WIOD 2014 Analysis
dezembro 18, 2025
Source:vignettes/articles/wiod_2014_analysis.Rmd
wiod_2014_analysis.RmdIntroduction
This vignette demonstrates how to use the fio package to
analyze the World Input-Output Database (WIOD) 2014 (2016 release). We
will use fio to download the data, parse it, create a
Multi-Regional Input-Output Matrix (miom) object, and
perform various analyses including technical coefficients, Leontief
inverse, multipliers, bilateral trade, and spillover effects.
Data
We download the data from the WIOD website and parse it to create a
Multi-Regional Input-Output Matrix (miom) object.
# download wiod 2016 release
fio::download_wiod(year = 2016)This downloads a zip file with the WIOD 2016 release, containing IOTs from 2000 to 2014. We’ll use the 2014 IOTs for this analysis.
Creating the MIOM Object
We parse the wiot object to create the miom
object. Calling names() we find that the wiot
object has the following columns:
- IndustryCode
- IndustryDescription
- Country
- RNr
- Year
Then the intermediate transactions matrix, followed by the final demand matrix.
# load the data
load("WIOT2014_October16_ROW.RData")
# columns
names(wiot)
#> [1] "IndustryCode" "IndustryDescription" "Country"
#> [4] "RNr" "Year" "AUS1"
#> [7] "AUS2" "AUS3" "AUS4"
#> [10] "AUS5" "AUS6" "AUS7"
#> [13] "AUS8" "AUS9" "AUS10"
#> [16] "AUS11" "AUS12" "AUS13"
#> [19] "AUS14" "AUS15" "AUS16"
#> [22] "AUS17" "AUS18" "AUS19"
#> [25] "AUS20" "AUS21" "AUS22"
#> [28] "AUS23" "AUS24" "AUS25"
#> [31] "AUS26" "AUS27" "AUS28"
#> [34] "AUS29" "AUS30" "AUS31"
#> [37] "AUS32" "AUS33" "AUS34"
#> [40] "AUS35" "AUS36" "AUS37"
#> [43] "AUS38" "AUS39" "AUS40"
#> [46] "AUS41" "AUS42" "AUS43"
#> [49] "AUS44" "AUS45" "AUS46"
#> [52] "AUS47" "AUS48" "AUS49"
#> [55] "AUS50" "AUS51" "AUS52"
#> [58] "AUS53" "AUS54" "AUS55"
#> [61] "AUS56" "AUT1" "AUT2"
#> [64] "AUT3" "AUT4" "AUT5"
#> [67] "AUT6" "AUT7" "AUT8"
#> [70] "AUT9" "AUT10" "AUT11"
#> [73] "AUT12" "AUT13" "AUT14"
#> [76] "AUT15" "AUT16" "AUT17"
#> [79] "AUT18" "AUT19" "AUT20"
#> [82] "AUT21" "AUT22" "AUT23"
#> [85] "AUT24" "AUT25" "AUT26"
#> [88] "AUT27" "AUT28" "AUT29"
#> [91] "AUT30" "AUT31" "AUT32"
#> [94] "AUT33" "AUT34" "AUT35"
#> [97] "AUT36" "AUT37" "AUT38"
#> [100] "AUT39" "AUT40" "AUT41"
#> [103] "AUT42" "AUT43" "AUT44"
#> [106] "AUT45" "AUT46" "AUT47"
#> [109] "AUT48" "AUT49" "AUT50"
#> [112] "AUT51" "AUT52" "AUT53"
#> [115] "AUT54" "AUT55" "AUT56"
#> [118] "BEL1" "BEL2" "BEL3"
#> [121] "BEL4" "BEL5" "BEL6"
#> [124] "BEL7" "BEL8" "BEL9"
#> [127] "BEL10" "BEL11" "BEL12"
#> [130] "BEL13" "BEL14" "BEL15"
#> [133] "BEL16" "BEL17" "BEL18"
#> [136] "BEL19" "BEL20" "BEL21"
#> [139] "BEL22" "BEL23" "BEL24"
#> [142] "BEL25" "BEL26" "BEL27"
#> [145] "BEL28" "BEL29" "BEL30"
#> [148] "BEL31" "BEL32" "BEL33"
#> [151] "BEL34" "BEL35" "BEL36"
#> [154] "BEL37" "BEL38" "BEL39"
#> [157] "BEL40" "BEL41" "BEL42"
#> [160] "BEL43" "BEL44" "BEL45"
#> [163] "BEL46" "BEL47" "BEL48"
#> [166] "BEL49" "BEL50" "BEL51"
#> [169] "BEL52" "BEL53" "BEL54"
#> [172] "BEL55" "BEL56" "BGR1"
#> [175] "BGR2" "BGR3" "BGR4"
#> [178] "BGR5" "BGR6" "BGR7"
#> [181] "BGR8" "BGR9" "BGR10"
#> [184] "BGR11" "BGR12" "BGR13"
#> [187] "BGR14" "BGR15" "BGR16"
#> [190] "BGR17" "BGR18" "BGR19"
#> [193] "BGR20" "BGR21" "BGR22"
#> [196] "BGR23" "BGR24" "BGR25"
#> [199] "BGR26" "BGR27" "BGR28"
#> [202] "BGR29" "BGR30" "BGR31"
#> [205] "BGR32" "BGR33" "BGR34"
#> [208] "BGR35" "BGR36" "BGR37"
#> [211] "BGR38" "BGR39" "BGR40"
#> [214] "BGR41" "BGR42" "BGR43"
#> [217] "BGR44" "BGR45" "BGR46"
#> [220] "BGR47" "BGR48" "BGR49"
#> [223] "BGR50" "BGR51" "BGR52"
#> [226] "BGR53" "BGR54" "BGR55"
#> [229] "BGR56" "BRA1" "BRA2"
#> [232] "BRA3" "BRA4" "BRA5"
#> [235] "BRA6" "BRA7" "BRA8"
#> [238] "BRA9" "BRA10" "BRA11"
#> [241] "BRA12" "BRA13" "BRA14"
#> [244] "BRA15" "BRA16" "BRA17"
#> [247] "BRA18" "BRA19" "BRA20"
#> [250] "BRA21" "BRA22" "BRA23"
#> [253] "BRA24" "BRA25" "BRA26"
#> [256] "BRA27" "BRA28" "BRA29"
#> [259] "BRA30" "BRA31" "BRA32"
#> [262] "BRA33" "BRA34" "BRA35"
#> [265] "BRA36" "BRA37" "BRA38"
#> [268] "BRA39" "BRA40" "BRA41"
#> [271] "BRA42" "BRA43" "BRA44"
#> [274] "BRA45" "BRA46" "BRA47"
#> [277] "BRA48" "BRA49" "BRA50"
#> [280] "BRA51" "BRA52" "BRA53"
#> [283] "BRA54" "BRA55" "BRA56"
#> [286] "CAN1" "CAN2" "CAN3"
#> [289] "CAN4" "CAN5" "CAN6"
#> [292] "CAN7" "CAN8" "CAN9"
#> [295] "CAN10" "CAN11" "CAN12"
#> [298] "CAN13" "CAN14" "CAN15"
#> [301] "CAN16" "CAN17" "CAN18"
#> [304] "CAN19" "CAN20" "CAN21"
#> [307] "CAN22" "CAN23" "CAN24"
#> [310] "CAN25" "CAN26" "CAN27"
#> [313] "CAN28" "CAN29" "CAN30"
#> [316] "CAN31" "CAN32" "CAN33"
#> [319] "CAN34" "CAN35" "CAN36"
#> [322] "CAN37" "CAN38" "CAN39"
#> [325] "CAN40" "CAN41" "CAN42"
#> [328] "CAN43" "CAN44" "CAN45"
#> [331] "CAN46" "CAN47" "CAN48"
#> [334] "CAN49" "CAN50" "CAN51"
#> [337] "CAN52" "CAN53" "CAN54"
#> [340] "CAN55" "CAN56" "CHE1"
#> [343] "CHE2" "CHE3" "CHE4"
#> [346] "CHE5" "CHE6" "CHE7"
#> [349] "CHE8" "CHE9" "CHE10"
#> [352] "CHE11" "CHE12" "CHE13"
#> [355] "CHE14" "CHE15" "CHE16"
#> [358] "CHE17" "CHE18" "CHE19"
#> [361] "CHE20" "CHE21" "CHE22"
#> [364] "CHE23" "CHE24" "CHE25"
#> [367] "CHE26" "CHE27" "CHE28"
#> [370] "CHE29" "CHE30" "CHE31"
#> [373] "CHE32" "CHE33" "CHE34"
#> [376] "CHE35" "CHE36" "CHE37"
#> [379] "CHE38" "CHE39" "CHE40"
#> [382] "CHE41" "CHE42" "CHE43"
#> [385] "CHE44" "CHE45" "CHE46"
#> [388] "CHE47" "CHE48" "CHE49"
#> [391] "CHE50" "CHE51" "CHE52"
#> [394] "CHE53" "CHE54" "CHE55"
#> [397] "CHE56" "CHN1" "CHN2"
#> [400] "CHN3" "CHN4" "CHN5"
#> [403] "CHN6" "CHN7" "CHN8"
#> [406] "CHN9" "CHN10" "CHN11"
#> [409] "CHN12" "CHN13" "CHN14"
#> [412] "CHN15" "CHN16" "CHN17"
#> [415] "CHN18" "CHN19" "CHN20"
#> [418] "CHN21" "CHN22" "CHN23"
#> [421] "CHN24" "CHN25" "CHN26"
#> [424] "CHN27" "CHN28" "CHN29"
#> [427] "CHN30" "CHN31" "CHN32"
#> [430] "CHN33" "CHN34" "CHN35"
#> [433] "CHN36" "CHN37" "CHN38"
#> [436] "CHN39" "CHN40" "CHN41"
#> [439] "CHN42" "CHN43" "CHN44"
#> [442] "CHN45" "CHN46" "CHN47"
#> [445] "CHN48" "CHN49" "CHN50"
#> [448] "CHN51" "CHN52" "CHN53"
#> [451] "CHN54" "CHN55" "CHN56"
#> [454] "CYP1" "CYP2" "CYP3"
#> [457] "CYP4" "CYP5" "CYP6"
#> [460] "CYP7" "CYP8" "CYP9"
#> [463] "CYP10" "CYP11" "CYP12"
#> [466] "CYP13" "CYP14" "CYP15"
#> [469] "CYP16" "CYP17" "CYP18"
