The data are available at https://data.wto.org/ . Select: Indicators → International trade statistics, Reporting economies → all, Product / Sectors - Tariffs → all, Partner economies → World, Years → 2015. Download the data as a CSV file (ZIPed).
> wdir <- "D:/vlado/docs/papers/2021/sunbelt/weighted/exp"
> setwd(wdir)
> source("https://raw.githubusercontent.com/bavla/Rnet/master/R/Pajek.R")
> b <- function(A,cut=0){ return(matrix(as.numeric(A>cut),nrow=nrow(A),dimnames=dimnames(A))) }
> T <- read.csv("WtoData_2015exp.csv")
> dim(T)
[1] 79348 24
> str(T)
'data.frame': 79348 obs. of 24 variables:
$ Indicator.Category : Factor w/ 3 levels "Merchandise trade - indices and prices",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Indicator.Code : Factor w/ 38 levels "ITS_CS_AM6","ITS_CS_AX6",..: 11 11 11 11 11 11 11 11 11 11 ...
$ Indicator : Factor w/ 38 levels "Commercial services exports by main sector ...",..: 27 27 27 27 27 27 27 27 27 27 ...
$ Reporting.Economy.Code : Factor w/ 262 levels "000","004","008",..: 1 2 3 4 5 7 8 9 10 11 ...
$ Reporting.Economy.ISO3A.Code : Factor w/ 216 levels "","ABW","AFG",..: 1 3 6 59 11 4 12 15 9 13 ...
$ Reporting.Economy : Factor w/ 262 levels "Afghanistan",..: 257 1 5 6 7 10 12 22 13 19 ...
$ Partner.Economy.Code : int 0 0 0 0 0 0 0 0 0 0 ...
$ Partner.Economy.ISO3A.Code : logi NA NA NA NA NA NA ...
$ Partner.Economy : Factor w/ 1 level "World": 1 1 1 1 1 1 1 1 1 1 ...
$ Product.Sector.Classification.Code: Factor w/ 2 levels "BOP6","SITC3": 2 2 2 2 2 2 2 2 2 2 ...
$ Product.Sector.Classification : Factor w/ 2 levels "Merchandise - SITC Revision 3 (aggregates)",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Product.Sector.Code : Factor w/ 187 levels "AG","AGFO","GS",..: 187 187 187 187 187 187 187 187 187 187 ...
$ Product.Sector : Factor w/ 180 levels "Accommodation services",..: 170 170 170 170 170 170 170 170 170 170 ...
$ Period.Code : Factor w/ 17 levels "A","M01","M02",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Period : Factor w/ 17 levels "Annual","April",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Frequency.Code : Factor w/ 3 levels "A","M","Q": 1 1 1 1 1 1 1 1 1 1 ...
$ Frequency : Factor w/ 3 levels "Annual","Monthly",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Unit.Code : Factor w/ 8 levels "APC","ID0501",..: 5 5 5 5 5 5 5 5 5 5 ...
$ Unit : Factor w/ 8 levels "% change over previous month",..: 8 8 8 8 8 8 8 8 8 8 ...
$ Year : int 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ...
$ Value.Flag.Code : Factor w/ 2 levels "","E": 1 1 1 1 1 1 1 1 1 1 ...
$ Value.Flag : Factor w/ 2 levels "","Estimate": 1 1 1 1 1 1 1 1 1 1 ...
$ Text.Value : logi NA NA NA NA NA NA ...
$ Value : num 87.6 99.9 82.4 88.3 110.5 ...
