean contig 0 cincparity mean majpowa 0 jointdem 0 nukadum_df 0 nukbdum_df 0 ab_MR_IR_150_IOC_a 1 ab > _MR_IR_150_IOC_b 1 abmbbmIOC 1 pcyrs mean pcyrs2 mean pcyrs3 mean

. simqi, prval(1)

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------Pr(chall=1) | .0000306 .0000195 6.76e-06 .0000807

. simqi, fd(prval(1)) changex(ab_MR_IR_150_IOC_a 0 1 ab_MR_IR_150_IOC_b 0 1 abmbbmIOC 0 1) Model 4: Remove nuclear-only missiles, remain consistent with original model (FFT, 150 km) with better Year and Range data

. estsimp probit chall sqrtcmindist contig cincparity majpowa jointdem nukadum_df nukbdum_df ab_IR_150_FFT_a ab_IR_150_F > FT_b ab150FFT pcyrs pcyrs2 pcyrs3, robust cluster (id)

Iteration 0: log pseudolikelihood = -3405.7614 Iteration 1: log pseudolikelihood = -2726.7277 Iteration 2: log pseudolikelihood = -2525.0023 Iteration 3: log pseudolikelihood = -2476.2177 Iteration 4: log pseudolikelihood = -2468.9821 Iteration 5: log pseudolikelihood = -2468.7181 Iteration 6: log pseudolikelihood = -2468.7176

Probit regression Number of obs = 1295374 Wald chi2(13) = 1010.68 Prob > chi2 = 0.0000 Log pseudolikelihood = -2468.7176 Pseudo R2 = 0.2751

(Std. Err. adjusted for 37430 clusters in id) ------| Robust chall | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------sqrtcmindist | -.0144155 .0015604 -9.24 0.000 -.0174738 -.0113572 contig | .3721096 .0769653 4.83 0.000 .2212603 .5229588 cincparity | .1522235 .0718408 2.12 0.034 .0114182 .2930289 majpowa | .2149931 .1206744 1.78 0.075 -.0215243 .4515106 jointdem | -.1901853 .061229 -3.11 0.002 -.310192 -.0701786 nukadum_df | .5113075 .1222833 4.18 0.000 .2716365 .7509784 nukbdum_df | .5251672 .0839247 6.26 0.000 .3606778 .6896566 ab_IR_15~T_a | .0403069 .0981448 0.41 0.681 -.1520534 .2326672 ab_IR_15~T_b | -.2749013 .1135293 -2.42 0.015 -.4974147 -.0523879 ab150FFT | .1551761 .1627655 0.95 0.340 -.1638384 .4741906 pcyrs | -.0598721 .0085155 -7.03 0.000 -.0765622 -.043182 pcyrs2 | .001562 .0003764 4.15 0.000 .0008244 .0022997 pcyrs3 | -.0000131 4.57e-06 -2.86 0.004 -.0000221 -4.14e-06 _cons | -2.47109 .0861337 -28.69 0.000 -2.639909 -2.302271 ------

Simulating main parameters. Please wait.... % of simulations completed: 7% 14% 21% 28% 35% 42% 50% 57% 64% 71% 78% 85% 92% 100%

Number of simulations : 1000 Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9 b10 b11 b12 b13 b14

. setx sqrtcmindist mean contig 0 cincparity mean majpowa 0 jointdem 0 nukadum_df 0 nukbdum_df 0 ab_IR_150_FFT_a 0 ab_IR > _150_FFT_b 0 ab150FFT 0 pcyrs mean pcyrs2 mean pcyrs3 mean

. simqi, prval(1)

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------Pr(chall=1) | .0000202 5.32e-06 .0000117 .0000315

. setx sqrtcmindist mean contig 0 cincparity mean majpowa 0 jointdem 0 nukadum_df 0 Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------Pr(chall=1) | .000027 .0000147 7.23e-06 .0000645

. simqi, fd(prval(1)) changex(ab_IR_150_FFT_a 0 1)

First Difference: ab_IR_150_FFT_a 0 1

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------dPr(chall = 1) | 6.84e-06 .0000123 -9.24e-06 .0000371

. setx sqrtcmindist mean contig 0 cincparity mean majpowa 0 jointdem 0 nukadum_df 0 nukbdum_df 0 ab_IR_150_FFT_a 0 ab_IR > _150_FFT_b 1 ab150FFT 0 pcyrs mean pcyrs2 mean pcyrs3 mean

. simqi, prval(1)

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------Pr(chall=1) | 6.82e-06 4.11e-06 1.83e-06 .0000177

. simqi, fd(prval(1)) changex(ab_IR_150_FFT_b 0 1)

First Difference: ab_IR_150_FFT_b 0 1

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------dPr(chall = 1) | -.0000133 4.81e-06 -.0000234 -4.60e-06

. setx sqrtcmindist mean contig 0 cincparity mean majpowa 0 jointdem 0 nukadum_df 0 nukbdum_df 0 ab_IR_150_FFT_a 1 ab_IR > _150_FFT_b 1 ab150FFT 1 pcyrs mean pcyrs2 mean pcyrs3 mean

. simqi, prval(1)

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------Pr(chall=1) | .0000171 .0000126 3.32e-06 .0000512

. simqi, fd(prval(1)) changex(ab_IR_150_FFT_a 0 1 ab_IR_150_FFT_b 0 1 ab150FFT 0 1)

First Difference: ab_IR_150_FFT_a 0 1 ab_IR_150_FFT_b 0 1 ab150FFT 0 1

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------dPr(chall = 1) | -3.09e-06 .0000114 -.0000173 .0000259

Model 5: Remove nuclear-only missiles, remain consistent with original model (IOC, 150 km) with better Year and Range data

. estsimp probit chall sqrtcmindist contig cincparity majpowa jointdem nukadum_df nukbdum_df ab_IR_150_IOC_a ab_IR_150_I > OC_b ab150IOC pcyrs pcyrs2 pcyrs3, robust cluster (id)

Iteration 0: log pseudolikelihood = -3405.7614 Iteration 1: log pseudolikelihood = -2725.259 Iteration 2: log pseudolikelihood = -2522.4637 Iteration 3: log pseudolikelihood = -2473.7081 Wald chi2(13) = 1011.98 Prob > chi2 = 0.0000 Log pseudolikelihood = -2466.3127 Pseudo R2 = 0.2758

(Std. Err. adjusted for 37430 clusters in id) ------| Robust chall | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------sqrtcmindist | -.0143997 .0015501 -9.29 0.000 -.0174379 -.0113615 contig | .3701622 .0770588 4.80 0.000 .2191297 .5211946 cincparity | .1493507 .0719397 2.08 0.038 .0083515 .29035 majpowa | .2137766 .1217973 1.76 0.079 -.0249417 .4524948 jointdem | -.1890344 .061687 -3.06 0.002 -.3099387 -.06813 nukadum_df | .5066306 .1182086 4.29 0.000 .274946 .7383152 nukbdum_df | .52022 .0815708 6.38 0.000 .3603441 .6800959 ab_IR_15~C_a | .0571204 .0962286 0.59 0.553 -.1314841 .245725 ab_IR_15~C_b | -.3665934 .1330415 -2.76 0.006 -.62735 -.1058369 ab150IOC | .2549231 .1789264 1.42 0.154 -.0957663 .6056125 pcyrs | -.0594634 .0085402 -6.96 0.000 -.0762018 -.0427249 pcyrs2 | .0015476 .0003776 4.10 0.000 .0008076 .0022876 pcyrs3 | -.000013 4.59e-06 -2.82 0.005 -.000022 -3.97e-06 _cons | -2.471686 .0853932 -28.94 0.000 -2.639054 -2.304319 ------

Simulating main parameters. Please wait.... % of simulations completed: 7% 14% 21% 28% 35% 42% 50% 57% 64% 71% 78% 85% 92% 100%

Number of simulations : 1000 Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9 b10 b11 b12 b13 b14

. setx sqrtcmindist mean contig 0 cincparity mean majpowa 0 jointdem 0 nukadum_df 0 nukbdum_df 0 ab_IR_150_IOC_a 0 ab_IR > _150_IOC_b 0 ab150IOC 0 pcyrs mean pcyrs2 mean pcyrs3 mean

. simqi, prval(1)

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------Pr(chall=1) | .0000203 5.59e-06 .0000111 .0000328

. setx sqrtcmindist mean contig 0 cincparity mean majpowa 0 jointdem 0 nukadum_df 0 nukbdum_df 0 ab_IR_150_IOC_a 1 ab_IR > _150_IOC_b 0 ab150IOC 0 pcyrs mean pcyrs2 mean pcyrs3 mean

. simqi, prval(1)

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------Pr(chall=1) | .0000291 .0000168 8.64e-06 .0000717

. simqi, fd(prval(1)) changex(ab_IR_150_IOC_a 0 1)

First Difference: ab_IR_150_IOC_a 0 1

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------dPr(chall = 1) | 8.77e-06 .0000138 -8.64e-06 .0000459

. setx sqrtcmindist mean contig 0 cincparity mean majpowa 0 jointdem 0 nukadum_df 0 Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------Pr(chall=1) | 4.67e-06 3.52e-06 8.68e-07 .0000139

. simqi, fd(prval(1)) changex(ab_IR_150_IOC_b 0 1)

First Difference: ab_IR_150_IOC_b 0 1

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------dPr(chall = 1) | -.0000156 5.09e-06 -.0000268 -7.01e-06