#> [472] "CYP19" "CYP20" "CYP21"
#> [475] "CYP22" "CYP23" "CYP24"
#> [478] "CYP25" "CYP26" "CYP27"
#> [481] "CYP28" "CYP29" "CYP30"
#> [484] "CYP31" "CYP32" "CYP33"
#> [487] "CYP34" "CYP35" "CYP36"
#> [490] "CYP37" "CYP38" "CYP39"
#> [493] "CYP40" "CYP41" "CYP42"
#> [496] "CYP43" "CYP44" "CYP45"
#> [499] "CYP46" "CYP47" "CYP48"
#> [502] "CYP49" "CYP50" "CYP51"
#> [505] "CYP52" "CYP53" "CYP54"
#> [508] "CYP55" "CYP56" "CZE1"
#> [511] "CZE2" "CZE3" "CZE4"
#> [514] "CZE5" "CZE6" "CZE7"
#> [517] "CZE8" "CZE9" "CZE10"
#> [520] "CZE11" "CZE12" "CZE13"
#> [523] "CZE14" "CZE15" "CZE16"
#> [526] "CZE17" "CZE18" "CZE19"
#> [529] "CZE20" "CZE21" "CZE22"
#> [532] "CZE23" "CZE24" "CZE25"
#> [535] "CZE26" "CZE27" "CZE28"
#> [538] "CZE29" "CZE30" "CZE31"
#> [541] "CZE32" "CZE33" "CZE34"
#> [544] "CZE35" "CZE36" "CZE37"
#> [547] "CZE38" "CZE39" "CZE40"
#> [550] "CZE41" "CZE42" "CZE43"
#> [553] "CZE44" "CZE45" "CZE46"
#> [556] "CZE47" "CZE48" "CZE49"
#> [559] "CZE50" "CZE51" "CZE52"
#> [562] "CZE53" "CZE54" "CZE55"
#> [565] "CZE56" "DEU1" "DEU2"
#> [568] "DEU3" "DEU4" "DEU5"
#> [571] "DEU6" "DEU7" "DEU8"
#> [574] "DEU9" "DEU10" "DEU11"
#> [577] "DEU12" "DEU13" "DEU14"
#> [580] "DEU15" "DEU16" "DEU17"
#> [583] "DEU18" "DEU19" "DEU20"
#> [586] "DEU21" "DEU22" "DEU23"
#> [589] "DEU24" "DEU25" "DEU26"
#> [592] "DEU27" "DEU28" "DEU29"
#> [595] "DEU30" "DEU31" "DEU32"
#> [598] "DEU33" "DEU34" "DEU35"
#> [601] "DEU36" "DEU37" "DEU38"
#> [604] "DEU39" "DEU40" "DEU41"
#> [607] "DEU42" "DEU43" "DEU44"
#> [610] "DEU45" "DEU46" "DEU47"
#> [613] "DEU48" "DEU49" "DEU50"
#> [616] "DEU51" "DEU52" "DEU53"
#> [619] "DEU54" "DEU55" "DEU56"
#> [622] "DNK1" "DNK2" "DNK3"
#> [625] "DNK4" "DNK5" "DNK6"
#> [628] "DNK7" "DNK8" "DNK9"
#> [631] "DNK10" "DNK11" "DNK12"
#> [634] "DNK13" "DNK14" "DNK15"
#> [637] "DNK16" "DNK17" "DNK18"
#> [640] "DNK19" "DNK20" "DNK21"
#> [643] "DNK22" "DNK23" "DNK24"
#> [646] "DNK25" "DNK26" "DNK27"
#> [649] "DNK28" "DNK29" "DNK30"
#> [652] "DNK31" "DNK32" "DNK33"
#> [655] "DNK34" "DNK35" "DNK36"
#> [658] "DNK37" "DNK38" "DNK39"
#> [661] "DNK40" "DNK41" "DNK42"
#> [664] "DNK43" "DNK44" "DNK45"
#> [667] "DNK46" "DNK47" "DNK48"
#> [670] "DNK49" "DNK50" "DNK51"
#> [673] "DNK52" "DNK53" "DNK54"
#> [676] "DNK55" "DNK56" "ESP1"
#> [679] "ESP2" "ESP3" "ESP4"
#> [682] "ESP5" "ESP6" "ESP7"
#> [685] "ESP8" "ESP9" "ESP10"
#> [688] "ESP11" "ESP12" "ESP13"
#> [691] "ESP14" "ESP15" "ESP16"
#> [694] "ESP17" "ESP18" "ESP19"
#> [697] "ESP20" "ESP21" "ESP22"
#> [700] "ESP23" "ESP24" "ESP25"
#> [703] "ESP26" "ESP27" "ESP28"
#> [706] "ESP29" "ESP30" "ESP31"
#> [709] "ESP32" "ESP33" "ESP34"
#> [712] "ESP35" "ESP36" "ESP37"
#> [715] "ESP38" "ESP39" "ESP40"
#> [718] "ESP41" "ESP42" "ESP43"
#> [721] "ESP44" "ESP45" "ESP46"
#> [724] "ESP47" "ESP48" "ESP49"
#> [727] "ESP50" "ESP51" "ESP52"
#> [730] "ESP53" "ESP54" "ESP55"
#> [733] "ESP56" "EST1" "EST2"
#> [736] "EST3" "EST4" "EST5"
#> [739] "EST6" "EST7" "EST8"
#> [742] "EST9" "EST10" "EST11"
#> [745] "EST12" "EST13" "EST14"
#> [748] "EST15" "EST16" "EST17"
#> [751] "EST18" "EST19" "EST20"
#> [754] "EST21" "EST22" "EST23"
#> [757] "EST24" "EST25" "EST26"
#> [760] "EST27" "EST28" "EST29"
#> [763] "EST30" "EST31" "EST32"
#> [766] "EST33" "EST34" "EST35"
#> [769] "EST36" "EST37" "EST38"
#> [772] "EST39" "EST40" "EST41"
#> [775] "EST42" "EST43" "EST44"
#> [778] "EST45" "EST46" "EST47"
#> [781] "EST48" "EST49" "EST50"
#> [784] "EST51" "EST52" "EST53"
#> [787] "EST54" "EST55" "EST56"
#> [790] "FIN1" "FIN2" "FIN3"
#> [793] "FIN4" "FIN5" "FIN6"
#> [796] "FIN7" "FIN8" "FIN9"
#> [799] "FIN10" "FIN11" "FIN12"
#> [802] "FIN13" "FIN14" "FIN15"
#> [805] "FIN16" "FIN17" "FIN18"
#> [808] "FIN19" "FIN20" "FIN21"
#> [811] "FIN22" "FIN23" "FIN24"
#> [814] "FIN25" "FIN26" "FIN27"
#> [817] "FIN28" "FIN29" "FIN30"
#> [820] "FIN31" "FIN32" "FIN33"
#> [823] "FIN34" "FIN35" "FIN36"
#> [826] "FIN37" "FIN38" "FIN39"
#> [829] "FIN40" "FIN41" "FIN42"
#> [832] "FIN43" "FIN44" "FIN45"
#> [835] "FIN46" "FIN47" "FIN48"
#> [838] "FIN49" "FIN50" "FIN51"
#> [841] "FIN52" "FIN53" "FIN54"
#> [844] "FIN55" "FIN56" "FRA1"
#> [847] "FRA2" "FRA3" "FRA4"
#> [850] "FRA5" "FRA6" "FRA7"
#> [853] "FRA8" "FRA9" "FRA10"
#> [856] "FRA11" "FRA12" "FRA13"
#> [859] "FRA14" "FRA15" "FRA16"
#> [862] "FRA17" "FRA18" "FRA19"
#> [865] "FRA20" "FRA21" "FRA22"
#> [868] "FRA23" "FRA24" "FRA25"
#> [871] "FRA26" "FRA27" "FRA28"
#> [874] "FRA29" "FRA30" "FRA31"
#> [877] "FRA32" "FRA33" "FRA34"
#> [880] "FRA35" "FRA36" "FRA37"
#> [883] "FRA38" "FRA39" "FRA40"
#> [886] "FRA41" "FRA42" "FRA43"
#> [889] "FRA44" "FRA45" "FRA46"
#> [892] "FRA47" "FRA48" "FRA49"
#> [895] "FRA50" "FRA51" "FRA52"
#> [898] "FRA53" "FRA54" "FRA55"
#> [901] "FRA56" "GBR1" "GBR2"
#> [904] "GBR3" "GBR4" "GBR5"
#> [907] "GBR6" "GBR7" "GBR8"
#> [910] "GBR9" "GBR10" "GBR11"
#> [913] "GBR12" "GBR13" "GBR14"
#> [916] "GBR15" "GBR16" "GBR17"
#> [919] "GBR18" "GBR19" "GBR20"
#> [922] "GBR21" "GBR22" "GBR23"
#> [925] "GBR24" "GBR25" "GBR26"
#> [928] "GBR27" "GBR28" "GBR29"
#> [931] "GBR30" "GBR31" "GBR32"
#> [934] "GBR33" "GBR34" "GBR35"
#> [937] "GBR36" "GBR37" "GBR38"
#> [940] "GBR39" "GBR40" "GBR41"
#> [943] "GBR42" "GBR43" "GBR44"
#> [946] "GBR45" "GBR46" "GBR47"
#> [949] "GBR48" "GBR49" "GBR50"
#> [952] "GBR51" "GBR52" "GBR53"
#> [955] "GBR54" "GBR55" "GBR56"
#> [958] "GRC1" "GRC2" "GRC3"
#> [961] "GRC4" "GRC5" "GRC6"
#> [964] "GRC7" "GRC8" "GRC9"
#> [967] "GRC10" "GRC11" "GRC12"
#> [970] "GRC13" "GRC14" "GRC15"
#> [973] "GRC16" "GRC17" "GRC18"
#> [976] "GRC19" "GRC20" "GRC21"
#> [979] "GRC22" "GRC23" "GRC24"
#> [982] "GRC25" "GRC26" "GRC27"
#> [985] "GRC28" "GRC29" "GRC30"
#> [988] "GRC31" "GRC32" "GRC33"
#> [991] "GRC34" "GRC35" "GRC36"
#> [994] "GRC37" "GRC38" "GRC39"
#> [997] "GRC40" "GRC41" "GRC42"
#> [1000] "GRC43" "GRC44" "GRC45"
#> [1003] "GRC46" "GRC47" "GRC48"
#> [1006] "GRC49" "GRC50" "GRC51"
#> [1009] "GRC52" "GRC53" "GRC54"
#> [1012] "GRC55" "GRC56" "HRV1"
#> [1015] "HRV2" "HRV3" "HRV4"
#> [1018] "HRV5" "HRV6" "HRV7"
#> [1021] "HRV8" "HRV9" "HRV10"
#> [1024] "HRV11" "HRV12" "HRV13"
#> [1027] "HRV14" "HRV15" "HRV16"
#> [1030] "HRV17" "HRV18" "HRV19"
#> [1033] "HRV20" "HRV21" "HRV22"
#> [1036] "HRV23" "HRV24" "HRV25"
#> [1039] "HRV26" "HRV27" "HRV28"
#> [1042] "HRV29" "HRV30" "HRV31"
#> [1045] "HRV32" "HRV33" "HRV34"
