A15-Análisis Insumo Producto Teoría de Leontief y Análisis de Encadenamiento de Rasmussen

Funciones

#Matriz de coeficientes tecnicos
mip_coeficientes_tecnicos<-function(matriz_consumo_intermedio,
                                    vector_demanda_final){
  filas_ci<-nrow(matriz_consumo_intermedio)
  columnas_ci<-ncol(matriz_consumo_intermedio)
  filas_x<-nrow(vector_demanda_final)
  if(filas_ci!=columnas_ci){
    stop("Ingrese una matriz de Consumo Intermedio, Cuadrada",call. = FALSE)
  }
  if(filas_ci!=filas_x){
    stop("Vector de demanda final incompatible (diferente dimensión)",call. = FALSE)
  }
  v<-solve(diag(as.vector(vector_demanda_final)))
  A<-matriz_consumo_intermedio%*%v
  list(A=A,V=v)
}

#matriz tecnológica

mip_matriz_tecnologica<-function(matriz_coeficientes_tecnicos){
  filas_A<-nrow(matriz_coeficientes_tecnicos)
  columnas_A<-ncol(matriz_coeficientes_tecnicos)
  if(filas_A!=columnas_A){
    stop("Ingrese una matriz de coef. técnicos cuadrada",call. = FALSE)
  }
  tipo_matriz<-typeof(matriz_coeficientes_tecnicos)
  if(tipo_matriz!="double"){
    stop("La matriz ingresada no es numerica",call. = FALSE)
  }
  T<-diag(1,filas_A)-matriz_coeficientes_tecnicos
  T
}
mip_matriz_leontief<-function(matriz_tecnologica){
  L<-solve(matriz_tecnologica)
  L
}
mip_multiplicadores_produccion_mp<-function(matriz_leontief){
  mp<-rowSums(matriz_leontief)
  mp
}
mip_multiplicadores_expansion_demanda_me<-function(matriz_leontief){
  me<-colSums(matriz_leontief)
  me
}
mip_encadenamiento_pd<-function(matriz_leontief){
  mp<-mip_multiplicadores_produccion_mp(matriz_leontief)
  mp/mean(mp)
}
mip_encadenamiento_sd<-function(matriz_leontief){
  me<-mip_multiplicadores_expansion_demanda_me(matriz_leontief)
  me/mean(me)
}
mip_tabla_rasmussen<-function(matriz_leontief){
library(dplyr)
pd<-mip_encadenamiento_pd(matriz_leontief)
sd<-mip_encadenamiento_sd(matriz_leontief)
rasmussen<-data.frame(pd=pd,sd=sd)
rasmussen_clasificado<-rasmussen %>% 
  mutate(clasificacion=case_when(pd>1 & sd>1 ~ "Sector Clave",
                                           pd<1 & sd>1 ~"Sector Estrategico",
                                           pd>1 & sd<1 ~"Sector Impulsor",
                                           pd<1 & sd<1 ~"Sector Isla",
                                           TRUE ~ "No clasificado")) %>% mutate(sector=row_number()) %>% select(sector,pd,sd,clasificacion)
rasmussen_clasificado
}

Obtención de Datos

Cálculo del Consumo Intermedio, la Demanda Final y el Vector de Producción

Consumo Intermedio “2006 y 2009”

#Para el año 1990
library(readxl)
mip_1990_ci<-read_excel("C:/Users/User/Desktop/documents_rstudio/ciclo_6/Anexo_Resolución_32_MATRIZ_INSUMO_PRODUCTO_A_PRECIOS_CORRIENTES_EN_DOLARES_1990-2006.xlsx", 
    sheet = "MIP 1990", range = "I14:BA59")
names(mip_1990_ci)<-as.character(1:46)
print(mip_1990_ci)