> select <- c("Reporting.Economy.ISO3A.Code","Reporting.Economy","Product.Sector.Code","Product.Sector","Value")
> D <- T[,select]
> dim(D)
[1] 79348 5
> N <- D[,c("Reporting.Economy.ISO3A.Code","Product.Sector","Value")]
> colnames(N) <- c("country","sector","value")
> nr <- length(levels(N$country)); nc<- length(levels(N$sector))
> nr
[1] 216
> nc
[1] 180
> B <- matrix(data=0,nrow=nr,ncol=nc,dimnames=list(levels(N$country),levels(N$sector)))
> for(i in 1:nrow(N)) B[as.integer(N$country[i]),as.integer(N$sector[i])] <- N$value[i]
> names15 <- substr(levels(N$sector),1,15)
> names <- levels(N$sector)
> names15
[1] "Accommodation s" "Accounting, aud" "Acquisition of " "Advertising ser" "Advertising, ma"
[6] "Agricultural pr" "Air transport" "Architectural s" "Architectural, " "Artistic relate"
[11] "Audio-visual se" "Audiovisual and" "Audiovisual pro" "Automotive prod" "Auxiliary insur"
...
[171] "Tourism-related" "Trade-related s" "Trademarks" "Transport" "Transport equip"
[176] "Travel" "Underwriting an" "Waste treatment" "Waste treatment" "Work undertaken"
> C <- colSums(B)
> sum(C>0)
[1] 180
> names[60:64]
[1] "Goods for processing abroad - Goods sent , Goods r" "Goods for processing in reporting economy – Goods "
[3] "Gross freight insurance claims receivable (credits" "Gross freight insurance premiums receivable (credi"
[5] "Gross life insurance claims receivable (credits) a"
> names15[60] <- "Goods 4 abroad"
> names15[61] <- "Goods 4 inside"
> names15[62] <- "GFI claims"
> names15[63] <- "GFI premiums"
> names15[64] <- "GLI claims"
> names15[65] <- "GLI premiums"
> names15[66] <- "GODI claims"
> names15[67] <- "GODI premiums"
> names15[80] <- "LRD audio-vis"
> names15[81] <- "LRD computer"
> names15[82] <- "LRD other"
> names[105:107]
[1] "Of which: payable by border, seasonal, and other s" "Of which: Payable by border, seasonal, and other s"
> names15[111] <- "Other -postal"
> names15[120] <- "Other BS"
> names15[121] <- "Other BS n.i.e."
> names15[123] <- "Other CS"
> names15[124] <- "Other CS -CC"
> names15[128] <- "Other PS"
> names15[129] <- "Other PCRS"
> names15[160] <- "Serv agricultur"
> names15[161] <- "Serv mining"
> names15[166] <- "Tele equipment"
> names15[167] <- "Tele services"
> names15[168] <- "Tele computer"
> names15[179] <- "Waste agricult"
> colnames(B) <- names15
> dim(B)
[1] 216 180
> bimatrix2net(B,Net="WTO_2015exp.net")
> CS <- B > rs <- rowSums(CS) > cs <- colSums(CS) > Q <- CS > T <- sum(CS) > for(u in 1:nrow(CS)) for(v in 1:ncol(CS)) Q[u,v] <- a <- CS[u,v]*T/rs[u]/cs[v] > Z <- log(Q) Warning message: In log(Q) : NaNs produced > Z[Z == -Inf] <- 0 > Z[is.nan(Z)] <- 0 > t <- hclust(dist(Z),method="ward.D") > plot(t,hang=-1,cex=0.3,main="WTO countries export / log-deviation / Ward") > f <- hclust(dist(t(Z)),method="ward.D") > plot(f,hang=-1,cex=0.3,main="WTO sectors export / log-deviation / Ward") > > bimatrix2net(t(Z),Net="WTOexplogdev.net") > pC <- cutree(t,4) > vector2clu(t$order,Clu="WTOexplogdevC.per") > vector2clu(pC,Clu="WTOexplogdevC.clu") > pS <- cutree(f,4) > vector2clu(f$order,Clu="WTOexplogdevS.per") > vector2clu(pS,Clu="WTOexplogdevS.clu") > save(CS,names,file="WTOexp_2015.Rdata",ascii=TRUE)