#> [1048] "HRV35" "HRV36" "HRV37"
#> [1051] "HRV38" "HRV39" "HRV40"
#> [1054] "HRV41" "HRV42" "HRV43"
#> [1057] "HRV44" "HRV45" "HRV46"
#> [1060] "HRV47" "HRV48" "HRV49"
#> [1063] "HRV50" "HRV51" "HRV52"
#> [1066] "HRV53" "HRV54" "HRV55"
#> [1069] "HRV56" "HUN1" "HUN2"
#> [1072] "HUN3" "HUN4" "HUN5"
#> [1075] "HUN6" "HUN7" "HUN8"
#> [1078] "HUN9" "HUN10" "HUN11"
#> [1081] "HUN12" "HUN13" "HUN14"
#> [1084] "HUN15" "HUN16" "HUN17"
#> [1087] "HUN18" "HUN19" "HUN20"
#> [1090] "HUN21" "HUN22" "HUN23"
#> [1093] "HUN24" "HUN25" "HUN26"
#> [1096] "HUN27" "HUN28" "HUN29"
#> [1099] "HUN30" "HUN31" "HUN32"
#> [1102] "HUN33" "HUN34" "HUN35"
#> [1105] "HUN36" "HUN37" "HUN38"
#> [1108] "HUN39" "HUN40" "HUN41"
#> [1111] "HUN42" "HUN43" "HUN44"
#> [1114] "HUN45" "HUN46" "HUN47"
#> [1117] "HUN48" "HUN49" "HUN50"
#> [1120] "HUN51" "HUN52" "HUN53"
#> [1123] "HUN54" "HUN55" "HUN56"
#> [1126] "IDN1" "IDN2" "IDN3"
#> [1129] "IDN4" "IDN5" "IDN6"
#> [1132] "IDN7" "IDN8" "IDN9"
#> [1135] "IDN10" "IDN11" "IDN12"
#> [1138] "IDN13" "IDN14" "IDN15"
#> [1141] "IDN16" "IDN17" "IDN18"
#> [1144] "IDN19" "IDN20" "IDN21"
#> [1147] "IDN22" "IDN23" "IDN24"
#> [1150] "IDN25" "IDN26" "IDN27"
#> [1153] "IDN28" "IDN29" "IDN30"
#> [1156] "IDN31" "IDN32" "IDN33"
#> [1159] "IDN34" "IDN35" "IDN36"
#> [1162] "IDN37" "IDN38" "IDN39"
#> [1165] "IDN40" "IDN41" "IDN42"
#> [1168] "IDN43" "IDN44" "IDN45"
#> [1171] "IDN46" "IDN47" "IDN48"
#> [1174] "IDN49" "IDN50" "IDN51"
#> [1177] "IDN52" "IDN53" "IDN54"
#> [1180] "IDN55" "IDN56" "IND1"
#> [1183] "IND2" "IND3" "IND4"
#> [1186] "IND5" "IND6" "IND7"
#> [1189] "IND8" "IND9" "IND10"
#> [1192] "IND11" "IND12" "IND13"
#> [1195] "IND14" "IND15" "IND16"
#> [1198] "IND17" "IND18" "IND19"
#> [1201] "IND20" "IND21" "IND22"
#> [1204] "IND23" "IND24" "IND25"
#> [1207] "IND26" "IND27" "IND28"
#> [1210] "IND29" "IND30" "IND31"
#> [1213] "IND32" "IND33" "IND34"
#> [1216] "IND35" "IND36" "IND37"
#> [1219] "IND38" "IND39" "IND40"
#> [1222] "IND41" "IND42" "IND43"
#> [1225] "IND44" "IND45" "IND46"
#> [1228] "IND47" "IND48" "IND49"
#> [1231] "IND50" "IND51" "IND52"
#> [1234] "IND53" "IND54" "IND55"
#> [1237] "IND56" "IRL1" "IRL2"
#> [1240] "IRL3" "IRL4" "IRL5"
#> [1243] "IRL6" "IRL7" "IRL8"
#> [1246] "IRL9" "IRL10" "IRL11"
#> [1249] "IRL12" "IRL13" "IRL14"
#> [1252] "IRL15" "IRL16" "IRL17"
#> [1255] "IRL18" "IRL19" "IRL20"
#> [1258] "IRL21" "IRL22" "IRL23"
#> [1261] "IRL24" "IRL25" "IRL26"
#> [1264] "IRL27" "IRL28" "IRL29"
#> [1267] "IRL30" "IRL31" "IRL32"
#> [1270] "IRL33" "IRL34" "IRL35"
#> [1273] "IRL36" "IRL37" "IRL38"
#> [1276] "IRL39" "IRL40" "IRL41"
#> [1279] "IRL42" "IRL43" "IRL44"
#> [1282] "IRL45" "IRL46" "IRL47"
#> [1285] "IRL48" "IRL49" "IRL50"
#> [1288] "IRL51" "IRL52" "IRL53"
#> [1291] "IRL54" "IRL55" "IRL56"
#> [1294] "ITA1" "ITA2" "ITA3"
#> [1297] "ITA4" "ITA5" "ITA6"
#> [1300] "ITA7" "ITA8" "ITA9"
#> [1303] "ITA10" "ITA11" "ITA12"
#> [1306] "ITA13" "ITA14" "ITA15"
#> [1309] "ITA16" "ITA17" "ITA18"
#> [1312] "ITA19" "ITA20" "ITA21"
#> [1315] "ITA22" "ITA23" "ITA24"
#> [1318] "ITA25" "ITA26" "ITA27"
#> [1321] "ITA28" "ITA29" "ITA30"
#> [1324] "ITA31" "ITA32" "ITA33"
#> [1327] "ITA34" "ITA35" "ITA36"
#> [1330] "ITA37" "ITA38" "ITA39"
#> [1333] "ITA40" "ITA41" "ITA42"
#> [1336] "ITA43" "ITA44" "ITA45"
#> [1339] "ITA46" "ITA47" "ITA48"
#> [1342] "ITA49" "ITA50" "ITA51"
#> [1345] "ITA52" "ITA53" "ITA54"
#> [1348] "ITA55" "ITA56" "JPN1"
#> [1351] "JPN2" "JPN3" "JPN4"
#> [1354] "JPN5" "JPN6" "JPN7"
#> [1357] "JPN8" "JPN9" "JPN10"
#> [1360] "JPN11" "JPN12" "JPN13"
#> [1363] "JPN14" "JPN15" "JPN16"
#> [1366] "JPN17" "JPN18" "JPN19"
#> [1369] "JPN20" "JPN21" "JPN22"
#> [1372] "JPN23" "JPN24" "JPN25"
#> [1375] "JPN26" "JPN27" "JPN28"
#> [1378] "JPN29" "JPN30" "JPN31"
#> [1381] "JPN32" "JPN33" "JPN34"
#> [1384] "JPN35" "JPN36" "JPN37"
#> [1387] "JPN38" "JPN39" "JPN40"
#> [1390] "JPN41" "JPN42" "JPN43"
#> [1393] "JPN44" "JPN45" "JPN46"
#> [1396] "JPN47" "JPN48" "JPN49"
#> [1399] "JPN50" "JPN51" "JPN52"
#> [1402] "JPN53" "JPN54" "JPN55"
#> [1405] "JPN56" "KOR1" "KOR2"
#> [1408] "KOR3" "KOR4" "KOR5"
#> [1411] "KOR6" "KOR7" "KOR8"
#> [1414] "KOR9" "KOR10" "KOR11"
#> [1417] "KOR12" "KOR13" "KOR14"
#> [1420] "KOR15" "KOR16" "KOR17"
#> [1423] "KOR18" "KOR19" "KOR20"
#> [1426] "KOR21" "KOR22" "KOR23"
#> [1429] "KOR24" "KOR25" "KOR26"
#> [1432] "KOR27" "KOR28" "KOR29"
#> [1435] "KOR30" "KOR31" "KOR32"
#> [1438] "KOR33" "KOR34" "KOR35"
#> [1441] "KOR36" "KOR37" "KOR38"
#> [1444] "KOR39" "KOR40" "KOR41"
#> [1447] "KOR42" "KOR43" "KOR44"
#> [1450] "KOR45" "KOR46" "KOR47"
#> [1453] "KOR48" "KOR49" "KOR50"
#> [1456] "KOR51" "KOR52" "KOR53"
#> [1459] "KOR54" "KOR55" "KOR56"
#> [1462] "LTU1" "LTU2" "LTU3"
#> [1465] "LTU4" "LTU5" "LTU6"
#> [1468] "LTU7" "LTU8" "LTU9"
#> [1471] "LTU10" "LTU11" "LTU12"
#> [1474] "LTU13" "LTU14" "LTU15"
#> [1477] "LTU16" "LTU17" "LTU18"
#> [1480] "LTU19" "LTU20" "LTU21"
#> [1483] "LTU22" "LTU23" "LTU24"
#> [1486] "LTU25" "LTU26" "LTU27"
#> [1489] "LTU28" "LTU29" "LTU30"
#> [1492] "LTU31" "LTU32" "LTU33"
#> [1495] "LTU34" "LTU35" "LTU36"
#> [1498] "LTU37" "LTU38" "LTU39"
#> [1501] "LTU40" "LTU41" "LTU42"
#> [1504] "LTU43" "LTU44" "LTU45"
#> [1507] "LTU46" "LTU47" "LTU48"
#> [1510] "LTU49" "LTU50" "LTU51"
#> [1513] "LTU52" "LTU53" "LTU54"
#> [1516] "LTU55" "LTU56" "LUX1"
#> [1519] "LUX2" "LUX3" "LUX4"
#> [1522] "LUX5" "LUX6" "LUX7"
#> [1525] "LUX8" "LUX9" "LUX10"
#> [1528] "LUX11" "LUX12" "LUX13"
#> [1531] "LUX14" "LUX15" "LUX16"
#> [1534] "LUX17" "LUX18" "LUX19"
#> [1537] "LUX20" "LUX21" "LUX22"
#> [1540] "LUX23" "LUX24" "LUX25"
#> [1543] "LUX26" "LUX27" "LUX28"
#> [1546] "LUX29" "LUX30" "LUX31"
#> [1549] "LUX32" "LUX33" "LUX34"
#> [1552] "LUX35" "LUX36" "LUX37"
#> [1555] "LUX38" "LUX39" "LUX40"
#> [1558] "LUX41" "LUX42" "LUX43"
#> [1561] "LUX44" "LUX45" "LUX46"
#> [1564] "LUX47" "LUX48" "LUX49"
#> [1567] "LUX50" "LUX51" "LUX52"
#> [1570] "LUX53" "LUX54" "LUX55"
#> [1573] "LUX56" "LVA1" "LVA2"
#> [1576] "LVA3" "LVA4" "LVA5"
#> [1579] "LVA6" "LVA7" "LVA8"
#> [1582] "LVA9" "LVA10" "LVA11"
#> [1585] "LVA12" "LVA13" "LVA14"
#> [1588] "LVA15" "LVA16" "LVA17"
#> [1591] "LVA18" "LVA19" "LVA20"
#> [1594] "LVA21" "LVA22" "LVA23"
#> [1597] "LVA24" "LVA25" "LVA26"
#> [1600] "LVA27" "LVA28" "LVA29"
#> [1603] "LVA30" "LVA31" "LVA32"