## # A tibble: 45 × 45
##       `1`     `2`     `3`    `4`    `5`    `6`    `7`   `8`    `9`  `10`    `11`
##     <dbl>   <dbl>   <dbl>  <dbl>  <dbl>  <dbl>  <dbl> <dbl>  <dbl> <dbl>   <dbl>
##  1   0       0        0      0   0         0   0        0   0         0  0      
##  2   0       0     3753.     0   0      1901.  7.00e3   0   0         0  5.26e-1
##  3   0       0        0   1678.  0         0   0        0   0         0  0      
##  4   0    1095.   12618.   298.  1.83e3    0   0        0   0         0  6.84e+0
##  5   0       0        0      0   0      7085.  5.53e0   0   0         0  7.21e+4
##  6   0       0        0      0   0         0   6.77e3   0   9.21e0    0  4.48e+3
##  7 584.     20.5    884.    90.7 1.63e2   31.7 0       16.6 0         0  1.27e+2
##  8   0       0        0      0   0         0   0        0   1.74e3    0  0      
##  9   1.05    2.63    33.3   19.6 6.18e0  984.  1.18e0   0   1.84e0 2010. 2.5 e+0
## 10   0       0        0      0   0      1506.  0        0   1.70e3    0  4.86e+3
## # ℹ 35 more rows
## # ℹ 34 more variables: `12` <dbl>, `13` <dbl>, `14` <dbl>, `15` <dbl>,
## #   `16` <dbl>, `17` <dbl>, `18` <dbl>, `19` <dbl>, `20` <dbl>, `21` <dbl>,
## #   `22` <dbl>, `23` <dbl>, `24` <dbl>, `25` <dbl>, `26` <dbl>, `27` <dbl>,
## #   `28` <dbl>, `29` <dbl>, `30` <dbl>, `31` <dbl>, `32` <dbl>, `33` <dbl>,
## #   `34` <dbl>, `35` <dbl>, `36` <dbl>, `37` <dbl>, `38` <dbl>, `39` <dbl>,
## #   `40` <dbl>, `41` <dbl>, `42` <dbl>, `43` <dbl>, `44` <dbl>, `45` <dbl>

#Para el año 1990
# Para el año 2006
library(readxl)
library(dplyr)


mip_2006_ci<-read_excel("C:/Users/User/Desktop/documents_rstudio/ciclo_6/Anexo_Resolución_32_MATRIZ_INSUMO_PRODUCTO_A_PRECIOS_CORRIENTES_EN_DOLARES_1990-2006.xlsx", 
    sheet = "MIP 2006", range = "I15:BB60", 
    col_names = FALSE)
names(mip_2006_ci)<-as.character(1:46)
print(mip_2006_ci)

## # A tibble: 46 × 46
##      `1`   `2`   `3`   `4`   `5`   `6`   `7`   `8`   `9`  `10`  `11`   `12`
##    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>
##  1    86     0     0     0     0     0     0     0     0     0     0      0
##  2     0     0     0     0     0     0     0     0     0     0     0      0
##  3     0     0 11004     0     0  5313 28072     0     0     0     1     65
##  4     0     0     0  3071     0     0     0     0     0     0     0      0
##  5     0   104 38676  1357 11299     0     0     0     0     0    14    469
##  6     0     0     0     0     0  8777    19     0     0     0 89833 144571
##  7     0     0     0     0     0     0 16874     0     7     0  4137     57
##  8   576     1  2000   272   563    65     0    36     0     0   204    734
##  9     0     0     0     0     0     0     0     0 44464     0     0      0
## 10     1     1    73    58    21  2023     6     0     2 15334     3     89
## # ℹ 36 more rows
## # ℹ 34 more variables: `13` <dbl>, `14` <dbl>, `15` <dbl>, `16` <dbl>,
## #   `17` <dbl>, `18` <dbl>, `19` <dbl>, `20` <dbl>, `21` <dbl>, `22` <dbl>,
## #   `23` <dbl>, `24` <dbl>, `25` <dbl>, `26` <dbl>, `27` <dbl>, `28` <dbl>,
## #   `29` <dbl>, `30` <dbl>, `31` <dbl>, `32` <dbl>, `33` <dbl>, `34` <dbl>,
## #   `35` <dbl>, `36` <dbl>, `37` <dbl>, `38` <dbl>, `39` <dbl>, `40` <dbl>,
## #   `41` <dbl>, `42` <dbl>, `43` <dbl>, `44` <dbl>, `45` <dbl>, `46` <dbl>

Demanda Final “1990 y 2006”

# Pra ael año 1990
mip_1990_df <-read_excel("C:/Users/User/Desktop/documents_rstudio/ciclo_6/Anexo_Resolución_32_MATRIZ_INSUMO_PRODUCTO_A_PRECIOS_CORRIENTES_EN_DOLARES_1990-2006.xlsx", 
    sheet = "MIP 1990", range = "BD14:BE59", 
    col_names = FALSE)
names(mip_1990_df)<-c(1:2)
print(mip_1990_df)