. setx sqrtcmindist mean contig 0 cincparity mean majpowa 0 jointdem 0 nukadum_df 0 nukbdum_df 0 ab_IR_150_IOC_a 1 ab_IR > _150_IOC_b 1 ab150IOC 1 pcyrs mean pcyrs2 mean pcyrs3 mean

. simqi, prval(1)

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------Pr(chall=1) | .0000187 .0000139 3.56e-06 .0000529

. simqi, fd(prval(1)) changex(ab_IR_150_IOC_a 0 1 ab_IR_150_IOC_b 0 1 ab150IOC 0 1)

First Difference: ab_IR_150_IOC_a 0 1 ab_IR_150_IOC_b 0 1 ab150IOC 0 1

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------dPr(chall = 1) | -1.56e-06 .0000126 -.0000182 .0000304

Model 6: Remove nuclear-only missiles, FFT, alter lower range threshold to 40 km, with better Year and Range data

. estsimp probit chall sqrtcmindist contig cincparity majpowa jointdem nukadum_df nukbdum_df ab_IR_BSRBM_FFT_a ab_IR_BSR > BM_FFT_b ab40FFT pcyrs pcyrs2 pcyrs3, robust cluster (id)

Iteration 0: log pseudolikelihood = -3405.7614 Iteration 1: log pseudolikelihood = -2725.5863 Iteration 2: log pseudolikelihood = -2523.7578 Iteration 3: log pseudolikelihood = -2475.103 Iteration 4: log pseudolikelihood = -2467.9051 Iteration 5: log pseudolikelihood = -2467.6431 Iteration 6: log pseudolikelihood = -2467.6427

Probit regression Number of obs = 1295374 Wald chi2(13) = 996.68 Prob > chi2 = 0.0000 Log pseudolikelihood = -2467.6427 Pseudo R2 = 0.2755

(Std. Err. adjusted for 37430 clusters in id) ------| Robust chall | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------sqrtcmindist | -.0144571 .0015534 -9.31 0.000 -.0175017 -.0114125 contig | .3727967 .0768222 4.85 0.000 .222228 .5233654 cincparity | .1408457 .0713896 1.97 0.049 .0009247 .2807666 majpowa | .220944 .1212848 1.82 0.069 -.0167698 .4586578 ab40FFT | .2741482 .1540418 1.78 0.075 -.0277681 .5760646 pcyrs | -.0593815 .0085386 -6.95 0.000 -.0761168 -.0426463 pcyrs2 | .0015532 .0003775 4.11 0.000 .0008132 .0022931 pcyrs3 | -.0000131 4.59e-06 -2.85 0.004 -.0000221 -4.08e-06 _cons | -2.467522 .0857335 -28.78 0.000 -2.635556 -2.299487 ------

Simulating main parameters. Please wait.... % of simulations completed: 7% 14% 21% 28% 35% 42% 50% 57% 64% 71% 78% 85% 92% 100%

Number of simulations : 1000 Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9 b10 b11 b12 b13 b14

. setx sqrtcmindist mean contig 0 cincparity mean majpowa 0 jointdem 0 nukadum_df 0 nukbdum_df 0 ab_IR_BSRBM_FFT_a 0 ab_ > IR_BSRBM_FFT_b 0 ab40FFT 0 pcyrs mean pcyrs2 mean pcyrs3 mean

. simqi, prval(1)

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------Pr(chall=1) | .0000205 5.58e-06 .0000115 .0000334

. setx sqrtcmindist mean contig 0 cincparity mean majpowa 0 jointdem 0 nukadum_df 0 nukbdum_df 0 ab_IR_BSRBM_FFT_a 1 ab_ > IR_BSRBM_FFT_b 0 ab40FFT 0 pcyrs mean pcyrs2 mean pcyrs3 mean

. simqi, prval(1)

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------Pr(chall=1) | .0000246 .0000135 7.26e-06 .0000574

. simqi, fd(prval(1)) changex(ab_IR_BSRBM_FFT_a 0 1)

First Difference: ab_IR_BSRBM_FFT_a 0 1

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------dPr(chall = 1) | 4.07e-06 .0000111 -.0000103 .000034

. setx sqrtcmindist mean contig 0 cincparity mean majpowa 0 jointdem 0 nukadum_df 0 nukbdum_df 0 ab_IR_BSRBM_FFT_a 0 ab_ > IR_BSRBM_FFT_b 1 ab40FFT 0 pcyrs mean pcyrs2 mean pcyrs3 mean

. simqi, prval(1)

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------Pr(chall=1) | 5.80e-06 3.88e-06 1.29e-06 .0000154

. simqi, fd(prval(1)) changex(ab_IR_BSRBM_FFT_b 0 1)

First Difference: ab_IR_BSRBM_FFT_b 0 1

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------dPr(chall = 1) | -.0000147 4.90e-06 -.0000258 -6.09e-06

. setx sqrtcmindist mean contig 0 cincparity mean majpowa 0 jointdem 0 nukadum_df 0 Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------Pr(chall=1) | .000022 .0000146 5.42e-06 .0000609

. simqi, fd(prval(1)) changex(ab_IR_BSRBM_FFT_a 0 1 ab_IR_BSRBM_FFT_b 0 1 ab40FFT 0 1)

First Difference: ab_IR_BSRBM_FFT_a 0 1 ab_IR_BSRBM_FFT_b 0 1 ab40FFT 0 1

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------dPr(chall = 1) | 1.52e-06 .0000128 -.0000144 .0000341

Model 7: Remove nuclear-only missiles, IOC, alter lower range threshold to 40 km, with better Year and Range data

. estsimp probit chall sqrtcmindist contig cincparity majpowa jointdem nukadum_df nukbdum_df ab_IR_BSRBM_IOC_a ab_IR_BSR > BM_IOC_b ab40IOC pcyrs pcyrs2 pcyrs3, robust cluster (id)

Iteration 0: log pseudolikelihood = -3405.7614 Iteration 1: log pseudolikelihood = -2723.7926 Iteration 2: log pseudolikelihood = -2521.2455 Iteration 3: log pseudolikelihood = -2472.5648 Iteration 4: log pseudolikelihood = -2465.4413 Iteration 5: log pseudolikelihood = -2465.187 Iteration 6: log pseudolikelihood = -2465.1866

Probit regression Number of obs = 1295374 Wald chi2(13) = 1011.15 Prob > chi2 = 0.0000 Log pseudolikelihood = -2465.1866 Pseudo R2 = 0.2762

(Std. Err. adjusted for 37430 clusters in id) ------| Robust chall | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------sqrtcmindist | -.0144557 .0015462 -9.35 0.000 -.0174862 -.0114252 contig | .3733013 .0767683 4.86 0.000 .2228381 .5237645 cincparity | .1391972 .0711671 1.96 0.050 -.0002878 .2786821 majpowa | .2133282 .1219605 1.75 0.080 -.0257101 .4523665 jointdem | -.1877922 .0606588 -3.10 0.002 -.3066814 -.0689031 nukadum_df | .5033336 .1181708 4.26 0.000 .2717231 .7349442 nukbdum_df | .522995 .0837271 6.25 0.000 .3588929 .687097 ab_IR_BS~C_a | .0332317 .0945689 0.35 0.725 -.1521199 .2185833 ab_IR_BS~C_b | -.3960633 .131426 -3.01 0.003 -.6536535 -.138473 ab40IOC | .3282247 .1672258 1.96 0.050 .0004681 .6559812 pcyrs | -.0594263 .0085425 -6.96 0.000 -.0761692 -.0426833 pcyrs2 | .0015579 .0003778 4.12 0.000 .0008173 .0022984 pcyrs3 | -.0000131 4.59e-06 -2.86 0.004 -.0000221 -4.13e-06 _cons | -2.467201 .0854746 -28.86 0.000 -2.634728 -2.299674 ------

Simulating main parameters. Please wait.... % of simulations completed: 7% 14% 21% 28% 35% 42% 50% 57% 64% 71% 78% 85% 92% 100%

Number of simulations : 1000 Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9 b10 b11 b12 b13 b14 . simqi, prval(1)

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------Pr(chall=1) | .0000207 5.45e-06 .0000123 .0000339

. setx sqrtcmindist mean contig 0 cincparity mean majpowa 0 jointdem 0 nukadum_df 0 nukbdum_df 0 ab_IR_BSRBM_IOC_a 1 ab_ > IR_BSRBM_IOC_b 0 ab40IOC 0 pcyrs mean pcyrs2 mean pcyrs3 mean

. simqi, prval(1)

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------Pr(chall=1) | .0000267 .0000151 7.76e-06 .0000649

. simqi, fd(prval(1)) changex(ab_IR_BSRBM_IOC_a 0 1)

First Difference: ab_IR_BSRBM_IOC_a 0 1

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------dPr(chall = 1) | 6.06e-06 .0000124 -9.72e-06 .000038

. setx sqrtcmindist mean contig 0 cincparity mean majpowa 0 jointdem 0 nukadum_df 0 nukbdum_df 0 ab_IR_BSRBM_IOC_a 0 ab_ > IR_BSRBM_IOC_b 1 ab40IOC 0 pcyrs mean pcyrs2 mean pcyrs3 mean

. simqi, prval(1)

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------Pr(chall=1) | 4.43e-06 3.76e-06 7.17e-07 .0000145

. simqi, fd(prval(1)) changex(ab_IR_BSRBM_IOC_b 0 1)

First Difference: ab_IR_BSRBM_IOC_b 0 1

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------dPr(chall = 1) | -.0000163 4.85e-06 -.0000274 -7.64e-06