#> [1606] "LVA33" "LVA34" "LVA35"
#> [1609] "LVA36" "LVA37" "LVA38"
#> [1612] "LVA39" "LVA40" "LVA41"
#> [1615] "LVA42" "LVA43" "LVA44"
#> [1618] "LVA45" "LVA46" "LVA47"
#> [1621] "LVA48" "LVA49" "LVA50"
#> [1624] "LVA51" "LVA52" "LVA53"
#> [1627] "LVA54" "LVA55" "LVA56"
#> [1630] "MEX1" "MEX2" "MEX3"
#> [1633] "MEX4" "MEX5" "MEX6"
#> [1636] "MEX7" "MEX8" "MEX9"
#> [1639] "MEX10" "MEX11" "MEX12"
#> [1642] "MEX13" "MEX14" "MEX15"
#> [1645] "MEX16" "MEX17" "MEX18"
#> [1648] "MEX19" "MEX20" "MEX21"
#> [1651] "MEX22" "MEX23" "MEX24"
#> [1654] "MEX25" "MEX26" "MEX27"
#> [1657] "MEX28" "MEX29" "MEX30"
#> [1660] "MEX31" "MEX32" "MEX33"
#> [1663] "MEX34" "MEX35" "MEX36"
#> [1666] "MEX37" "MEX38" "MEX39"
#> [1669] "MEX40" "MEX41" "MEX42"
#> [1672] "MEX43" "MEX44" "MEX45"
#> [1675] "MEX46" "MEX47" "MEX48"
#> [1678] "MEX49" "MEX50" "MEX51"
#> [1681] "MEX52" "MEX53" "MEX54"
#> [1684] "MEX55" "MEX56" "MLT1"
#> [1687] "MLT2" "MLT3" "MLT4"
#> [1690] "MLT5" "MLT6" "MLT7"
#> [1693] "MLT8" "MLT9" "MLT10"
#> [1696] "MLT11" "MLT12" "MLT13"
#> [1699] "MLT14" "MLT15" "MLT16"
#> [1702] "MLT17" "MLT18" "MLT19"
#> [1705] "MLT20" "MLT21" "MLT22"
#> [1708] "MLT23" "MLT24" "MLT25"
#> [1711] "MLT26" "MLT27" "MLT28"
#> [1714] "MLT29" "MLT30" "MLT31"
#> [1717] "MLT32" "MLT33" "MLT34"
#> [1720] "MLT35" "MLT36" "MLT37"
#> [1723] "MLT38" "MLT39" "MLT40"
#> [1726] "MLT41" "MLT42" "MLT43"
#> [1729] "MLT44" "MLT45" "MLT46"
#> [1732] "MLT47" "MLT48" "MLT49"
#> [1735] "MLT50" "MLT51" "MLT52"
#> [1738] "MLT53" "MLT54" "MLT55"
#> [1741] "MLT56" "NLD1" "NLD2"
#> [1744] "NLD3" "NLD4" "NLD5"
#> [1747] "NLD6" "NLD7" "NLD8"
#> [1750] "NLD9" "NLD10" "NLD11"
#> [1753] "NLD12" "NLD13" "NLD14"
#> [1756] "NLD15" "NLD16" "NLD17"
#> [1759] "NLD18" "NLD19" "NLD20"
#> [1762] "NLD21" "NLD22" "NLD23"
#> [1765] "NLD24" "NLD25" "NLD26"
#> [1768] "NLD27" "NLD28" "NLD29"
#> [1771] "NLD30" "NLD31" "NLD32"
#> [1774] "NLD33" "NLD34" "NLD35"
#> [1777] "NLD36" "NLD37" "NLD38"
#> [1780] "NLD39" "NLD40" "NLD41"
#> [1783] "NLD42" "NLD43" "NLD44"
#> [1786] "NLD45" "NLD46" "NLD47"
#> [1789] "NLD48" "NLD49" "NLD50"
#> [1792] "NLD51" "NLD52" "NLD53"
#> [1795] "NLD54" "NLD55" "NLD56"
#> [1798] "NOR1" "NOR2" "NOR3"
#> [1801] "NOR4" "NOR5" "NOR6"
#> [1804] "NOR7" "NOR8" "NOR9"
#> [1807] "NOR10" "NOR11" "NOR12"
#> [1810] "NOR13" "NOR14" "NOR15"
#> [1813] "NOR16" "NOR17" "NOR18"
#> [1816] "NOR19" "NOR20" "NOR21"
#> [1819] "NOR22" "NOR23" "NOR24"
#> [1822] "NOR25" "NOR26" "NOR27"
#> [1825] "NOR28" "NOR29" "NOR30"
#> [1828] "NOR31" "NOR32" "NOR33"
#> [1831] "NOR34" "NOR35" "NOR36"
#> [1834] "NOR37" "NOR38" "NOR39"
#> [1837] "NOR40" "NOR41" "NOR42"
#> [1840] "NOR43" "NOR44" "NOR45"
#> [1843] "NOR46" "NOR47" "NOR48"
#> [1846] "NOR49" "NOR50" "NOR51"
#> [1849] "NOR52" "NOR53" "NOR54"
#> [1852] "NOR55" "NOR56" "POL1"
#> [1855] "POL2" "POL3" "POL4"
#> [1858] "POL5" "POL6" "POL7"
#> [1861] "POL8" "POL9" "POL10"
#> [1864] "POL11" "POL12" "POL13"
#> [1867] "POL14" "POL15" "POL16"
#> [1870] "POL17" "POL18" "POL19"
#> [1873] "POL20" "POL21" "POL22"
#> [1876] "POL23" "POL24" "POL25"
#> [1879] "POL26" "POL27" "POL28"
#> [1882] "POL29" "POL30" "POL31"
#> [1885] "POL32" "POL33" "POL34"
#> [1888] "POL35" "POL36" "POL37"
#> [1891] "POL38" "POL39" "POL40"
#> [1894] "POL41" "POL42" "POL43"
#> [1897] "POL44" "POL45" "POL46"
#> [1900] "POL47" "POL48" "POL49"
#> [1903] "POL50" "POL51" "POL52"
#> [1906] "POL53" "POL54" "POL55"
#> [1909] "POL56" "PRT1" "PRT2"
#> [1912] "PRT3" "PRT4" "PRT5"
#> [1915] "PRT6" "PRT7" "PRT8"
#> [1918] "PRT9" "PRT10" "PRT11"
#> [1921] "PRT12" "PRT13" "PRT14"
#> [1924] "PRT15" "PRT16" "PRT17"
#> [1927] "PRT18" "PRT19" "PRT20"
#> [1930] "PRT21" "PRT22" "PRT23"
#> [1933] "PRT24" "PRT25" "PRT26"
#> [1936] "PRT27" "PRT28" "PRT29"
#> [1939] "PRT30" "PRT31" "PRT32"
#> [1942] "PRT33" "PRT34" "PRT35"
#> [1945] "PRT36" "PRT37" "PRT38"
#> [1948] "PRT39" "PRT40" "PRT41"
#> [1951] "PRT42" "PRT43" "PRT44"
#> [1954] "PRT45" "PRT46" "PRT47"
#> [1957] "PRT48" "PRT49" "PRT50"
#> [1960] "PRT51" "PRT52" "PRT53"
#> [1963] "PRT54" "PRT55" "PRT56"
#> [1966] "ROU1" "ROU2" "ROU3"
#> [1969] "ROU4" "ROU5" "ROU6"
#> [1972] "ROU7" "ROU8" "ROU9"
#> [1975] "ROU10" "ROU11" "ROU12"
#> [1978] "ROU13" "ROU14" "ROU15"
#> [1981] "ROU16" "ROU17" "ROU18"
#> [1984] "ROU19" "ROU20" "ROU21"
#> [1987] "ROU22" "ROU23" "ROU24"
#> [1990] "ROU25" "ROU26" "ROU27"
#> [1993] "ROU28" "ROU29" "ROU30"
#> [1996] "ROU31" "ROU32" "ROU33"
#> [1999] "ROU34" "ROU35" "ROU36"
#> [2002] "ROU37" "ROU38" "ROU39"
#> [2005] "ROU40" "ROU41" "ROU42"
#> [2008] "ROU43" "ROU44" "ROU45"
#> [2011] "ROU46" "ROU47" "ROU48"
#> [2014] "ROU49" "ROU50" "ROU51"
#> [2017] "ROU52" "ROU53" "ROU54"
#> [2020] "ROU55" "ROU56" "RUS1"
#> [2023] "RUS2" "RUS3" "RUS4"
#> [2026] "RUS5" "RUS6" "RUS7"
#> [2029] "RUS8" "RUS9" "RUS10"
#> [2032] "RUS11" "RUS12" "RUS13"
#> [2035] "RUS14" "RUS15" "RUS16"
#> [2038] "RUS17" "RUS18" "RUS19"
#> [2041] "RUS20" "RUS21" "RUS22"
#> [2044] "RUS23" "RUS24" "RUS25"
#> [2047] "RUS26" "RUS27" "RUS28"
#> [2050] "RUS29" "RUS30" "RUS31"
#> [2053] "RUS32" "RUS33" "RUS34"
#> [2056] "RUS35" "RUS36" "RUS37"
#> [2059] "RUS38" "RUS39" "RUS40"
#> [2062] "RUS41" "RUS42" "RUS43"
#> [2065] "RUS44" "RUS45" "RUS46"
#> [2068] "RUS47" "RUS48" "RUS49"
#> [2071] "RUS50" "RUS51" "RUS52"
#> [2074] "RUS53" "RUS54" "RUS55"
#> [2077] "RUS56" "SVK1" "SVK2"
#> [2080] "SVK3" "SVK4" "SVK5"
#> [2083] "SVK6" "SVK7" "SVK8"
#> [2086] "SVK9" "SVK10" "SVK11"
#> [2089] "SVK12" "SVK13" "SVK14"
#> [2092] "SVK15" "SVK16" "SVK17"
#> [2095] "SVK18" "SVK19" "SVK20"
#> [2098] "SVK21" "SVK22" "SVK23"
#> [2101] "SVK24" "SVK25" "SVK26"
#> [2104] "SVK27" "SVK28" "SVK29"
#> [2107] "SVK30" "SVK31" "SVK32"
#> [2110] "SVK33" "SVK34" "SVK35"
#> [2113] "SVK36" "SVK37" "SVK38"
#> [2116] "SVK39" "SVK40" "SVK41"
#> [2119] "SVK42" "SVK43" "SVK44"
#> [2122] "SVK45" "SVK46" "SVK47"
#> [2125] "SVK48" "SVK49" "SVK50"
#> [2128] "SVK51" "SVK52" "SVK53"
#> [2131] "SVK54" "SVK55" "SVK56"
#> [2134] "SVN1" "SVN2" "SVN3"
#> [2137] "SVN4" "SVN5" "SVN6"
#> [2140] "SVN7" "SVN8" "SVN9"
#> [2143] "SVN10" "SVN11" "SVN12"
#> [2146] "SVN13" "SVN14" "SVN15"
#> [2149] "SVN16" "SVN17" "SVN18"
#> [2152] "SVN19" "SVN20" "SVN21"
#> [2155] "SVN22" "SVN23" "SVN24"
#> [2158] "SVN25" "SVN26" "SVN27"
#> [2161] "SVN28" "SVN29" "SVN30"
#> [2164] "SVN31" "SVN32" "SVN33"