## # A tibble: 46 × 2
##        `1`   `2`
##      <dbl> <dbl>
##  1      0      0
##  2      0      0
##  3 154500.     0
##  4      0      0
##  5 182388.     0
##  6  28392.     0
##  7 161574.     0
##  8  50923.     0
##  9  20244.     0
## 10   4181.     0
## # ℹ 36 more rows

# Para el año 2006
library(readxl)
mip_2006_df <-read_excel("C:/Users/User/Desktop/documents_rstudio/ciclo_6/Anexo_Resolución_32_MATRIZ_INSUMO_PRODUCTO_A_PRECIOS_CORRIENTES_EN_DOLARES_1990-2006.xlsx", 
    sheet = "MIP 2006", range = "BD15:BE60", 
    col_names = FALSE)
names(mip_2006_df)<-c(1:2)
print(mip_2006_df)

## # A tibble: 46 × 2
##         `1`   `2`
##       <dbl> <dbl>
##  1       0      0
##  2       0      0
##  3  314561.     0
##  4       0      0
##  5 1323380.     0
##  6   50357.     0
##  7  526957.     0
##  8  101237.     0
##  9   44341.     0
## 10    2422      0
## # ℹ 36 more rows

Vector de producción “1990 y 2006”

#Para el año 1990
library(readxl)
vectorprodu1990_x<-read_excel("C:/Users/User/Desktop/documents_rstudio/ciclo_6/Anexo_Resolución_32_MATRIZ_INSUMO_PRODUCTO_A_PRECIOS_CORRIENTES_EN_DOLARES_1990-2006.xlsx", 
    sheet = "MIP 1990", range = "BI14:BI59", 
    col_names = FALSE)
names(vectorprodu1990_x)<-c("x")
print(vectorprodu1990_x)

## # A tibble: 46 × 1
##          x
##      <dbl>
##  1 254545.
##  2  31712.
##  3 293495.
##  4  44229.
##  5 246870.
##  6 199058.
##  7 182977.
##  8  71190.
##  9  42357.
## 10 157813.
## # ℹ 36 more rows

# Para el año 2006
 library(readxl)
vectorprodu2006_x <- read_excel("C:/Users/User/Desktop/documents_rstudio/ciclo_6/Anexo_Resolución_32_MATRIZ_INSUMO_PRODUCTO_A_PRECIOS_CORRIENTES_EN_DOLARES_1990-2006.xlsx", 
    sheet = "MIP 2006", range = "BI15:BI60", 
    col_names = FALSE)
names(vectorprodu2006_x)<-c("x")
print(vectorprodu2006_x)

## # A tibble: 46 × 1
##           x
##       <dbl>
##  1  182287 
##  2   34523 
##  3  715438.
##  4   80222 
##  5 1474837.
##  6  312032.
##  7  576046.
##  8  172777.
##  9  188273.
## 10  490735.
## # ℹ 36 more rows

Reducción de filas y columnas de la MIP “1990 y 2006”

# Para el año 1990
library(dplyr)

# Sumar filas 41 a 45 de mip_1990_ci
servicios_row1990<- colSums(mip_1990_ci[41:45, ])

# Combinar las primeras 40 filas con la nueva fila sumada (servicios_row1990)
temporal1990 <- rbind(mip_1990_ci[1:40, ], servicios_row1990)

# Sumar columnas 41 a 45 en las filas del data frame temporal1990
servicios_col1990 <- rowSums(temporal1990[, 41:45])

# Combinar las primeras 40 columnas con la nueva columna sumada (servicios_col1990)
mip_1990_ci_corregida <- cbind(temporal1990[, 1:40], servicios_col1990)

# Renombrar las columnas de mip_1990_ci_corregida
names(mip_1990_ci_corregida) <- as.character(1:41)

# Sumar filas 41 a 45 de mip1990_x y agregar al final
x1990 <- rbind(vectorprodu1990_x[1:40, ], colSums(vectorprodu1990_x[41:45, ]))
print(x1990)

## # A tibble: 41 × 1
##          x
##      <dbl>
##  1 254545.
##  2  31712.
##  3 293495.
##  4  44229.
##  5 246870.
##  6 199058.
##  7 182977.
##  8  71190.
##  9  42357.
## 10 157813.
## # ℹ 31 more rows

# Para el año 2006
library(dplyr)

# Sumar filas 41 a 46 de mip_2006_ci
servicios_row <- colSums(mip_2006_ci[41:46, ])

# Combinar las primeras 40 filas con la nueva fila sumada (servicios_row)
temporal <- rbind(mip_2006_ci[1:40, ], servicios_row)