. setx sqrtcmindist mean contig 0 cincparity mean majpowa 0 jointdem 0 nukadum_df 0 nukbdum_df 0 ab_IR_BSRBM_IOC_a 1 ab_ > IR_BSRBM_IOC_b 1 ab40IOC 1 pcyrs mean pcyrs2 mean pcyrs3 mean

. simqi, prval(1)

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------Pr(chall=1) | .0000214 .0000138 5.31e-06 .000059

. simqi, fd(prval(1)) changex(ab_IR_BSRBM_IOC_a 0 1 ab_IR_BSRBM_IOC_b 0 1 ab40IOC 0 1)

First Difference: ab_IR_BSRBM_IOC_a 0 1 ab_IR_BSRBM_IOC_b 0 1 ab40IOC 0 1

Quantity of Interest | Mean Std. Err. [95% Conf. Interval] ------+------dPr(chall = 1) | 7.23e-07 .0000117 -.0000145 .0000333

Appendix H. Model 1 - Average Marginal Effects

=" (1) (2) (3) (4) (5) (6) (7) =" chall chall chall chall chall chall chall

chall =" =" =" =" =" =" =" sqrtcmindist -0.0139*** -0.0139*** -0.0140*** -0.0143*** -0.0142*** -0.0142*** -0.0142*** =" (-9.10) (-9.05) (-9.12) (-9.28) (-9.31) (-9.29) (-9.32) contig 0.356*** 0.361*** 0.361*** 0.376*** 0.376*** 0.374*** 0.375*** =" (4.82) (4.86) (4.87) (4.93) (4.93) (4.89) (4.92) cincparity 0.162* 0.160* 0.159* 0.162* 0.163* 0.164* 0.164* =" (2.29) (2.26) (2.24) (2.28) (2.29) (2.32) (2.31) majpowa 0.0916 0.151 0.147 0.210 0.205 0.213 0.204 =" (0.73) (1.20) (1.12) (1.76) (1.69) (1.77) (1.68) jointdem -0.173** -0.178** -0.178** -0.192** -0.191** -0.188** -0.188**

=" (-2.86) (-2.97) (-2.97) (-3.13) (-3.10) (-3.09) (-3.09)

abmrange 0.344*** =" =" =" =" =" =" =" (4.40) =" =" =" =" =" =" nukadum_df 0.375** 0.395** 0.427** 0.513*** 0.511*** 0.506*** 0.511*** =" (2.86) (2.89) (3.27) (4.20) (4.33) (4.12) (4.33) bbmrange -0.211* =" =" =" =" =" ="

=" (-2.24) =" =" =" =" =" =" nukbdum_df 0.561*** 0.524*** 0.514*** 0.520*** 0.514*** 0.515*** 0.518*** =" (6.52) (5.56) (5.91) (6.09) (6.24) (5.86) (6.10) pcyrs -0.0591*** -0.0592*** -0.0593*** -0.0599*** -0.0599*** -0.0598*** -0.0597***

=" (-6.86) (-6.91) (-6.92) (-7.04) (-7.03) (-7.00) (-7.00) pcyrs2 0.00154*** 0.00154*** 0.00154*** 0.00156*** 0.00156*** 0.00156*** 0.00156*** =" (4.05) (4.07) (4.08) (4.15) (4.14) (4.13) (4.13) pcyrs3 -0.0000132** -0.0000131** -0.0000131** -0.0000131** -0.0000131** -0.0000131** -0.0000131** =" (-2.84) (-2.84) (-2.84) (-2.86) (-2.85) (-2.85) (-2.85) ab_MR_IR_150_FFT_a =" 0.247** =" =" =" =" =" =" =" (2.90) =" =" =" =" =" ab_MR_IR_150_FFT_b =" -0.138 =" =" =" =" =" =" =" (-1.44) =" =" =" =" ="

1 ab_MR_IR_150_IOC_a =" =" 0.228** =" =" =" =" =" =" =" (2.76) =" =" =" =" ab_MR_IR_150_IOC_b =" =" -0.138 =" =" =" =" =" =" =" (-1.45) =" =" =" =" ab_IR_150_FFT_a =" =" =" 0.0743 =" =" =" =" =" =" =" (0.84) =" =" =" ab_IR_150_FFT_b =" =" =" -0.204* =" =" ="

=" =" =" =" (-2.02) =" =" =" ab_IR_150_IOC_a =" =" =" =" 0.109 =" =" =" =" =" =" =" (1.28) =" =" ab_IR_150_IOC_b =" =" =" =" -0.239* =" =" =" =" =" =" =" (-2.33) =" =" ab_IR_BSRBM_FFT_a =" =" =" =" =" 0.0812 =" =" =" =" =" =" =" (0.94) =" ab_IR_BSRBM_FFT_b =" =" =" =" =" -0.179 =" =" =" =" =" =" =" (-1.89) ="

ab_IR_BSRBM_IOC_a =" =" =" =" =" =" 0.103

=" =" =" =" =" =" =" (1.25) ab_IR_BSRBM_IOC_b =" =" =" =" =" =" -0.224*

=" =" =" =" =" =" =" (-2.35)

_cons -2.503*** -2.499*** -2.495*** -2.482*** -2.485*** -2.484*** -2.485*** =" (-29.39) (-29.23) (-29.34) (-29.05) (-29.25) (-29.17) (-29.24)

N 1295374 1295374 1295374 1295374 1295374 1295374 1295374 t statistics in parentheses ="* p<0.05 ** p<0.01 *** p<0.001"

2 Appendix I. Model 2 – Rare Events Logit Model Results

Model 1: Mettler & Reiter's original model relogit chall sqrtcmindist contig cincparity majpowa jointdem abmrange nukadum bbmrange nukbdum pcyrs pcyrs2 pcyrs3

(109,410 missing values generated)

Corrected logit estimates Number of obs = 1295374

------| Robust chall | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------sqrtcmindist | -.0508776 .0040703 -12.50 0.000 -.0588553 -.0429 contig | .7795661 .1791918 4.35 0.000 .4283566 1.130776 cincparity | .454469 .1733296 2.62 0.009 .1147491 .7941888 majpowa | .2457163 .2912045 0.84 0.399 -.3250341 .8164666 jointdem | -.4332314 .1509145 -2.87 0.004 -.7290183 -.1374445 abmrange | 1.011438 .2038395 4.96 0.000 .6119193 1.410956 nukadum | .9992922 .3288132 3.04 0.002 .3548302 1.643754 bbmrange | -.8309057 .2637296 -3.15 0.002 -1.347806 -.3140052 nukbdum | 1.622008 .2571991 6.31 0.000 1.117907 2.126109 pcyrs | -.1842082 .0256714 -7.18 0.000 -.2345233 -.1338931 pcyrs2 | .0048302 .001172 4.12 0.000 .002533 .0071273 pcyrs3 | -.0000417 .0000145 -2.87 0.004 -.0000702 -.0000132 _cons | -4.778102 .2183752 -21.88 0.000 -5.206109 -4.350094 ------

Model 2: Mettler & Reiter's original model (FFT, 150km, no range maximum) with better Year and Range data, and fixed Nuk dummy variable relogit chall sqrtcmindist contig cincparity majpowa jointdem ab_MR_IR_150_FFT_a nukadum_df ab_MR_IR_150_FFT_b nukbdum_df pcyrs pcyrs2 pcyrs3

(109,410 missing values generated)

Corrected logit estimates Number of obs = 1295374

------| Robust chall | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------sqrtcmindist | -.0508476 .0040756 -12.48 0.000 -.0588357 -.0428595 contig | .79053 .178944 4.42 0.000 .4398063 1.141254 cincparity | .4620791 .1710687 2.70 0.007 .1267905 .7973677 majpowa | .3938553 .2703763 1.46 0.145 -.1360725 .9237832 jointdem | -.4718952 .1490491 -3.17 0.002 -.7640261 -.1797643 ab_MR_IR_150_FFT_a | .6566543 .2282462 2.88 0.004 .2092999 1.104009 nukadum_df | 1.151609 .3296832 3.49 0.000 .5054416 1.797776 ab_MR_IR_150_FFT_b | -.5086427 .2448643 -2.08 0.038 -.9885679 -.0287174 nukbdum_df | 1.407485 .244103 5.77 0.000 .9290517 1.885918 pcyrs | -.1820347 .0257845 -7.06 0.000 -.2325715 -.131498 pcyrs2 | .0046642 .0011772 3.96 0.000 .0023568 .0069716 pcyrs3 | -.0000393 .0000146 -2.69 0.007 -.0000679 -.0000107 _cons | -4.78666 .2165771 -22.10 0.000 -5.211143 -4.362176 relogit chall sqrtcmindist contig cincparity majpowa jointdem ab_MR_IR_150_IOC_a nukadum_df ab_MR_IR_150_IOC_b nukbdum_df pcyrs pcyrs2 pcyrs3

(109,410 missing values generated)