#> [2167] "SVN34" "SVN35" "SVN36"
#> [2170] "SVN37" "SVN38" "SVN39"
#> [2173] "SVN40" "SVN41" "SVN42"
#> [2176] "SVN43" "SVN44" "SVN45"
#> [2179] "SVN46" "SVN47" "SVN48"
#> [2182] "SVN49" "SVN50" "SVN51"
#> [2185] "SVN52" "SVN53" "SVN54"
#> [2188] "SVN55" "SVN56" "SWE1"
#> [2191] "SWE2" "SWE3" "SWE4"
#> [2194] "SWE5" "SWE6" "SWE7"
#> [2197] "SWE8" "SWE9" "SWE10"
#> [2200] "SWE11" "SWE12" "SWE13"
#> [2203] "SWE14" "SWE15" "SWE16"
#> [2206] "SWE17" "SWE18" "SWE19"
#> [2209] "SWE20" "SWE21" "SWE22"
#> [2212] "SWE23" "SWE24" "SWE25"
#> [2215] "SWE26" "SWE27" "SWE28"
#> [2218] "SWE29" "SWE30" "SWE31"
#> [2221] "SWE32" "SWE33" "SWE34"
#> [2224] "SWE35" "SWE36" "SWE37"
#> [2227] "SWE38" "SWE39" "SWE40"
#> [2230] "SWE41" "SWE42" "SWE43"
#> [2233] "SWE44" "SWE45" "SWE46"
#> [2236] "SWE47" "SWE48" "SWE49"
#> [2239] "SWE50" "SWE51" "SWE52"
#> [2242] "SWE53" "SWE54" "SWE55"
#> [2245] "SWE56" "TUR1" "TUR2"
#> [2248] "TUR3" "TUR4" "TUR5"
#> [2251] "TUR6" "TUR7" "TUR8"
#> [2254] "TUR9" "TUR10" "TUR11"
#> [2257] "TUR12" "TUR13" "TUR14"
#> [2260] "TUR15" "TUR16" "TUR17"
#> [2263] "TUR18" "TUR19" "TUR20"
#> [2266] "TUR21" "TUR22" "TUR23"
#> [2269] "TUR24" "TUR25" "TUR26"
#> [2272] "TUR27" "TUR28" "TUR29"
#> [2275] "TUR30" "TUR31" "TUR32"
#> [2278] "TUR33" "TUR34" "TUR35"
#> [2281] "TUR36" "TUR37" "TUR38"
#> [2284] "TUR39" "TUR40" "TUR41"
#> [2287] "TUR42" "TUR43" "TUR44"
#> [2290] "TUR45" "TUR46" "TUR47"
#> [2293] "TUR48" "TUR49" "TUR50"
#> [2296] "TUR51" "TUR52" "TUR53"
#> [2299] "TUR54" "TUR55" "TUR56"
#> [2302] "TWN1" "TWN2" "TWN3"
#> [2305] "TWN4" "TWN5" "TWN6"
#> [2308] "TWN7" "TWN8" "TWN9"
#> [2311] "TWN10" "TWN11" "TWN12"
#> [2314] "TWN13" "TWN14" "TWN15"
#> [2317] "TWN16" "TWN17" "TWN18"
#> [2320] "TWN19" "TWN20" "TWN21"
#> [2323] "TWN22" "TWN23" "TWN24"
#> [2326] "TWN25" "TWN26" "TWN27"
#> [2329] "TWN28" "TWN29" "TWN30"
#> [2332] "TWN31" "TWN32" "TWN33"
#> [2335] "TWN34" "TWN35" "TWN36"
#> [2338] "TWN37" "TWN38" "TWN39"
#> [2341] "TWN40" "TWN41" "TWN42"
#> [2344] "TWN43" "TWN44" "TWN45"
#> [2347] "TWN46" "TWN47" "TWN48"
#> [2350] "TWN49" "TWN50" "TWN51"
#> [2353] "TWN52" "TWN53" "TWN54"
#> [2356] "TWN55" "TWN56" "USA1"
#> [2359] "USA2" "USA3" "USA4"
#> [2362] "USA5" "USA6" "USA7"
#> [2365] "USA8" "USA9" "USA10"
#> [2368] "USA11" "USA12" "USA13"
#> [2371] "USA14" "USA15" "USA16"
#> [2374] "USA17" "USA18" "USA19"
#> [2377] "USA20" "USA21" "USA22"
#> [2380] "USA23" "USA24" "USA25"
#> [2383] "USA26" "USA27" "USA28"
#> [2386] "USA29" "USA30" "USA31"
#> [2389] "USA32" "USA33" "USA34"
#> [2392] "USA35" "USA36" "USA37"
#> [2395] "USA38" "USA39" "USA40"
#> [2398] "USA41" "USA42" "USA43"
#> [2401] "USA44" "USA45" "USA46"
#> [2404] "USA47" "USA48" "USA49"
#> [2407] "USA50" "USA51" "USA52"
#> [2410] "USA53" "USA54" "USA55"
#> [2413] "USA56" "ROW1" "ROW2"
#> [2416] "ROW3" "ROW4" "ROW5"
#> [2419] "ROW6" "ROW7" "ROW8"
#> [2422] "ROW9" "ROW10" "ROW11"
#> [2425] "ROW12" "ROW13" "ROW14"
#> [2428] "ROW15" "ROW16" "ROW17"
#> [2431] "ROW18" "ROW19" "ROW20"
#> [2434] "ROW21" "ROW22" "ROW23"
#> [2437] "ROW24" "ROW25" "ROW26"
#> [2440] "ROW27" "ROW28" "ROW29"
#> [2443] "ROW30" "ROW31" "ROW32"
#> [2446] "ROW33" "ROW34" "ROW35"
#> [2449] "ROW36" "ROW37" "ROW38"
#> [2452] "ROW39" "ROW40" "ROW41"
#> [2455] "ROW42" "ROW43" "ROW44"
#> [2458] "ROW45" "ROW46" "ROW47"
#> [2461] "ROW48" "ROW49" "ROW50"
#> [2464] "ROW51" "ROW52" "ROW53"
#> [2467] "ROW54" "ROW55" "ROW56"
#> [2470] "AUS57" "AUS58" "AUS59"
#> [2473] "AUS60" "AUS61" "AUT57"
#> [2476] "AUT58" "AUT59" "AUT60"
#> [2479] "AUT61" "BEL57" "BEL58"
#> [2482] "BEL59" "BEL60" "BEL61"
#> [2485] "BGR57" "BGR58" "BGR59"
#> [2488] "BGR60" "BGR61" "BRA57"
#> [2491] "BRA58" "BRA59" "BRA60"
#> [2494] "BRA61" "CAN57" "CAN58"
#> [2497] "CAN59" "CAN60" "CAN61"
#> [2500] "CHE57" "CHE58" "CHE59"
#> [2503] "CHE60" "CHE61" "CHN57"
#> [2506] "CHN58" "CHN59" "CHN60"
#> [2509] "CHN61" "CYP57" "CYP58"
#> [2512] "CYP59" "CYP60" "CYP61"
#> [2515] "CZE57" "CZE58" "CZE59"
#> [2518] "CZE60" "CZE61" "DEU57"
#> [2521] "DEU58" "DEU59" "DEU60"
#> [2524] "DEU61" "DNK57" "DNK58"
#> [2527] "DNK59" "DNK60" "DNK61"
#> [2530] "ESP57" "ESP58" "ESP59"
#> [2533] "ESP60" "ESP61" "EST57"
#> [2536] "EST58" "EST59" "EST60"
#> [2539] "EST61" "FIN57" "FIN58"
#> [2542] "FIN59" "FIN60" "FIN61"
#> [2545] "FRA57" "FRA58" "FRA59"
#> [2548] "FRA60" "FRA61" "GBR57"
#> [2551] "GBR58" "GBR59" "GBR60"
#> [2554] "GBR61" "GRC57" "GRC58"
#> [2557] "GRC59" "GRC60" "GRC61"
#> [2560] "HRV57" "HRV58" "HRV59"
#> [2563] "HRV60" "HRV61" "HUN57"
#> [2566] "HUN58" "HUN59" "HUN60"
#> [2569] "HUN61" "IDN57" "IDN58"
#> [2572] "IDN59" "IDN60" "IDN61"
#> [2575] "IND57" "IND58" "IND59"
#> [2578] "IND60" "IND61" "IRL57"
#> [2581] "IRL58" "IRL59" "IRL60"
#> [2584] "IRL61" "ITA57" "ITA58"
#> [2587] "ITA59" "ITA60" "ITA61"
#> [2590] "JPN57" "JPN58" "JPN59"
#> [2593] "JPN60" "JPN61" "KOR57"
#> [2596] "KOR58" "KOR59" "KOR60"
#> [2599] "KOR61" "LTU57" "LTU58"
#> [2602] "LTU59" "LTU60" "LTU61"
#> [2605] "LUX57" "LUX58" "LUX59"
#> [2608] "LUX60" "LUX61" "LVA57"
#> [2611] "LVA58" "LVA59" "LVA60"
#> [2614] "LVA61" "MEX57" "MEX58"
#> [2617] "MEX59" "MEX60" "MEX61"
#> [2620] "MLT57" "MLT58" "MLT59"
#> [2623] "MLT60" "MLT61" "NLD57"
#> [2626] "NLD58" "NLD59" "NLD60"
#> [2629] "NLD61" "NOR57" "NOR58"
#> [2632] "NOR59" "NOR60" "NOR61"
#> [2635] "POL57" "POL58" "POL59"
#> [2638] "POL60" "POL61" "PRT57"
#> [2641] "PRT58" "PRT59" "PRT60"
#> [2644] "PRT61" "ROU57" "ROU58"
#> [2647] "ROU59" "ROU60" "ROU61"
#> [2650] "RUS57" "RUS58" "RUS59"
#> [2653] "RUS60" "RUS61" "SVK57"
#> [2656] "SVK58" "SVK59" "SVK60"
#> [2659] "SVK61" "SVN57" "SVN58"
#> [2662] "SVN59" "SVN60" "SVN61"
#> [2665] "SWE57" "SWE58" "SWE59"
#> [2668] "SWE60" "SWE61" "TUR57"
#> [2671] "TUR58" "TUR59" "TUR60"
#> [2674] "TUR61" "TWN57" "TWN58"
#> [2677] "TWN59" "TWN60" "TWN61"
#> [2680] "USA57" "USA58" "USA59"
#> [2683] "USA60" "USA61" "ROW57"
#> [2686] "ROW58" "ROW59" "ROW60"
#> [2689] "ROW61" "TOT"Expanding the Country column, we find that it has 45
unique values, corresponding to the 44 countries in the WIOD 2014
release plus a “Total” row. The IndustryDescription column
has 56 unique values, corresponding to the 56 sectors in the WIOD 2014
release, plus a total row for the intermediate transactions matrix and
the Value Added matrix.
