library(ioanalysis)Loading required package: ggplot2Loading required package: plot3DLoading required package: lpSolveInput-Output
Analisis Input Output
Membangun Object Input Output
Matriks Z
b001<-c(72,13,8,58,60,22,24,91,43,92)
b002<-c(5,1,28,64,94,65,96,15,76,30)
b003<-c(53,7,47,25,93,16,90,79,81,23)
b004<-c(46,41,66,96,90,49,2,29,35,66)
b005<-c(73,40,37,87,29,47,95,97,46,1)
b006<-c(55,67,26,95,10,16,96,67,70,99)
b007<-c(73,24,19,9,65,52,82,79,77,87)
b008<-c(96,63,46,97,68,36,47,40,76,61)
b009<-c(28,32,44,86,22,68,45,89,6,48)
b010<-c(81,62,91,64,52,100,58,94,65,17)
Z <-matrix(rbind(b001,b002,b003,b004,b005,b006,b007,b008,b009,b010), ncol=10)###Label Matriks Transaksi
region <- c(rep("Sulawesi Selatan",5),rep("Jawa Timur",5))
sector <- c("Pertanian","Perikanan","Industri","Pemerintahan","Jasa")
sector <- c(sector,sector)
id <- rbind(region,sector)
blank <- matrix(NA, ncol = 2, nrow = 2)
Z <- rbind( cbind(blank, id), cbind(t(id), Z))Matriks Nilai Tambah
V<-matrix(c(
297,284,126,196,179,211,132,147,141,259,
247,106,107,103,280,293,183,264,272,221,
173,266,100,199,191,119,251,238,286,227), ncol=10, byrow=TRUE)
label <- matrix(c("Upah", "Pendapatan Pemilik", "Pajak Tak Langsung"),
ncol = 1)
blank <- matrix(NA, ncol = 1, nrow = 3)
V <- cbind(blank, label, V)Matriks Permintaan Akhir
f<-matrix(c(
177,296,236,169,285,52,208,64,
199,167,23,63,105,59,87,166,
270,35,51,97,218,223,155,106,
71,117,202,255,4,261,200,156,
153,18,24,108,91,123,60,267,
37,22,48,102,12,286,196,179,
241,147,165,26,150,209,269,228,
154,142,141,84,139,29,290,164,
299,211,289,219,284,78,203,122,
297,291,14,185,215,229,89,6),nrow =10,byrow=TRUE)
label <- c("Rumahtangga", "Pemerintah", "Investasi", "Ekspor")
label <- matrix(c(label, label), nrow = 1)
id <- rbind(region[c(1:4,6:9)], label)
f <- rbind(id, f)Matriks Kolom Total Produksi
one.10 <- matrix(rep(1, 10), ncol = 1)
one.8 <- matrix(rep(1, 8), ncol = 1)
X <- matrix(as.numeric(Z[3:12, 3:12]), nrow = 10)%*%one.10 +
matrix(as.numeric(f[3:12,]), nrow = 10)%*%one.8
label <- matrix(c(NA,"Total"))
X <- rbind(label, X)Matriks Kolom Impor (dalam hal ini adalah residual)
M <- matrix(NA, nrow = 1, ncol = 12)
one.3 <- matrix(rep(1, 3), ncol = 1)
M[1, 3:12] <- t(one.10)%*%matrix(as.numeric(Z[3:12, 3:12]), nrow = 10) +
t(one.3)%*%matrix(as.numeric(V[,3:12]), nrow = 3)
M[1, 2] <- "Impor"###Satukan semuanya
blank <- matrix(NA, nrow=5, ncol = 9)
holder <- cbind(f, X)
holder <- rbind(holder, blank)
hold <- rbind(Z, V, M, t(X))
ss.FullIOTable <- cbind(hold, holder)Membuat Matriks fV dan memasukkan ke Dalam Tabel IO
fV <- matrix(c(
21,22,55,27,33,77,
78,77,53,20,1,48), ncol=6, byrow=TRUE)
ss.FullIOTable[15:16, c(13:15, 17:19)] <- fVMemasukkan Data menjadi Objek IO
Z <- matrix(as.numeric(ss.FullIOTable[3:12, 3:12]), ncol = 10)
f <- matrix(as.numeric(ss.FullIOTable[3:12, c(13:15, 17:19)]), nrow = dim(Z)[1])
E <- matrix(as.numeric(ss.FullIOTable[3:12, c(16, 20)]), nrow = 10)
X <- matrix(as.numeric(ss.FullIOTable[3:12, 21]), ncol = 1)
V <- matrix(as.numeric(ss.FullIOTable[13:15, 3:12]), ncol = 10)
M <- as.numeric(ss.FullIOTable[16, 3:12])
fV <- matrix(as.numeric(ss.FullIOTable[15:16, c(13:15,17:19)]), nrow = 2)ss.IO <- as.inputoutput(Z = Z, RS_label = ss.FullIOTable[3:12, 1:2],
f = f, f_label = ss.FullIOTable[1:2, c(13:15, 17:19)],
E = E, E_label = ss.FullIOTable[1:2, c(16, 20)],
X = X,
V = V, V_label = ss.FullIOTable[13:15, 2],
M = M, M_label = ss.FullIOTable[16,2],
fV = fV, fV_label = ss.FullIOTable[15:16, 2])Analisi Input Output
Memeriksa kesamaan Sektor
check.RS(ss.IO)[1] TRUE###Analisis Keterkaitan
linkages(ss.IO, ES = NULL, regions = "all", sectors = "all", type = c("total"),
normalize = FALSE, intra.inter = FALSE)$`Sulawesi Selatan`
BL.tot FL.tot
Pertanian 1.435956 1.359417
Perikanan 1.386453 1.517649
Industri 1.360057 1.447335
Pemerintahan 1.563565 1.430537
Jasa 1.607494 1.574279
$`Jawa Timur`
BL.tot FL.tot
Pertanian 1.460790 1.586101
Perikanan 1.460265 1.413869
Industri 1.557731 1.515752
Pemerintahan 1.385242 1.319484
Jasa 1.381315 1.499344###Total Ekspor
export.total(ss.IO) [,1]
[1,] 1050
[2,] 762
[3,] 1088
[4,] 1057
[5,] 935
[6,] 641
[7,] 997
[8,] 1055
[9,] 1352
[10,] 1143###Total Impor
import.total(ss.IO) [,1]
[1,] 1299
[2,] 1006
[3,] 745
[4,] 1179
[5,] 1233
[6,] 1094
[7,] 1201
[8,] 1329
[9,] 1274
[10,] 1231L1 <- leontief.inv(ss.IO, region = "Sulawesi Selatan")
# Otherwise
Z <- ss.IO$Z
X <- ss.IO$X
L2 <- leontief.inv(Z, X)
View(L1)
View(L2)###Sektor Kunci
sektor_kunci <- key.sector(ss.IO)
View(sektor_kunci)###-
extraction(ss.IO, ES = NULL, regions = 1, sectors = 1, type = "backward.total",
aggregate = FALSE, simultaneous = FALSE, normalize = FALSE) Pertanian
Sulawesi Selatan.Pertanian 668.4299
Sulawesi Selatan.Perikanan 373.1259
Sulawesi Selatan.Industri 387.9606
Sulawesi Selatan.Pemerintahan 600.7683
Sulawesi Selatan.Jasa 584.2872
Jawa Timur.Pertanian 480.8720
Jawa Timur.Perikanan 463.1370
Jawa Timur.Industri 497.8907
Jawa Timur.Pemerintahan 495.6907
Jawa Timur.Jasa 444.4978