Corrected logit estimates Number of obs = 1295374

------| Robust chall | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------sqrtcmindist | -.0509952 .0040747 -12.52 0.000 -.0589814 -.043009 contig | .7938136 .1792328 4.43 0.000 .4425238 1.145103 cincparity | .4654576 .1714983 2.71 0.007 .1293271 .801588 majpowa | .3419653 .2894719 1.18 0.237 -.2253891 .9093197 jointdem | -.4713671 .1490827 -3.16 0.002 -.7635639 -.1791703 ab_MR_IR_150_IOC_a | .6217958 .2108871 2.95 0.003 .2084648 1.035127 nukadum_df | 1.269343 .300889 4.22 0.000 .6796117 1.859075 ab_MR_IR_150_IOC_b | -.5581457 .2331759 -2.39 0.017 -1.015162 -.1011294 nukbdum_df | 1.393205 .219626 6.34 0.000 .9627459 1.823664 pcyrs | -.1823285 .025783 -7.07 0.000 -.2328622 -.1317948 pcyrs2 | .0046651 .0011767 3.96 0.000 .0023588 .0069714 pcyrs3 | -.0000393 .0000146 -2.69 0.007 -.0000678 -.0000107 _cons | -4.777009 .2163701 -22.08 0.000 -5.201087 -4.352931 ------

Model 4: Remove nuclear-only missiles, remain consistent with original model (FFT, 150 km) with better Year and Range data, and fixed Nuk dummy variable relogit chall sqrtcmindist contig cincparity majpowa jointdem ab_IR_150_FFT_a nukadum_df ab_IR_150_FFT_b nukbdum_df pcyrs pcyrs2 pcyrs3

(109,410 missing values generated)

Corrected logit estimates Number of obs = 1295374

------| Robust chall | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------sqrtcmindist | -.0521522 .0040685 -12.82 0.000 -.0601263 -.0441782 contig | .8576465 .1860994 4.61 0.000 .4928984 1.222395 cincparity | .4518214 .1690982 2.67 0.008 .1203951 .7832477 majpowa | .4844392 .2695308 1.80 0.072 -.0438315 1.01271 jointdem | -.5334714 .1562662 -3.41 0.001 -.8397475 -.2271953 ab_IR_150_FFT_a | -.0242815 .2292145 -0.11 0.916 -.4735336 .4249706 nukadum_df | 1.621688 .3074742 5.27 0.000 1.01905 2.224326 ab_IR_150_FFT_b | -.6185377 .2351692 -2.63 0.009 -1.079461 -.1576145 nukbdum_df | 1.42206 .2084572 6.82 0.000 1.013491 1.830629 pcyrs | -.1835469 .0255836 -7.17 0.000 -.2336898 -.1334039 pcyrs2 | .004691 .001167 4.02 0.000 .0024038 .0069782 pcyrs3 | -.0000391 .0000144 -2.71 0.007 -.0000674 -.0000108 _cons | -4.73387 .2174833 -21.77 0.000 -5.160129 -4.30761 ------

Model 5: Remove nuclear-only missiles, remain consistent with original model (150 km), but IOC, better Year and Range data, and fixed Nuk dummy variable Corrected logit estimates Number of obs = 1295374

------| Robust chall | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------sqrtcmindist | -.0518232 .0040533 -12.79 0.000 -.0597676 -.0438788 contig | .853495 .1852365 4.61 0.000 .4904381 1.216552 cincparity | .4631942 .1694488 2.73 0.006 .1310807 .7953077 majpowa | .4738098 .2741872 1.73 0.084 -.0635872 1.011207 jointdem | -.5341757 .1577782 -3.39 0.001 -.8434153 -.224936 ab_IR_150_IOC_a | .1148641 .210471 0.55 0.585 -.2976515 .5273798 nukadum_df | 1.575181 .2767894 5.69 0.000 1.032684 2.117678 ab_IR_150_IOC_b | -.7328221 .2382675 -3.08 0.002 -1.199818 -.2658264 nukbdum_df | 1.409501 .1975327 7.14 0.000 1.022345 1.796658 pcyrs | -.182057 .025655 -7.10 0.000 -.2323398 -.1317742 pcyrs2 | .0046301 .0011736 3.95 0.000 .0023298 .0069304 pcyrs3 | -.0000384 .0000145 -2.65 0.008 -.0000669 -9.96e-06 _cons | -4.753304 .2175871 -21.85 0.000 -5.179767 -4.326841 ------

Model 6: Remove nuclear-only missiles, FFT, alter lower range threshold to 40 km, with better Year and Range data, and fixed Nuk dummy variable relogit chall sqrtcmindist contig cincparity majpowa jointdem ab_IR_BSRBM_FFT_a nukadum_df ab_IR_BSRBM_FFT_b nukbdum_df pcyrs pcyrs2 pcyrs3

(109,410 missing values generated)

Corrected logit estimates Number of obs = 1295374

------| Robust chall | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------sqrtcmindist | -.0521816 .0040571 -12.86 0.000 -.0601335 -.0442298 contig | .8461306 .1867048 4.53 0.000 .4801959 1.212065 cincparity | .474598 .168456 2.82 0.005 .1444304 .8047656 majpowa | .4893952 .2686812 1.82 0.069 -.0372103 1.016001 jointdem | -.4977766 .1514089 -3.29 0.001 -.7945327 -.2010206 ab_IR_BSRBM_FFT_a | .026562 .2347564 0.11 0.910 -.433552 .4866761 nukadum_df | 1.594652 .3114654 5.12 0.000 .9841906 2.205112 ab_IR_BSRBM_FFT_b | -.5741288 .2371249 -2.42 0.015 -1.038885 -.1093725 nukbdum_df | 1.423706 .2214646 6.43 0.000 .9896437 1.857769 pcyrs | -.1841274 .0256241 -7.19 0.000 -.2343497 -.133905 pcyrs2 | .0047345 .0011695 4.05 0.000 .0024424 .0070265 pcyrs3 | -.0000398 .0000145 -2.75 0.006 -.0000682 -.0000115 _cons | -4.737584 .2163281 -21.90 0.000 -5.161579 -4.313588 ------

Model 7: Remove nuclear-only missiles, IOC, alter lower range threshold to 40 km, with better Year and Range data, and fixed Nuk dummy variable relogit chall sqrtcmindist contig cincparity majpowa jointdem ab_IR_BSRBM_IOC_a nukadum_df ab_IR_BSRBM_IOC_b nukbdum_df pcyrs pcyrs2 pcyrs3

(109,410 missing values generated) chall | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------sqrtcmindist | -.0519822 .0040444 -12.85 0.000 -.0599091 -.0440552 contig | .8389384 .1845576 4.55 0.000 .4772121 1.200665 cincparity | .4874394 .1699011 2.87 0.004 .1544394 .8204394 majpowa | .4731417 .2750617 1.72 0.085 -.0659693 1.012253 jointdem | -.5057507 .1525638 -3.32 0.001 -.8047703 -.2067311 ab_IR_BSRBM_IOC_a | .1541088 .2104097 0.73 0.464 -.2582866 .5665041 nukadum_df | 1.556843 .2778736 5.60 0.000 1.012221 2.101466 ab_IR_BSRBM_IOC_b | -.7216742 .2358845 -3.06 0.002 -1.183999 -.2593491 nukbdum_df | 1.431799 .2059939 6.95 0.000 1.028059 1.83554 pcyrs | -.1829095 .0256297 -7.14 0.000 -.2331428 -.1326762 pcyrs2 | .0046761 .0011703 4.00 0.000 .0023823 .0069699 pcyrs3 | -.0000391 .0000145 -2.70 0.007 -.0000675 -.0000107 _cons | -4.749836 .2164481 -21.94 0.000 -5.174067 -4.325606 ------Appendix J. Model 2 - Average Marginal Effects

=" (1) (2) (3) (4) (5) (6) (7) =" chall chall chall chall chall chall chall

sqrtcmindist -0.0509*** -0.0508*** -0.0510*** -0.0522*** -0.0518*** -0.0522*** -0.0520***

=" (-12.50) (-12.48) (-12.52) (-12.82) (-12.79) (-12.86) (-12.85) contig 0.780*** 0.791*** 0.794*** 0.858*** 0.853*** 0.846*** 0.839*** =" (4.35) (4.42) (4.43) (4.61) (4.61) (4.53) (4.55) cincparity 0.454** 0.462** 0.465** 0.452** 0.463** 0.475** 0.487** =" (2.62) (2.70) (2.71) (2.67) (2.73) (2.82) (2.87) majpowa 0.246 0.394 0.342 0.484 0.474 0.489 0.473 =" (0.84) (1.46) (1.18) (1.80) (1.73) (1.82) (1.72) jointdem -0.433** -0.472** -0.471** -0.533*** -0.534*** -0.498** -0.506*** =" (-2.87) (-3.17) (-3.16) (-3.41) (-3.39) (-3.29) (-3.32)

abmrange 1.011*** =" =" =" =" =" =" =" (4.96) =" =" =" =" =" =" nukadum 0.999** =" =" =" =" =" =" =" (3.04) =" =" =" =" =" =" bbmrange -0.831** =" =" =" =" =" =" =" (-3.15) =" =" =" =" =" ="

nukbdum 1.622*** =" =" =" =" =" =" =" (6.31) =" =" =" =" =" =" pcyrs -0.184*** -0.182*** -0.182*** -0.184*** -0.182*** -0.184*** -0.183*** =" (-7.18) (-7.06) (-7.07) (-7.17) (-7.10) (-7.19) (-7.14)

pcyrs2 0.00483*** 0.00466*** 0.00467*** 0.00469*** 0.00463*** 0.00473*** 0.00468*** =" (4.12) (3.96) (3.96) (4.02) (3.95) (4.05) (4.00) pcyrs3 -0.0000417** -0.0000393** -0.0000393** -0.0000391** -0.0000384** -0.0000398** -0.0000391** =" (-2.87) (-2.69) (-2.69) (-2.71) (-2.65) (-2.75) (-2.70) ab_MR_IR_150_FFT_a =" 0.657** =" =" =" =" =" =" =" (2.88) =" =" =" =" =" nukadum_df =" 1.152*** 1.269*** 1.622*** 1.575*** 1.595*** 1.557*** =" =" (3.49) (4.22) (5.27) (5.69) (5.12) (5.60)

ab_MR_IR_150_FFT_b =" -0.509* =" =" =" =" ="