# unique countries
unique(wiot$Country)
#> [1] "AUS" "AUT" "BEL" "BGR" "BRA" "CAN" "CHE" "CHN" "CYP" "CZE" "DEU" "DNK"
#> [13] "ESP" "EST" "FIN" "FRA" "GBR" "GRC" "HRV" "HUN" "IDN" "IND" "IRL" "ITA"
#> [25] "JPN" "KOR" "LTU" "LUX" "LVA" "MEX" "MLT" "NLD" "NOR" "POL" "PRT" "ROU"
#> [37] "RUS" "SVK" "SVN" "SWE" "TUR" "TWN" "USA" "ROW" "TOT"
# unique sectors
unique(wiot$IndustryDescription)
#> [1] "Crop and animal production, hunting and related service activities"
#> [2] "Forestry and logging"
#> [3] "Fishing and aquaculture"
#> [4] "Mining and quarrying"
#> [5] "Manufacture of food products, beverages and tobacco products"
#> [6] "Manufacture of textiles, wearing apparel and leather products"
#> [7] "Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials"
#> [8] "Manufacture of paper and paper products"
#> [9] "Printing and reproduction of recorded media"
#> [10] "Manufacture of coke and refined petroleum products "
#> [11] "Manufacture of chemicals and chemical products "
#> [12] "Manufacture of basic pharmaceutical products and pharmaceutical preparations"
#> [13] "Manufacture of rubber and plastic products"
#> [14] "Manufacture of other non-metallic mineral products"
#> [15] "Manufacture of basic metals"
#> [16] "Manufacture of fabricated metal products, except machinery and equipment"
#> [17] "Manufacture of computer, electronic and optical products"
#> [18] "Manufacture of electrical equipment"
#> [19] "Manufacture of machinery and equipment n.e.c."
#> [20] "Manufacture of motor vehicles, trailers and semi-trailers"
#> [21] "Manufacture of other transport equipment"
#> [22] "Manufacture of furniture; other manufacturing"
#> [23] "Repair and installation of machinery and equipment"
#> [24] "Electricity, gas, steam and air conditioning supply"
#> [25] "Water collection, treatment and supply"
#> [26] "Sewerage; waste collection, treatment and disposal activities; materials recovery; remediation activities and other waste management services "
#> [27] "Construction"
#> [28] "Wholesale and retail trade and repair of motor vehicles and motorcycles"
#> [29] "Wholesale trade, except of motor vehicles and motorcycles"
#> [30] "Retail trade, except of motor vehicles and motorcycles"
#> [31] "Land transport and transport via pipelines"
#> [32] "Water transport"
#> [33] "Air transport"
#> [34] "Warehousing and support activities for transportation"
#> [35] "Postal and courier activities"
#> [36] "Accommodation and food service activities"
#> [37] "Publishing activities"
#> [38] "Motion picture, video and television programme production, sound recording and music publishing activities; programming and broadcasting activities"
#> [39] "Telecommunications"
#> [40] "Computer programming, consultancy and related activities; information service activities"
#> [41] "Financial service activities, except insurance and pension funding"
#> [42] "Insurance, reinsurance and pension funding, except compulsory social security"
#> [43] "Activities auxiliary to financial services and insurance activities"
#> [44] "Real estate activities"
#> [45] "Legal and accounting activities; activities of head offices; management consultancy activities"
#> [46] "Architectural and engineering activities; technical testing and analysis"
#> [47] "Scientific research and development"
#> [48] "Advertising and market research"
#> [49] "Other professional, scientific and technical activities; veterinary activities"
#> [50] "Administrative and support service activities"
#> [51] "Public administration and defence; compulsory social security"
#> [52] "Education"
#> [53] "Human health and social work activities"
#> [54] "Other service activities"
#> [55] "Activities of households as employers; undifferentiated goods- and services-producing activities of households for own use"
#> [56] "Activities of extraterritorial organizations and bodies"
#> [57] "Total intermediate consumption"
#> [58] "taxes less subsidies on products"
#> [59] "Cif/ fob adjustments on exports"
#> [60] "Direct purchases abroad by residents"
#> [61] "Purchases on the domestic territory by non-residents "
#> [62] "Value added at basic prices"
#> [63] "International Transport Margins"
#> [64] "Output at basic prices"
# structure
n_countries <- 44
n_sectors <- 56
n_interm <- n_countries * n_sectors
# extract intermediate transactions matrix
Z <- as.matrix(wiot[1:n_interm, 6:(5 + n_interm)])
# total production vector
x <- as.numeric(wiot[1:n_interm, ncol(wiot)])
# fix zeros to avoid division by zero (NaNs in Leontief Inverse)
# since Z columns are 0 where x is 0, setting x=1 results in A=0/1=0, which is correct
x[x == 0] <- 1
x <- matrix(x, nrow = 1)
# extract countries and sectors
countries_long <- as.character(wiot$Country[1:n_interm])
sectors_long <- as.character(wiot$IndustryCode[1:n_interm])
# get unique values respecting order
countries <- unique(countries_long)
sectors <- unique(sectors_long)
# validating dimensions
if (length(countries) != n_countries) stop("Number of extracted countries does not match expected 44.")
if (length(sectors) != n_sectors) stop("Number of extracted sectors does not match expected 56.")
# create miom object
wiod_miom <- fio::miom$new(
id = "wiod_2014",
intermediate_transactions = Z,
total_production = x,
countries = countries,
sectors = sectors
)
print(wiod_miom)
#> <miom>
#> Inherits from: <iom>
#> Public:
#> add: function (matrix_name, matrix)
#> allocation_coefficients_matrix: NULL
#> bilateral_trade: NULL
#> clone: function (deep = FALSE)
#> close_model: function (sectors)
#> compute_allocation_coeff: function ()
#> compute_field_influence: function (epsilon)
#> compute_ghosh_inverse: function ()
#> compute_hypothetical_extraction: function (matrix = "ghosh")
#> compute_key_sectors: function (matrix = "leontief")
#> compute_leontief_inverse: function ()
#> compute_multiplier_employment: function ()
#> compute_multiplier_output: function ()
#> compute_multiplier_taxes: function ()
#> compute_multiplier_wages: function ()
#> compute_multiregional_multipliers: function ()
#> compute_tech_coeff: function ()
#> countries: AUS AUT BEL BGR BRA CAN CHE CHN CYP CZE DEU DNK ESP EST ...
#> domestic_intermediate_transactions: list
#> exports: NULL
#> extract_country: function (country)
#> field_influence: NULL
#> final_demand_matrix: NULL
#> final_demand_others: NULL
#> get_bilateral_trade: function (origin_country, destination_country)
#> get_country_summary: function ()
#> get_net_spillover_matrix: function ()
#> get_regional_interdependence: function ()
#> get_spillover_matrix: function ()
#> ghosh_inverse_matrix: NULL
#> government_consumption: NULL
#> household_consumption: NULL
#> hypothetical_extraction: NULL
#> id: wiod_2014
#> imports: NULL
#> initialize: function (id, intermediate_transactions, total_production, countries,
#> intermediate_transactions: 12924.1796913047 83.0296371764357 19.1477309183893 115.9 ...
#> international_intermediate_transactions: list
#> key_sectors: NULL
#> leontief_inverse_matrix: NULL
#> multiplier_employment: NULL
#> multiplier_output: NULL
#> multiplier_taxes: NULL
#> multiplier_wages: NULL
#> multiregional_multipliers: NULL
#> n_countries: 44
#> n_sectors: 56
#> occupation: NULL
#> operating_income: NULL
#> remove: function (matrix_name)
#> sectors: A01 A02 A03 B C10-C12 C13-C15 C16 C17 C18 C19 C20 C21 C2 ...
#> set_max_threads: function (max_threads)
#> taxes: NULL
#> technical_coefficients_matrix: NULL
#> total_production: 70292.0344922962 2585.37968548282 3175.04439635749 17198 ...
#> update_final_demand_matrix: function ()
#> update_value_added_matrix: function ()
#> value_added_matrix: NULL
#> value_added_others: NULL
#> wages: NULL
#> Private:
#> decompose_transactions: function ()
#> ensure_labels: function (intermediate_transactions, total_production)
#> iom_elements: function ()Analysis
Technical Coefficients
Compute the technical coefficients matrix ().
wiod_miom$compute_tech_coeff()
# inspect a small corner of the matrix
wiod_miom$technical_coefficients_matrix[1:5, 1:5]
#> AUS1 AUS2 AUS3 AUS4 AUS5
#> 1 0.1838640720 4.349621e-02 0.0719613440 3.133357e-03 3.010553e-01
#> 2 0.0011812098 7.793824e-02 0.0000602552 9.995446e-05 3.169105e-05
#> 3 0.0002724026 4.173170e-07 0.0060177743 2.458415e-05 3.785148e-03
#> 4 0.0016492601 2.688347e-04 0.0018659508 2.611876e-02 3.971178e-03
#> 5 0.0226318757 3.403999e-04 0.0133984772 1.180404e-03 1.299549e-01Leontief Inverse
Compute the Leontief inverse matrix ().
wiod_miom$compute_leontief_inverse()
# inspect a small corner of the matrix
wiod_miom$leontief_inverse_matrix[1:5, 1:5]
#> AUS1 AUS2 AUS3 AUS4 AUS5
#> 1 1.2407423187 5.973791e-02 0.097460580 0.0078478729 0.432622975
#> 2 0.0021694662 1.084703e+00 0.000448754 0.0005569042 0.001053428
#> 3 0.0006396819 8.298081e-05 1.006266345 0.0001902368 0.004765386
#> 4 0.0168505348 5.076858e-02 0.025603805 1.0412554156 0.016683166
#> 5 0.0360356137 3.166686e-03 0.020243954 0.0057830393 1.165478279Multipliers
Compute multi-regional multipliers.