1 =" =" (-2.08) =" =" =" =" =" nukbdum_df =" 1.407*** 1.393*** 1.422*** 1.410*** 1.424*** 1.432*** =" =" (5.77) (6.34) (6.82) (7.14) (6.43) (6.95) ab_MR_IR_150_IOC_a =" =" 0.622** =" =" =" =" =" =" =" (2.95) =" =" =" =" ab_MR_IR_150_IOC_b =" =" -0.558* =" =" =" =" =" =" =" (-2.39) =" =" =" ="

ab_IR_150_FFT_a =" =" =" -0.0243 =" =" =" =" =" =" =" (-0.11) =" =" =" ab_IR_150_FFT_b =" =" =" -0.619** =" =" =" =" =" =" =" (-2.63) =" =" =" ab_IR_150_IOC_a =" =" =" =" 0.115 =" =" =" =" =" =" =" (0.55) =" =" ab_IR_150_IOC_b =" =" =" =" -0.733** =" =" =" =" =" =" =" (-3.08) =" =" ab_IR_BSRBM_FFT_a =" =" =" =" =" 0.0266 ="

=" =" =" =" =" =" (0.11) ="

ab_IR_BSRBM_FFT_b =" =" =" =" =" -0.574* =" =" =" =" =" =" =" (-2.42) ="

ab_IR_BSRBM_IOC_a =" =" =" =" =" =" 0.154 =" =" =" =" =" =" =" (0.73) ab_IR_BSRBM_IOC_b =" =" =" =" =" =" -0.722** =" =" =" =" =" =" =" (-3.06)

_cons -4.778*** -4.787*** -4.777*** -4.734*** -4.753*** -4.738*** -4.750*** =" (-21.88) (-22.10) (-22.08) (-21.77) (-21.85) (-21.90) (-21.94)

N 1295374 1295374 1295374 1295374 1295374 1295374 1295374

t statistics in parentheses ="* p<0.05 ** p<0.01 *** p<0.001"

2 Appendix K. Model 2 - Average Marginal Effects

=" (1) (2) (3) (4) (5) (6) (7) =" chall chall chall chall chall chall chall

sqrtcmindist -0.0509*** -0.0508*** -0.0510*** -0.0522*** -0.0518*** -0.0522*** -0.0520***

=" (-12.50) (-12.48) (-12.52) (-12.82) (-12.79) (-12.86) (-12.85) contig 0.780*** 0.791*** 0.794*** 0.858*** 0.853*** 0.846*** 0.839*** =" (4.35) (4.42) (4.43) (4.61) (4.61) (4.53) (4.55) cincparity 0.454** 0.462** 0.465** 0.452** 0.463** 0.475** 0.487** =" (2.62) (2.70) (2.71) (2.67) (2.73) (2.82) (2.87) majpowa 0.246 0.394 0.342 0.484 0.474 0.489 0.473 =" (0.84) (1.46) (1.18) (1.80) (1.73) (1.82) (1.72) jointdem -0.433** -0.472** -0.471** -0.533*** -0.534*** -0.498** -0.506*** =" (-2.87) (-3.17) (-3.16) (-3.41) (-3.39) (-3.29) (-3.32)

abmrange 1.011*** =" =" =" =" =" =" =" (4.96) =" =" =" =" =" =" nukadum 0.999** =" =" =" =" =" =" =" (3.04) =" =" =" =" =" =" bbmrange -0.831** =" =" =" =" =" =" =" (-3.15) =" =" =" =" =" ="

nukbdum 1.622*** =" =" =" =" =" =" =" (6.31) =" =" =" =" =" =" pcyrs -0.184*** -0.182*** -0.182*** -0.184*** -0.182*** -0.184*** -0.183*** =" (-7.18) (-7.06) (-7.07) (-7.17) (-7.10) (-7.19) (-7.14)

pcyrs2 0.00483*** 0.00466*** 0.00467*** 0.00469*** 0.00463*** 0.00473*** 0.00468*** =" (4.12) (3.96) (3.96) (4.02) (3.95) (4.05) (4.00) pcyrs3 -0.0000417** -0.0000393** -0.0000393** -0.0000391** -0.0000384** -0.0000398** -0.0000391** =" (-2.87) (-2.69) (-2.69) (-2.71) (-2.65) (-2.75) (-2.70) ab_MR_IR_150_FFT_a =" 0.657** =" =" =" =" =" =" =" (2.88) =" =" =" =" =" nukadum_df =" 1.152*** 1.269*** 1.622*** 1.575*** 1.595*** 1.557*** =" =" (3.49) (4.22) (5.27) (5.69) (5.12) (5.60)

ab_MR_IR_150_FFT_b =" -0.509* =" =" =" =" ="

1 =" =" (-2.08) =" =" =" =" =" nukbdum_df =" 1.407*** 1.393*** 1.422*** 1.410*** 1.424*** 1.432*** =" =" (5.77) (6.34) (6.82) (7.14) (6.43) (6.95) ab_MR_IR_150_IOC_a =" =" 0.622** =" =" =" =" =" =" =" (2.95) =" =" =" =" ab_MR_IR_150_IOC_b =" =" -0.558* =" =" =" =" =" =" =" (-2.39) =" =" =" ="

ab_IR_150_FFT_a =" =" =" -0.0243 =" =" =" =" =" =" =" (-0.11) =" =" =" ab_IR_150_FFT_b =" =" =" -0.619** =" =" =" =" =" =" =" (-2.63) =" =" =" ab_IR_150_IOC_a =" =" =" =" 0.115 =" =" =" =" =" =" =" (0.55) =" =" ab_IR_150_IOC_b =" =" =" =" -0.733** =" =" =" =" =" =" =" (-3.08) =" =" ab_IR_BSRBM_FFT_a =" =" =" =" =" 0.0266 ="

=" =" =" =" =" =" (0.11) ="

ab_IR_BSRBM_FFT_b =" =" =" =" =" -0.574* =" =" =" =" =" =" =" (-2.42) ="

ab_IR_BSRBM_IOC_a =" =" =" =" =" =" 0.154 =" =" =" =" =" =" =" (0.73) ab_IR_BSRBM_IOC_b =" =" =" =" =" =" -0.722** =" =" =" =" =" =" =" (-3.06)

_cons -4.778*** -4.787*** -4.777*** -4.734*** -4.753*** -4.738*** -4.750*** =" (-21.88) (-22.10) (-22.08) (-21.77) (-21.85) (-21.90) (-21.94)

N 1295374 1295374 1295374 1295374 1295374 1295374 1295374

t statistics in parentheses ="* p<0.05 ** p<0.01 *** p<0.001"

2 Appendix L. Model 3 Average Marginal Effects

=" (1) =" =" abmrange 0.000312***

=" (4.15)

bbmrange -0.000136 =" (-1.42)

abmbbm -0.0000896 =" (-0.73)

N 1295374 t statistics in parentheses ="* p<0.05 ** p<0.01 *** p<0.001"

1 Appendix M. Model 3 - Average Marginal Effects

=" (1) =" =" ab_IR_BSRBM_IOC_a 0.0000281

=" (0.35)

ab_IR_BSRBM_IOC_b -0.000335** =" (-2.93)

ab40IOC 0.000277 =" (1.95)

N 1295374 t statistics in parentheses ="* p<0.05 ** p<0.01 *** p<0.001"

1 Appendix N: Model 4 - Rare Events Logit Model Results - Nuclear Weapons-Ballistic Missile Interaction

Model 1: Mettler & Reiter's original model relogit chall sqrtcmindist contig cincparity majpowa jointdem nukbmadum nuknobmadum nonukbmadum nukbmbdum nuknobmbdum nonukbmbdum pcyrs pcyrs2 pcyrs3

(109,410 missing values generated)

Corrected logit estimates Number of obs = 1295374

------| Robust chall | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------sqrtcmindist | -.0516797 .0041312 -12.51 0.000 -.0597767 -.0435827 contig | .7469003 .1803631 4.14 0.000 .3933951 1.100406 cincparity | .4743106 .175303 2.71 0.007 .1307231 .8178982 majpowa | .3606597 .2699899 1.34 0.182 -.1685108 .8898302 jointdem | -.4378814 .1505524 -2.91 0.004 -.7329587 -.142804 nukbmadum | 1.865622 .2752575 6.78 0.000 1.326127 2.405117 nuknobmadum | 1.800801 .3502636 5.14 0.000 1.114297 2.487305 nonukbmadum | 1.199456 .1822715 6.58 0.000 .8422109 1.556702 nukbmbdum | .6447849 .1952815 3.30 0.001 .2620401 1.02753 nuknobmbdum | 2.123943 .2469542 8.60 0.000 1.639922 2.607965 nonukbmbdum | -.4300719 .2400951 -1.79 0.073 -.9006497 .0405058 pcyrs | -.1841707 .025602 -7.19 0.000 -.2343497 -.1339917 pcyrs2 | .0048785 .0011752 4.15 0.000 .0025752 .0071819 pcyrs3 | -.0000424 .0000146 -2.90 0.004 -.000071 -.0000138 _cons | -4.811657 .2180467 -22.07 0.000 -5.23902 -4.384293 ------