wiod_miom$compute_multiregional_multipliers()
multipliers <- wiod_miom$multiregional_multipliers
head(multipliers)
#> destination_country destination_sector destination_label
#> 1 AUS A01 AUS_A01
#> 2 AUS A02 AUS_A02
#> 3 AUS A03 AUS_A03
#> 4 AUS B AUS_B
#> 5 AUS C10-C12 AUS_C10-C12
#> 6 AUS C13-C15 AUS_C13-C15
#> intra_regional_multiplier spillover_multiplier total_multiplier
#> 1 1.943062 0.2969466 2.240008
#> 2 1.452797 0.4038390 1.856636
#> 3 1.535429 0.3813972 1.916826
#> 4 1.653958 0.3058091 1.959768
#> 5 2.288854 0.3186925 2.607547
#> 6 1.726600 0.5277454 2.254345
#> multiplier_to_AUS multiplier_to_AUT multiplier_to_BEL multiplier_to_BGR
#> 1 1.943062 0.0009241692 0.002758861 0.0001238116
#> 2 1.452797 0.0004372143 0.001138277 0.0001227772
#> 3 1.535429 0.0012504465 0.001887738 0.0001406286
#> 4 1.653958 0.0010232152 0.002402344 0.0001165439
#> 5 2.288854 0.0013064230 0.002666766 0.0001519445
#> 6 1.726600 0.0013017656 0.002629641 0.0002094356
#> multiplier_to_BRA multiplier_to_CAN multiplier_to_CHE multiplier_to_CHN
#> 1 0.002276453 0.002771944 0.002982965 0.04971349
#> 2 0.002676284 0.002427337 0.001218991 0.02613814
#> 3 0.002319685 0.003668137 0.002666579 0.06304876
#> 4 0.001817263 0.002696675 0.002281610 0.05344938
#> 5 0.003045518 0.003435303 0.002693924 0.05536673
#> 6 0.004632305 0.003806831 0.002422397 0.18135595
#> multiplier_to_CYP multiplier_to_CZE multiplier_to_DEU multiplier_to_DNK
#> 1 2.775959e-05 0.0004224850 0.009478387 0.0009231147
#> 2 3.198655e-05 0.0002227801 0.004368811 0.0005384190
#> 3 3.109951e-05 0.0005965222 0.012158630 0.0010177472
#> 4 2.906959e-05 0.0004841475 0.009504670 0.0008636884
#> 5 3.022388e-05 0.0005136122 0.010170511 0.0010293537
#> 6 3.465058e-05 0.0006358798 0.011412754 0.0010622292
#> multiplier_to_ESP multiplier_to_EST multiplier_to_FIN multiplier_to_FRA
#> 1 0.002176144 8.083161e-05 0.0007182112 0.004602542
#> 2 0.001460222 4.782012e-05 0.0003500961 0.002384622
#> 3 0.002796333 6.898651e-05 0.0009674640 0.005649331
#> 4 0.002074164 9.024358e-05 0.0008258744 0.004164778
#> 5 0.002618653 8.443963e-05 0.0017046651 0.005234284
#> 6 0.003699904 8.351721e-05 0.0008525216 0.006244268
#> multiplier_to_GBR multiplier_to_GRC multiplier_to_HRV multiplier_to_HUN
#> 1 0.006945507 0.0002709420 8.485303e-05 0.0002725895
#> 2 0.003647983 0.0003542640 1.151910e-04 0.0001591836
#> 3 0.008261048 0.0003092300 1.044330e-04 0.0004127322
#> 4 0.006770037 0.0002829849 8.861917e-05 0.0003007200
#> 5 0.007406232 0.0003147103 9.637737e-05 0.0003496159
#> 6 0.011737611 0.0003638989 1.088962e-04 0.0003397050
#> multiplier_to_IDN multiplier_to_IND multiplier_to_IRL multiplier_to_ITA
#> 1 0.006373683 0.003840025 0.0010433081 0.004595336
#> 2 0.012283637 0.003665256 0.0005213547 0.001946442
#> 3 0.008055881 0.004402301 0.0007378384 0.009458692
#> 4 0.006280513 0.003310474 0.0007770404 0.006327202
#> 5 0.007585796 0.004531884 0.0011274920 0.005464752
#> 6 0.010122973 0.018431872 0.0010179443 0.011518541
#> multiplier_to_JPN multiplier_to_KOR multiplier_to_LTU multiplier_to_LUX
#> 1 0.01133541 0.01315437 9.383210e-05 0.0003387792
#> 2 0.02128126 0.03406184 8.935646e-05 0.0004072236
#> 3 0.01784399 0.01966472 9.016333e-05 0.0003754964
#> 4 0.01153816 0.01165849 9.319060e-05 0.0003575600
#> 5 0.01147847 0.01112882 1.065749e-04 0.0003734229
#> 6 0.01035669 0.01909961 1.506659e-04 0.0004563254
#> multiplier_to_LVA multiplier_to_MEX multiplier_to_MLT multiplier_to_NLD
#> 1 4.805329e-05 0.001573719 2.260833e-05 0.003611187
#> 2 4.739492e-05 0.001209909 2.042198e-05 0.002051420
#> 3 5.278813e-05 0.001909338 2.425943e-05 0.003552115
#> 4 4.874760e-05 0.001420833 2.272002e-05 0.003264924
#> 5 6.106668e-05 0.001590505 2.357628e-05 0.003997279
#> 6 7.591093e-05 0.001740457 2.873606e-05 0.003506302
#> multiplier_to_NOR multiplier_to_POL multiplier_to_PRT multiplier_to_ROU
#> 1 0.001158450 0.0007449103 0.0003033162 0.0002377749
#> 2 0.001697678 0.0004490915 0.0002491883 0.0002075623
#> 3 0.001535203 0.0010631416 0.0003507277 0.0003252080
#> 4 0.001194689 0.0008902345 0.0003214690 0.0002699144
#> 5 0.001131026 0.0008921955 0.0003729214 0.0002936487
#> 6 0.001094653 0.0010713108 0.0012683477 0.0003957417
#> multiplier_to_RUS multiplier_to_SVK multiplier_to_SVN multiplier_to_SWE
#> 1 0.004922003 0.0002892057 9.400659e-05 0.0017867783
#> 2 0.013434710 0.0001360349 6.321230e-05 0.0008649097
#> 3 0.006865531 0.0002745919 1.487642e-04 0.0023605457
#> 4 0.006046886 0.0002679674 1.175375e-04 0.0018720401
#> 5 0.004555407 0.0003063588 1.267838e-04 0.0020996779
#> 6 0.004538042 0.0003017015 1.266656e-04 0.0022650560
#> multiplier_to_TUR multiplier_to_TWN multiplier_to_USA multiplier_to_ROW
#> 1 0.0011047479 0.005965797 0.02604875 0.1207055
#> 2 0.0009797962 0.012001196 0.01339803 0.2349317
#> 3 0.0016449284 0.008984176 0.03218315 0.1521381
#> 4 0.0012925722 0.005672950 0.02340328 0.1300937
#> 5 0.0014436772 0.005543222 0.02719302 0.1290457
#> 6 0.0065184793 0.010121129 0.02608771 0.1645163
# summary of simple multipliers by country
wiod_miom$get_country_summary()
#> country multiplier_simple_mean multiplier_simple_sum multiplier_simple_sd
#> 1 AUS 2.059444 115.32887 0.5286893
#> 2 AUT 2.197637 123.06768 0.4594470
#> 3 BEL 2.337291 130.88832 0.4807025
#> 4 BGR 2.302660 128.94898 0.4971555
#> 5 BRA 1.855324 103.89814 0.5501174
#> 6 CAN 2.108827 118.09430 0.5340617
#> 7 CHE 2.089600 117.01762 0.5500224
#> 8 CHN 2.572424 144.05577 0.9026179
#> 9 CYP 2.075184 116.21033 0.5207413
#> 10 CZE 2.389257 133.79839 0.4531277
#> 11 DEU 2.094890 117.31385 0.4296699
#> 12 DNK 2.136404 119.63863 0.4413923
#> 13 ESP 2.168283 121.42386 0.5331024
#> 14 EST 2.275109 127.40609 0.5011790
#> 15 FIN 2.189053 122.58695 0.4597874
#> 16 FRA 2.190604 122.67383 0.4657943
#> 17 GBR 2.097224 117.44453 0.4245538
#> 18 GRC 1.906824 106.78213 0.4294008
#> 19 HRV 2.050698 114.83906 0.3895920
#> 20 HUN 2.195959 122.97370 0.5031122
#> 21 IDN 1.897032 106.23380 0.5749050
#> 22 IND 1.798571 100.71997 0.7130320
#> 23 IRL 1.991669 111.53346 0.4593722
#> 24 ITA 2.244525 125.69342 0.5668888
#> 25 JPN 2.099949 117.59717 0.5870561
#> 26 KOR 2.396479 134.20284 0.5895838
#> 27 LTU 1.905340 106.69906 0.3897351
#> 28 LUX 2.256960 126.38977 0.6069931
#> 29 LVA 2.277365 127.53244 0.4919037
#> 30 MEX 1.888046 105.73058 0.5134985
#> 31 MLT 2.445577 136.95233 0.5991034
#> 32 NLD 2.183777 122.29151 0.4918711
#> 33 NOR 2.036868 114.06460 0.4057917
#> 34 POL 2.191662 122.73309 0.4659280
#> 35 PRT 2.158953 120.90138 0.5136721
#> 36 ROU 2.146740 120.21745 0.4294959
#> 37 ROW 2.425472 135.82643 0.6231174
#> 38 RUS 1.697147 95.04021 0.6557177
#> 39 SVK 2.233522 125.07722 0.5206769
#> 40 SVN 2.195134 122.92752 0.4549377
#> 41 SWE 2.084764 116.74678 0.4572012
#> 42 TUR 1.931434 108.16030 0.6183051
#> 43 TWN 2.353178 131.77796 0.6831262
#> 44 USA 1.968826 110.25423 0.3831758
#> multiplier_direct_mean multiplier_direct_sum multiplier_direct_sd
#> 1 0.4634548 25.95347 0.2210916
#> 2 0.5274088 29.53489 0.1823711
#> 3 0.5705468 31.95062 0.1833494
#> 4 0.5444802 30.49089 0.1913925
#> 5 0.4085393 22.87820 0.2443644
#> 6 0.4847580 27.14645 0.2177967
#> 7 0.4759913 26.65551 0.2246538
#> 8 0.5285805 29.60051 0.2804314
#> 9 0.4651514 26.04848 0.2166702
#> 10 0.5683148 31.82563 0.1704444
#> 11 0.5051221 28.28684 0.1710627
#> 12 0.5179266 29.00389 0.1845329
#> 13 0.5098436 28.55124 0.1970098
#> 14 0.5431742 30.41776 0.1869269
#> 15 0.5215762 29.20826 0.1821999
#> 16 0.5283461 29.58738 0.1875087
#> 17 0.4995706 27.97596 0.1657784
#> 18 0.4718180 26.42181 0.1980097
#> 19 0.4906760 27.47786 0.1614403
#> 20 0.5200324 29.12182 0.1853569
#> 21 0.4112426 23.02959 0.2460278
#> 22 0.3577596 20.03454 0.3075679
#> 23 0.4726003 26.46562 0.1960911
#> 24 0.5343323 29.92261 0.2156932
#> 25 0.4781882 26.77854 0.2265308
#> 26 0.5366267 30.05109 0.2005033
#> 27 0.4207426 23.56159 0.1603094
#> 28 0.5379669 30.12615 0.2503362
#> 29 0.5267513 29.49807 0.1844874
#> 30 0.4445117 24.89266 0.2294671
#> 31 0.5654713 31.66639 0.2191826
#> 32 0.5155459 28.87057 0.1840442
#> 33 0.4965577 27.80723 0.1932755
#> 34 0.5158620 28.88827 0.1823632
#> 35 0.5116325 28.65142 0.1914568
#> 36 0.4963158 27.79368 0.1770900
#> 37 0.5637251 31.56860 0.2193993
#> 38 0.3125308 17.50173 0.2856812
#> 39 0.5223184 29.24983 0.1882459
#> 40 0.5167453 28.93774 0.1730295
#> 41 0.4978477 27.87947 0.1964317
#> 42 0.4147050 23.22348 0.2555282
#> 43 0.5225322 29.26180 0.2189672
#> 44 0.4757405 26.64147 0.1570377
#> multiplier_indirect_mean multiplier_indirect_sum multiplier_indirect_sd
#> 1 1.595989 89.37540 0.3132792
#> 2 1.670228 93.53279 0.2824143
#> 3 1.766745 98.93770 0.3025478
#> 4 1.758180 98.45809 0.3102728
#> 5 1.446785 81.01994 0.3106439
#> 6 1.624069 90.94785 0.3222676
#> 7 1.613609 90.36211 0.3308386
#> 8 2.043844 114.45526 0.6295754
#> 9 1.610033 90.16186 0.3151930
#> 10 1.820942 101.97276 0.2883791
#> 11 1.589768 89.02701 0.2640468
#> 12 1.618477 90.63474 0.2702931
#> 13 1.658440 92.87262 0.3433670
#> 14 1.731935 96.98833 0.3199271
#> 15 1.667476 93.37868 0.2822491
#> 16 1.662258 93.08645 0.2839664
#> 17 1.597653 89.46858 0.2651035
#> 18 1.435006 80.36032 0.2450676
#> 19 1.560022 87.36120 0.2344153
#> 20 1.675926 93.85188 0.3252045
#> 21 1.485790 83.20422 0.3379542
#> 22 1.440811 80.68543 0.4132435
#> 23 1.519069 85.06784 0.2701229
#> 24 1.710193 95.77081 0.3572023
#> 25 1.621761 90.81863 0.3716032
#> 26 1.859853 104.15175 0.3962250
#> 27 1.484598 83.13747 0.2383128
#> 28 1.718993 96.26362 0.3641433
#> 29 1.750614 98.03436 0.3120286
#> 30 1.443534 80.83793 0.2949739
#> 31 1.880106 105.28594 0.3854595
#> 32 1.668231 93.42094 0.3136666
#> 33 1.540310 86.25737 0.2214755
#> 34 1.675800 93.84481 0.2886215
#> 35 1.647321 92.24996 0.3308059
#> 36 1.650424 92.42377 0.2564010
#> 37 1.861747 104.25783 0.4121330
#> 38 1.384616 77.53848 0.3727750
#> 39 1.711203 95.82739 0.3410645
#> 40 1.678389 93.98979 0.2880304
#> 41 1.586916 88.86731 0.2653788
#> 42 1.516729 84.93682 0.3693283
#> 43 1.830646 102.51615 0.4762566
#> 44 1.493085 83.61276 0.2321731Bilateral Trade
Analyze bilateral trade between two specific countries, e.g., USA and CHN.