Models 2 & 3 end up being the equivalent of Model 4 & 5, and this makes logical sense because of how you have to define the dummy variables as 1 only if In Range (IR): (1) nukbmdum = use the same ranges as the ab_mr_range because that is exactly what they (incorrectly) did for the whole analysis (2) nuknobmdum = nuk would need to be delivered by aircraft, I have (subjectively) selected 1400 km as the range (3) bmnonukdum = use the the ranges that I created that cap maximum range at 4000 km in an effort to weed out nuclear-only bms

Model 4: relogit chall sqrtcmindist contig cincparity majpowa jointdem nukbmadum_150_FFT_IR nuknobmadum_150_FFT_IR bmnonukadum_150_FFT_IR nukbmbdum_150_FFT_IR nuknobmbdum_150_FFT_IR bmnonukbdum_150_FFT_IR pcyrs pcyrs2 pcyrs3

(109,410 missing values generated)

Corrected logit estimates Number of obs = 1295374

------| Robust chall | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------sqrtcmindist | -.0503784 .0040018 -12.59 0.000 -.0582219 -.042535 contig | .7666741 .1820669 4.21 0.000 .4098295 1.123519 cincparity | .4015646 .1750879 2.29 0.022 .0583986 .7447306 majpowa | 1.012309 .2571722 3.94 0.000 .5082605 1.516357 nuknobmbdum_150_FFT_IR | .1383023 .3949883 0.35 0.726 -.6358606 .9124651 bmnonukbdum_150_FFT_IR | -.167403 .2197339 -0.76 0.446 -.5980736 .2632676 pcyrs | -.1883548 .0255329 -7.38 0.000 -.2383983 -.1383113 pcyrs2 | .0049885 .0011685 4.27 0.000 .0026983 .0072787 pcyrs3 | -.0000433 .0000146 -2.98 0.003 -.0000719 -.0000148 _cons | -4.70817 .210763 -22.34 0.000 -5.121258 -4.295082 ------

Model 5: relogit chall sqrtcmindist contig cincparity majpowa jointdem nukbmadum_150_IOC_IR nuknobmadum_150_IOC_IR bmnonukadum_150_IOC_IR nukbmbdum_150_IOC_IR nuknobmbdum_150_IOC_IR bmnonukbdum_150_IOC_IR pcyrs pcyrs2 pcyrs3

(109,410 missing values generated)

Corrected logit estimates Number of obs = 1295374

------| Robust chall | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------sqrtcmindist | -.0504798 .0039663 -12.73 0.000 -.0582535 -.042706 contig | .7494399 .1796701 4.17 0.000 .397293 1.101587 cincparity | .4393446 .1766046 2.49 0.013 .0932059 .7854833 majpowa | .958479 .2820258 3.40 0.001 .4057187 1.511239 jointdem | -.3820772 .1515016 -2.52 0.012 -.6790149 -.0851394 nukbmadum_150_IOC_IR | 1.166979 .2819553 4.14 0.000 .6143572 1.719602 nuknobmadum_150_IOC_IR | .2940391 .263679 1.12 0.265 -.2227622 .8108403 bmnonukadum_150_IOC_IR | .8048454 .185169 4.35 0.000 .4419209 1.16777 nukbmbdum_150_IOC_IR | .3848963 .2625229 1.47 0.143 -.1296391 .8994317 nuknobmbdum_150_IOC_IR | .9278336 .3003304 3.09 0.002 .3391968 1.51647 bmnonukbdum_150_IOC_IR | -.1926277 .2117729 -0.91 0.363 -.6076949 .2224395 pcyrs | -.1864796 .0256476 -7.27 0.000 -.2367479 -.1362112 pcyrs2 | .0048878 .0011754 4.16 0.000 .0025841 .0071916 pcyrs3 | -.0000422 .0000147 -2.88 0.004 -.0000709 -.0000135 _cons | -4.699475 .2066533 -22.74 0.000 -5.104508 -4.294442 ------

Model 6: relogit chall sqrtcmindist contig cincparity majpowa jointdem nukbmadum_BSRBM_FFT_IR nuknobmadum_BSRBM_FFT_IR bmnonukadum_BSRBM_FFT_IR nukbmbdum_BSRBM_FFT_IR nuknobmbdum_BSRBM_FFT_IR bmnonukbdum_BSRBM_FFT_IR pcyrs pcyrs2 pcyrs3

(109,410 missing values generated)

Corrected logit estimates Number of obs = 1295374

------| Robust chall | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------sqrtcmindist | -.0504692 .0040397 -12.49 0.000 -.0583869 -.0425515 contig | .7629387 .1823922 4.18 0.000 .4054565 1.120421 cincparity | .4010488 .1750628 2.29 0.022 .0579319 .7441656 majpowa | 1.022969 .2606571 3.92 0.000 .5120908 1.533848 jointdem | -.415775 .1566865 -2.65 0.008 -.7228748 -.1086752 nukbmadum_BSRBM_FFT_IR | 1.079179 .2599894 4.15 0.000 .5696088 1.588748 pcyrs2 | .0050273 .00117 4.30 0.000 .002734 .0073205 pcyrs3 | -.0000437 .0000146 -3.00 0.003 -.0000723 -.0000152 _cons | -4.699629 .2112985 -22.24 0.000 -5.113767 -4.285492 ------

Model 7: relogit chall sqrtcmindist contig cincparity majpowa jointdem nukbmadum_BSRBM_IOC_IR nuknobmadum_BSRBM_IOC_IR bmnonukadum_BSRBM_IOC_IR nukbmbdum_BSRBM_IOC_IR nuknobmbdum_BSRBM_IOC_IR bmnonukbdum_BSRBM_IOC_IR pcyrs pcyrs2 pcyrs3

(109,410 missing values generated)

Corrected logit estimates Number of obs = 1295374

------| Robust chall | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------sqrtcmindist | -.0504452 .0039615 -12.73 0.000 -.0582096 -.0426808 contig | .734525 .1794463 4.09 0.000 .3828167 1.086233 cincparity | .4382192 .1771998 2.47 0.013 .0909139 .7855245 majpowa | .9532838 .2752656 3.46 0.001 .4137732 1.492794 jointdem | -.3746715 .1519061 -2.47 0.014 -.672402 -.0769411 nukbmadum_BSRBM_IOC_IR | 1.20078 .2846776 4.22 0.000 .6428223 1.758738 nuknobmadum_BSRBM_IOC_IR | .3126578 .2643987 1.18 0.237 -.2055542 .8308698 bmnonukadum_BSRBM_IOC_IR | .8575269 .1838935 4.66 0.000 .4971022 1.217952 nukbmbdum_BSRBM_IOC_IR | .3555758 .2670978 1.33 0.183 -.1679263 .8790778 nuknobmbdum_BSRBM_IOC_IR | .9918111 .3086173 3.21 0.001 .3869322 1.59669 bmnonukbdum_BSRBM_IOC_IR | -.2443984 .2123413 -1.15 0.250 -.6605798 .171783 pcyrs | -.1849938 .0256185 -7.22 0.000 -.2352052 -.1347824 pcyrs2 | .0048021 .0011752 4.09 0.000 .0024989 .0071054 pcyrs3 | -.0000412 .0000146 -2.82 0.005 -.0000699 -.0000125 _cons | -4.706015 .20631 -22.81 0.000 -5.110375 -4.301655 ------Appendix O. Model 4 - Average Marginal Effects

=" (1) (2) (3) =" chall chall chall sqrtcmindist -0.0517*** -0.0505*** -0.0504***

=" (-12.51) (-12.73) (-12.73)

contig 0.747*** 0.749*** 0.735*** =" (4.14) (4.17) (4.09)

cincparity 0.474** 0.439* 0.438* =" (2.71) (2.49) (2.47) majpowa 0.361 0.958*** 0.953*** =" (1.34) (3.40) (3.46) jointdem -0.438** -0.382* -0.375* =" (-2.91) (-2.52) (-2.47) nukbmadum 1.866*** =" ="

=" (6.78) =" =" nuknobmadum 1.801*** =" =" =" (5.14) =" =" nonukbmadum 1.199*** =" =" =" (6.58) =" ="

nukbmbdum 0.645*** =" =" =" (3.30) =" ="

1 nuknobmbdum 2.124*** =" =" =" (8.60) =" =" nonukbmbdum -0.430 =" =" =" (-1.79) =" =" pcyrs -0.184*** -0.186*** -0.185*** =" (-7.19) (-7.27) (-7.22)

pcyrs2 0.00488*** 0.00489*** 0.00480***

=" (4.15) (4.16) (4.09)

pcyrs3 -0.0000424** -0.0000422** -0.0000412** =" (-2.90) (-2.88) (-2.82) nukbmadum_150_IOC_IR =" 1.167*** =" =" =" (4.14) =" nuknobmadum_150_IOC_IR =" 0.294 =" =" =" (1.12) =" bmnonukadum_150_IOC_IR =" 0.805*** =" =" =" (4.35) ="

nukbmbdum_150_IOC_IR =" 0.385 ="

=" =" (1.47) =" nuknobmbdum_150_IOC_IR =" 0.928** ="

=" =" (3.09) =" bmnonukbdum_150_IOC_IR =" -0.193 ="

2 =" =" (-0.91) =" nukbmadum_BSRBM_IOC_IR =" =" 1.201*** =" =" =" (4.22) nuknobmadum_BSRBM_IOC_IR =" =" 0.313 =" =" =" (1.18) bmnonukadum_BSRBM_IOC_IR =" =" 0.858*** =" =" =" (4.66)

nukbmbdum_BSRBM_IOC_IR =" =" 0.356

=" =" =" (1.33) nuknobmbdum_BSRBM_IOC_IR =" =" 0.992** =" =" =" (3.21) bmnonukbdum_BSRBM_IOC_IR =" =" -0.244 =" =" =" (-1.15)

_cons -4.812*** -4.699*** -4.706*** =" (-22.07) (-22.74) (-22.81)

N 1295374 1295374 1295374 t statistics in parentheses

="* p<0.05 ** p<0.01 *** p<0.001"

3 Appendix P. Model 4 - Average Marginal Effects

=" (1) (2) (3) =" chall chall chall sqrtcmindist -0.0517*** -0.0505*** -0.0504***

=" (-12.51) (-12.73) (-12.73)

contig 0.747*** 0.749*** 0.735*** =" (4.14) (4.17) (4.09)

cincparity 0.474** 0.439* 0.438* =" (2.71) (2.49) (2.47) majpowa 0.361 0.958*** 0.953*** =" (1.34) (3.40) (3.46) jointdem -0.438** -0.382* -0.375* =" (-2.91) (-2.52) (-2.47) nukbmadum 1.866*** =" ="

=" (6.78) =" =" nuknobmadum 1.801*** =" =" =" (5.14) =" =" nonukbmadum 1.199*** =" =" =" (6.58) =" ="

nukbmbdum 0.645*** =" =" =" (3.30) =" ="

1 nuknobmbdum 2.124*** =" =" =" (8.60) =" =" nonukbmbdum -0.430 =" =" =" (-1.79) =" =" pcyrs -0.184*** -0.186*** -0.185*** =" (-7.19) (-7.27) (-7.22)

pcyrs2 0.00488*** 0.00489*** 0.00480***

=" (4.15) (4.16) (4.09)

pcyrs3 -0.0000424** -0.0000422** -0.0000412** =" (-2.90) (-2.88) (-2.82) nukbmadum_150_IOC_IR =" 1.167*** =" =" =" (4.14) =" nuknobmadum_150_IOC_IR =" 0.294 =" =" =" (1.12) =" bmnonukadum_150_IOC_IR =" 0.805*** =" =" =" (4.35) ="

nukbmbdum_150_IOC_IR =" 0.385 ="

=" =" (1.47) =" nuknobmbdum_150_IOC_IR =" 0.928** ="

=" =" (3.09) =" bmnonukbdum_150_IOC_IR =" -0.193 ="

2 =" =" (-0.91) =" nukbmadum_BSRBM_IOC_IR =" =" 1.201*** =" =" =" (4.22) nuknobmadum_BSRBM_IOC_IR =" =" 0.313 =" =" =" (1.18) bmnonukadum_BSRBM_IOC_IR =" =" 0.858*** =" =" =" (4.66)

nukbmbdum_BSRBM_IOC_IR =" =" 0.356

=" =" =" (1.33) nuknobmbdum_BSRBM_IOC_IR =" =" 0.992** =" =" =" (3.21) bmnonukbdum_BSRBM_IOC_IR =" =" -0.244 =" =" =" (-1.15)

_cons -4.812*** -4.699*** -4.706*** =" (-22.07) (-22.74) (-22.81)

N 1295374 1295374 1295374 t statistics in parentheses

="* p<0.05 ** p<0.01 *** p<0.001"

3 Appendix Q. Model 5 - Average Marginal Effects

=" (1) (2) (3) (4) (5) (6) (7) =" chall chall chall chall chall chall chall chall =" =" =" =" =" =" =" sqrtcmindist -0.0147*** -0.0148*** -0.0148*** -0.0153*** -0.0152*** -0.0153*** -0.0153*** =" (-8.81) (-8.78) (-8.83) (-9.01) (-9.03) (-9.09) (-9.06) contig 0.331*** 0.334*** 0.335*** 0.349*** 0.350*** 0.366*** 0.350*** =" (4.23) (4.25) (4.26) (4.30) (4.31) (4.40) (4.29) cincparity 0.156* 0.156* 0.156* 0.149* 0.151* 0.156* 0.158*

=" (2.05) (2.05) (2.04) (1.96) (1.99) (2.05) (2.06)

majpowa 0.139 0.178 0.177 0.209 0.210 0.205 0.216 =" (1.11) (1.41) (1.36) (1.69) (1.71) (1.64) (1.76) jointdem -0.150* -0.155* -0.154* -0.174** -0.175** -0.169** -0.166** =" (-2.44) (-2.55) (-2.53) (-2.77) (-2.76) (-2.74) (-2.67) abmrange 0.207** =" =" =" =" =" =" =" (2.69) =" =" =" =" =" =" nukadum_df 0.486*** 0.513*** 0.530*** 0.612*** 0.599*** 0.637*** 0.602*** =" (3.77) (3.85) (4.26) (4.91) (5.06) (5.13) (5.14)

bbmrange -0.278** =" =" =" =" =" ="

=" (-2.68) =" =" =" =" =" =" nukbdum_df 0.564*** 0.581*** 0.560*** 0.516*** 0.509*** 0.509*** 0.515*** =" (5.86) (6.02) (6.24) (5.78) (5.86) (5.45) (5.72) pcyrs -0.0589*** -0.0590*** -0.0590*** -0.0599*** -0.0593*** -0.0600*** -0.0596*** =" (-6.66) (-6.71) (-6.70) (-6.86) (-6.80) (-6.85) (-6.80) pcyrs2 0.00147*** 0.00147*** 0.00147*** 0.00149*** 0.00146*** 0.00150*** 0.00149*** =" (3.74) (3.77) (3.77) (3.86) (3.79) (3.88) (3.83) pcyrs3 -0.0000118* -0.0000117* -0.0000117* -0.0000118* -0.0000116* -0.0000120* -0.0000119* =" (-2.47) (-2.47) (-2.48) (-2.52) (-2.46) (-2.56) (-2.52)

ab_MR_IR_150_FFT_a =" 0.127 =" =" =" =" ="

=" =" (1.39) =" =" =" =" ="

ab_MR_IR_150_FFT_b =" -0.280** =" =" =" =" ="

1 =" =" (-2.70) =" =" =" =" =" ab_MR_IR_150_IOC_a =" =" 0.117 =" =" =" =" =" =" =" (1.33) =" =" =" ="

ab_MR_IR_150_IOC_b =" =" -0.285** =" =" =" =" =" =" =" (-2.73) =" =" =" =" ab_IR_150_FFT_a =" =" =" -0.0553 =" =" =" =" =" =" =" (-0.58) =" =" =" ab_IR_150_FFT_b =" =" =" -0.280** =" =" =" =" =" =" =" (-2.61) =" =" =" ab_IR_150_IOC_a =" =" =" =" -0.0172 =" =" =" =" =" =" =" (-0.18) =" =" ab_IR_150_IOC_b =" =" =" =" -0.332** =" ="

=" =" =" =" =" (-3.07) =" ="

ab_IR_BSRBM_FFT_a =" =" =" =" =" -0.133 =" =" =" =" =" =" =" (-1.35) =" ab_IR_BSRBM_FFT_b =" =" =" =" =" -0.222* =" =" =" =" =" =" =" (-2.18) ="

ab_IR_BSRBM_IOC_a =" =" =" =" =" =" -0.0323 =" =" =" =" =" =" =" (-0.36) ab_IR_BSRBM_IOC_b =" =" =" =" =" =" -0.304** =" =" =" =" =" =" =" (-2.97)

_cons -2.492*** -2.486*** -2.486*** -2.465*** -2.473*** -2.474*** -2.472***

=" (-27.90) (-27.68) (-27.87) (-27.37) (-27.63) (-27.70) (-27.65)

N 1295374 1295374 1295374 1295374 1295374 1295374 1295374 t statistics in parentheses ="* p<0.05 ** p<0.01 *** p<0.001"

2 Appendix R. Model 5 - Average Marginal Effects

=" (1) (2) (3) (4) (5) (6) (7) =" chall chall chall chall chall chall chall chall =" =" =" =" =" =" =" sqrtcmindist -0.0147*** -0.0148*** -0.0148*** -0.0153*** -0.0152*** -0.0153*** -0.0153*** =" (-8.81) (-8.78) (-8.83) (-9.01) (-9.03) (-9.09) (-9.06) contig 0.331*** 0.334*** 0.335*** 0.349*** 0.350*** 0.366*** 0.350*** =" (4.23) (4.25) (4.26) (4.30) (4.31) (4.40) (4.29) cincparity 0.156* 0.156* 0.156* 0.149* 0.151* 0.156* 0.158*

=" (2.05) (2.05) (2.04) (1.96) (1.99) (2.05) (2.06)

majpowa 0.139 0.178 0.177 0.209 0.210 0.205 0.216 =" (1.11) (1.41) (1.36) (1.69) (1.71) (1.64) (1.76) jointdem -0.150* -0.155* -0.154* -0.174** -0.175** -0.169** -0.166** =" (-2.44) (-2.55) (-2.53) (-2.77) (-2.76) (-2.74) (-2.67) abmrange 0.207** =" =" =" =" =" =" =" (2.69) =" =" =" =" =" =" nukadum_df 0.486*** 0.513*** 0.530*** 0.612*** 0.599*** 0.637*** 0.602*** =" (3.77) (3.85) (4.26) (4.91) (5.06) (5.13) (5.14)

bbmrange -0.278** =" =" =" =" =" ="