# check if USA and CHN are in countries list
if ("USA" %in% wiod_miom$countries && "CHN" %in% wiod_miom$countries) {
trade_usa_chn <- wiod_miom$get_bilateral_trade(origin = "USA", destination = "CHN")
# show first few rows/cols
trade_usa_chn[1:5, 1:5]
# total exports from USA to China by sector (aggregated over destination sectors)
# this sums the rows of the bilateral trade matrix (which is dest x origin)
# wait, get_bilateral_trade returns: rows = Dest_Sector, cols = Origin_Sector
# so colSums gives total imports of Dest from Origin_Sector.
# rowSums gives total exports from Origin to Dest_Sector? No.
# matrix is M[dest, origin].
# sum over rows (dest) = total exports of Origin_Sector to Dest.
exports_by_sector <- colSums(trade_usa_chn)
head(exports_by_sector)
}
#> USA_A01 USA_A02 USA_A03 USA_B USA_C10-C12 USA_C13-C15
#> 1346.70809 90.20986 55.09193 1995.00225 3482.28374 1296.73154Spillover Effects
Compute the spillover matrix and net spillover matrix.
spillover_matrix <- wiod_miom$get_spillover_matrix()
# inspect
spillover_matrix[1:5, 1:5]
#> AUS1 AUS2 AUS3 AUS4 AUS5
#> 1 0 0 0 0 0
#> 2 0 0 0 0 0
#> 3 0 0 0 0 0
#> 4 0 0 0 0 0
#> 5 0 0 0 0 0
net_spillover <- wiod_miom$get_net_spillover_matrix()
# inspect a subset
net_spillover[1:10, 1:10]
#> AUS AUT BEL BGR BRA CAN
#> AUS 0.000000000 0.03308871 0.02910521 0.2802544 -0.009619304 -0.06790634
#> AUT -0.033088705 0.00000000 -0.24427666 0.7102411 -0.078103459 -0.04387404
#> BEL -0.029105206 0.24427666 0.00000000 0.4287889 -0.327193252 -0.29693186
#> BGR -0.280254380 -0.71024114 -0.42878894 0.0000000 -0.259914966 -0.20550396
#> BRA 0.009619304 0.07810346 0.32719325 0.2599150 0.000000000 0.12553149
#> CAN 0.067906336 0.04387404 0.29693186 0.2055040 -0.125531485 0.00000000
#> CHE 0.015465872 0.05324425 0.34593311 0.3131248 -0.021995623 0.02625571
#> CHN 3.471572275 1.55788114 3.01111906 2.2935853 1.730436753 3.06593128
#> CYP -0.124012173 -0.37236993 -0.54590031 -0.1078680 -0.203786011 -0.19815408
#> CZE -0.078564130 -0.22511351 -0.32289790 0.4794011 -0.087302183 -0.08434043
#> CHE CHN CYP CZE
#> AUS -0.01546587 -3.471572 0.1240122 0.07856413
#> AUT -0.05324425 -1.557881 0.3723699 0.22511351
#> BEL -0.34593311 -3.011119 0.5459003 0.32289790
#> BGR -0.31312482 -2.293585 0.1078680 -0.47940107
#> BRA 0.02199562 -1.730437 0.2037860 0.08730218
#> CAN -0.02625571 -3.065931 0.1981541 0.08434043
#> CHE 0.00000000 -1.294078 0.4090038 0.26664486
#> CHN 1.29407847 0.000000 3.2602694 3.42797913
#> CYP -0.40900384 -3.260269 0.0000000 -0.20672085
#> CZE -0.26664486 -3.427979 0.2067209 0.00000000Regional Interdependence
Compute regional interdependence measures.
interdep <- wiod_miom$get_regional_interdependence()
print(interdep)
#> country self_reliance total_spillover_out total_spillover_in
#> 1 AUS 1.696224 0.3632201 0.0040162983
#> 2 AUT 1.586058 0.6115791 0.0090095852
#> 3 BEL 1.495057 0.8422347 0.0114892560
#> 4 BGR 1.595982 0.7066787 0.0014007742
#> 5 BRA 1.618409 0.2369146 0.0043294183
#> 6 CAN 1.646627 0.4621996 0.0051182683
#> 7 CHE 1.625249 0.4643511 0.0070199791
#> 8 CHN 2.340102 0.2323227 0.0506299644
#> 9 CYP 1.387663 0.6875217 0.0013510276
#> 10 CZE 1.665298 0.7239595 0.0066892623
#> 11 DEU 1.642981 0.4519089 0.0576191529
#> 12 DNK 1.517992 0.6184119 0.0043113174
#> 13 ESP 1.725480 0.4428036 0.0116828177
#> 14 EST 1.480984 0.7941247 0.0016056681
#> 15 FIN 1.629016 0.5600363 0.0060023444
#> 16 FRA 1.689994 0.5006097 0.0212039849
#> 17 GBR 1.675807 0.4214164 0.0234390351
#> 18 GRC 1.539636 0.3671881 0.0023126137
#> 19 HRV 1.491285 0.5594125 0.0012672109
#> 20 HUN 1.410111 0.7858478 0.0038462582
#> 21 IDN 1.530838 0.3661947 0.0028966474
#> 22 IND 1.517515 0.2810557 0.0050358306
#> 23 IRL 1.273685 0.7179835 0.0035625521
#> 24 ITA 1.807899 0.4366263 0.0234877501
#> 25 JPN 1.755295 0.3446541 0.0118213256
#> 26 KOR 1.854094 0.5423851 0.0106088918
#> 27 LTU 1.350929 0.5544117 0.0022257098
#> 28 LUX 1.224515 1.0324453 0.0041747256
#> 29 LVA 1.624611 0.6527536 0.0017624301
#> 30 MEX 1.472021 0.4160249 0.0020104733
#> 31 MLT 1.374231 1.0713464 0.0005224297
#> 32 NLD 1.466945 0.7168318 0.0166233196
#> 33 NOR 1.600207 0.4366607 0.0062282462
#> 34 POL 1.645235 0.5464274 0.0117964860
#> 35 PRT 1.619023 0.5399305 0.0016871450
#> 36 ROU 1.647438 0.4993024 0.0033268831
#> 37 RUS 1.523961 0.1731858 0.0236021049
#> 38 SVK 1.549752 0.6837695 0.0030367532
#> 39 SVN 1.528439 0.6666951 0.0015945661
#> 40 SWE 1.585521 0.4992427 0.0105977444
#> 41 TUR 1.521307 0.4101273 0.0062323422
#> 42 TWN 1.629496 0.7236817 0.0047127057
#> 43 USA 1.731486 0.2373394 0.0410605451
#> 44 ROW 1.884311 0.5411612 0.1107522191
#> interdependence_index
#> 1 0.2141345
#> 2 0.3855969
#> 3 0.5633463
#> 4 0.4427862
#> 5 0.1463873
#> 6 0.2806948
#> 7 0.2857107
#> 8 0.0992789
#> 9 0.4954531
#> 10 0.4347328
#> 11 0.2750542
#> 12 0.4073881
#> 13 0.2566264
#> 14 0.5362142
#> 15 0.3437880
#> 16 0.2962197
#> 17 0.2514706
#> 18 0.2384903
#> 19 0.3751211
#> 20 0.5572950
#> 21 0.2392120
#> 22 0.1852078
#> 23 0.5637055
#> 24 0.2415103
#> 25 0.1963510
#> 26 0.2925338
#> 27 0.4103930
#> 28 0.8431464
#> 29 0.4017907
#> 30 0.2826216
#> 31 0.7795970
#> 32 0.4886562
#> 33 0.2728776
#> 34 0.3321273
#> 35 0.3334916
#> 36 0.3030782
#> 37 0.1136419
#> 38 0.4412121
#> 39 0.4361934
#> 40 0.3148761
#> 41 0.2695889
#> 42 0.4441138
#> 43 0.1370727
#> 44 0.2871932
# visualize interdependence index
library(ggplot2)
ggplot(interdep, aes(x = reorder(country, interdependence_index), y = interdependence_index)) +
geom_bar(stat = "identity") +
coord_flip() +
labs(title = "Regional Interdependence Index", x = "Country", y = "Index") +
theme_minimal()
plot of chunk interdependence