=" (-2.68) =" =" =" =" =" =" nukbdum_df 0.564*** 0.581*** 0.560*** 0.516*** 0.509*** 0.509*** 0.515*** =" (5.86) (6.02) (6.24) (5.78) (5.86) (5.45) (5.72) pcyrs -0.0589*** -0.0590*** -0.0590*** -0.0599*** -0.0593*** -0.0600*** -0.0596*** =" (-6.66) (-6.71) (-6.70) (-6.86) (-6.80) (-6.85) (-6.80) pcyrs2 0.00147*** 0.00147*** 0.00147*** 0.00149*** 0.00146*** 0.00150*** 0.00149*** =" (3.74) (3.77) (3.77) (3.86) (3.79) (3.88) (3.83) pcyrs3 -0.0000118* -0.0000117* -0.0000117* -0.0000118* -0.0000116* -0.0000120* -0.0000119* =" (-2.47) (-2.47) (-2.48) (-2.52) (-2.46) (-2.56) (-2.52)

ab_MR_IR_150_FFT_a =" 0.127 =" =" =" =" ="

=" =" (1.39) =" =" =" =" ="

ab_MR_IR_150_FFT_b =" -0.280** =" =" =" =" ="

1 =" =" (-2.70) =" =" =" =" =" ab_MR_IR_150_IOC_a =" =" 0.117 =" =" =" =" =" =" =" (1.33) =" =" =" ="

ab_MR_IR_150_IOC_b =" =" -0.285** =" =" =" =" =" =" =" (-2.73) =" =" =" =" ab_IR_150_FFT_a =" =" =" -0.0553 =" =" =" =" =" =" =" (-0.58) =" =" =" ab_IR_150_FFT_b =" =" =" -0.280** =" =" =" =" =" =" =" (-2.61) =" =" =" ab_IR_150_IOC_a =" =" =" =" -0.0172 =" =" =" =" =" =" =" (-0.18) =" =" ab_IR_150_IOC_b =" =" =" =" -0.332** =" ="

=" =" =" =" =" (-3.07) =" ="

ab_IR_BSRBM_FFT_a =" =" =" =" =" -0.133 =" =" =" =" =" =" =" (-1.35) =" ab_IR_BSRBM_FFT_b =" =" =" =" =" -0.222* =" =" =" =" =" =" =" (-2.18) ="

ab_IR_BSRBM_IOC_a =" =" =" =" =" =" -0.0323 =" =" =" =" =" =" =" (-0.36) ab_IR_BSRBM_IOC_b =" =" =" =" =" =" -0.304** =" =" =" =" =" =" =" (-2.97)

_cons -2.492*** -2.486*** -2.486*** -2.465*** -2.473*** -2.474*** -2.472***

=" (-27.90) (-27.68) (-27.87) (-27.37) (-27.63) (-27.70) (-27.65)

N 1295374 1295374 1295374 1295374 1295374 1295374 1295374 t statistics in parentheses ="* p<0.05 ** p<0.01 *** p<0.001"

2 Appendix S. Model 6 - One- vs Two Sided

=" (1) =" =" ab_IR_BSRBM_IOC_a -0.0000588

=" (-0.75)

ab_IR_BSRBM_IOC_b -0.000323** =" (-2.87)

ab40IOC 0.000183 =" (1.33)

N 1295374 t statistics in parentheses ="* p<0.05 ** p<0.01 *** p<0.001"

1 Appendix T. Model 6 - One- vs. Two-Sided

=" (1) =" =" ab_MR_IR_150_IOC_a 0.000100

=" (1.31)

ab_MR_IR_150_IOC_b -0.000199 =" (-1.92)

abmbbmIOC -0.0000483 =" (-0.37)

N 1295374 t statistics in parentheses ="* p<0.05 ** p<0.01 *** p<0.001"

1 Appendix U. Objectives in the 39 Challenge Cases Involving Missiles with Sufficient Range

1. Yugo 1992: Bosnia – stop the Serbian part of Bosnia from breaking away (status quo) 2. Algeria 1975: Mauritania – supporting opposition group (revision) 3. Algeria 1975: Morocco – supporting opposition group (revision) 4. Libya 1978: Chad – supporting a Chadian opposition group (revision) 5. Libya 1979: Chad – supporting a Chadian opposition group (revision) 6. Libya 1983: Chad – supporting a Chadian opposition group (revision) 7. Libya 1986: Chad – supporting a Chadian opposition group (revision) 8. Libya 1980: Tunisia – supporting and training Tunisia rebel group (revision) 9. Libya 1985: Tunisia – expels Tunisian workers, trying to press unity (revision) 10. Libya 1983: Sudan – buildup of troops on border looks like overthrow (revision) 11. Libya 1984: Sudan – bomber strikes Khartoum, Libyan incitement (revision) 12. Libya 1980: Egypt – calls for jihad against Sadat (revision) 13. Iran 2001: Azerbaijan – intercept ships surveying for oil (status quo) 14. Turkey 1998: Cyprus – threatens force to prevent deploy of S-300 (status quo) 15. Turkey 1998: Syria – mobilize troops to retaliate for PKK support (status quo) 16. Iraq 1980: Iran – getting back part of Shatt-al-Arab (revision) 17. Iraq 1990: Egypt – part of coalition in larger attempt to annex Kuwait (revision) 18. Iraq 1976: Syria – attempting to weaken Assad, an ally of Iran (revision) 19. Iraq 1990: Syria – part of coalition in larger attempt to annex Kuwait (revision) 20. Iraq 1990: Israel – attempting to annex Kuwait (revision) 21. Iraq 1990: Saudi – part of coalition in larger attempt to annex Kuwait (revision) 22. Iraq 1994: Saudi – build up troops trying to coerce end to sanctions (revision) 23. Iraq 1990: Kuwait – attempt to annex Kuwait (revision) 24. Iraq 1991: Kuwait – invades island to retrieve abandoned mil equip (status quo) 25. Iraq 1994: Kuwait – build up troops trying to coerce end to sanctions (revision) 26. Egypt 1977: Libya – clash over disputed boundary (status quo/revision) 27. Egypt 1983: Libya – Egypt backs up its ally, Sudan after Libya threat (status quo) 28. Egypt 1984: Libya – Egypt backs up its ally, Sudan after Libya threat (status quo) 29. Egypt 1985: Libya – retaliation for Egypt Air Hijacking (status quo) 30. Egypt. 1969: Israel – attempt to take back Sinai (revision) 31. Egypt 1973: Israel – attempt to take back Sinai (revision) 32. Syria 1980: Jordan – mobilized to dissuade Jordan from supporting Israel (status) 33. Syria 1973: Israel – attempt to take back Golan Heights (revision) 34. Syria 1976: Israel – attempting to stop partition of Lebanon from civ war (status) 35. Syria 1985: Israel – deploys SAMs to compel Israel to stop flyovers (status) 36. Yemen Rep 1979: Yemen Arab – Assassinations, declarations of war (revision) 37. DPRK 1996: ROK – submarine lands, kills ROK soldiers (antagonize) 38. DPRK 2006: ROK – nuclear test (status quo) 39. ROK 1993 DPRK – latter threatens to withdraw fuel rods & from NPT (status quo) Appendix V. Additional Crisis Cases in International Crisis Behavior v 12 (2008-2015)

Additional crises included in the v12 ICB dataset but not the v10 that was used. 20 crises added through 2015, 14 of which involve one or both sides possession missiles.

1. Djibouti-Eritrea (2008): #456 summary 2. Preah Vihear Temple I (2008): #457 summary 3. Russo-Georgian War (2008): #458 summary (Russia missiles) 4. North Korea Nuclear IV: Satellite Launch (2009): #459 summary (DPRK, ROK missiles) 5. Chad-Sudan V (2009): #460 summary 6. Cheonan Sinking (2010): #461 summary (DPRK, ROK missiles) 7. Yeonpyeong Island (2010): #462 summary (DPRK, ROK missiles) 8. Preah Vihear Temple II (2011): #463 summary 9. Libyan Civil War (2011): #464 summary (Libya missiles) (excluded) 10. Cote d’Ivoire Presidential Crisis (2011): #465 summary 11. Sudan-South Sudan (2011): #466 summary 12. Scarborough Shoal (2012): #467 summary (China missiles) 13. Syria-Turkey Border Incidents (2012): #468 summary (Syria, Turkeymissiles) 14. North Korea Nuclear V (2013): #469 summary (DPRK, ROK missiles) 15. Syria Chemical Weapons (2013): #470 summary (Syria missiles) 16. Crimea-Donbass (2014): #471 summary (Russia, Ukraine missiles) 17. Chinese Oil Rig (2014): #472 summary (China, Vietnam missiles) 18. India-Pakistan Border Firing (2014): #473 summary (India, Pakistan missiles) 19. Korean Land Mine (2015): #475 summary (DPRK, ROK missiles) 20. Turkey-Russia Jet Incident (2015): #476 summary (Turkey, Russia missiles)

Sources: Brecher, M. and J. Wilkenfeld (2000). A Study of Crisis. Ann Arbor: University of Michigan Press.

Brecher, M., J Wilkenfeld, K. Beardsley, P. James and D. Quinn (2017). International Crisis Behavior Data Codebook, Version 12.http://sites.duke.edu/icbdata/data-collections/

750 Solid Spin HE O N 195 Liquid Spin HE O N 350 Liquid Spin HE O N 482 Solid Inertial HE O N 985 Liquid Inertial HE O N 450 Solid Spin HE O N 771 LiquidIntertial HE R N