# Sumar columnas 41 a 46 en las filas del data frame temporal
servicios_col <- rowSums(temporal[, 41:46])

# Combinar las primeras 40 columnas con la nueva columna sumada (servicios_col)
mip_2006_ci_corregida <- cbind(temporal[, 1:40], servicios_col)

# Renombrar las columnas de mip_2006_ci_corregida
names(mip_2006_ci_corregida) <- as.character(1:41)

# Sumar filas 41 a 46 de mip2006_x y agregar al final
x2006 <- rbind(vectorprodu2006_x[1:40, ], colSums(vectorprodu2006_x[41:46, ]))
print(x2006)

## # A tibble: 41 × 1
##           x
##       <dbl>
##  1  182287 
##  2   34523 
##  3  715438.
##  4   80222 
##  5 1474837.
##  6  312032.
##  7  576046.
##  8  172777.
##  9  188273.
## 10  490735.
## # ℹ 31 more rows

Matriz de Coeficientes Técnicos “1990 y 2006”

options(scipen = 999)
# Para el año 1990
A1990<-mip_coeficientes_tecnicos(as.matrix(mip_1990_ci_corregida),as.matrix(x1990))[[1]]
head(A1990,4)

##      [,1]       [,2]       [,3]        [,4]        [,5]        [,6]       [,7]
## [1,]    0 0.00000000 0.00000000 0.000000000 0.000000000 0.000000000 0.00000000
## [2,]    0 0.00000000 0.01278693 0.000000000 0.000000000 0.009552252 0.03825109
## [3,]    0 0.00000000 0.00000000 0.037939549 0.000000000 0.000000000 0.00000000
## [4,]    0 0.03454185 0.04299281 0.006744214 0.007411221 0.000000000 0.00000000
##      [,8] [,9] [,10]          [,11]         [,12]       [,13]     [,14]
## [1,]    0    0     0 0.000000000000 0.00000000000 0.000000000 0.0000000
## [2,]    0    0     0 0.000003552546 0.00007609264 0.000000000 0.2496263
## [3,]    0    0     0 0.000000000000 0.00000000000 0.000000000 0.0000000
## [4,]    0    0     0 0.000046183100 0.00054714229 0.002867843 0.0000000
##          [,15]      [,16]       [,17]     [,18]      [,19]       [,20] [,21]
## [1,] 0.0000000 0.00000000 0.000000000 0.0000000 0.09133329 0.014688976     0
## [2,] 0.0000000 0.08980084 0.000000000 0.0000000 0.00000000 0.000000000     0
## [3,] 0.3799551 0.00000000 0.000000000 0.0000000 0.00000000 0.000000000     0
## [4,] 0.0000000 0.02852157 0.007891027 0.1023182 0.03071086 0.002985359     0
##            [,22] [,23] [,24] [,25] [,26] [,27] [,28] [,29] [,30]       [,31]
## [1,] 0.000000000     0     0     0     0     0     0     0     0 0.000000000
## [2,] 0.000000000     0     0     0     0     0     0     0     0 0.000000000
## [3,] 0.000000000     0     0     0     0     0     0     0     0 0.000000000
## [4,] 0.002471997     0     0     0     0     0     0     0     0 0.001230708
##      [,32] [,33] [,34] [,35]      [,36]         [,37] [,38]         [,39] [,40]
## [1,]     0     0     0     0 0.00000000 0.00000000000     0 0.00000000000     0
## [2,]     0     0     0     0 0.04047362 0.00000000000     0 0.00000000000     0
## [3,]     0     0     0     0 0.00000000 0.00000000000     0 0.00000000000     0
## [4,]     0     0     0     0 0.00000000 0.00000665244     0 0.00003812297     0
##            [,41]
## [1,] 0.000000000
## [2,] 0.007502471
## [3,] 0.000000000
## [4,] 0.002060970

# Para el año 2006
A2006<-mip_coeficientes_tecnicos(as.matrix(mip_2006_ci_corregida),as.matrix(x2006))[[1]]
head(A2006,4)

##              [,1] [,2]       [,3]       [,4] [,5]      [,6]       [,7] [,8]
## [1,] 0.0004717835    0 0.00000000 0.00000000    0 0.0000000 0.00000000    0
## [2,] 0.0000000000    0 0.00000000 0.00000000    0 0.0000000 0.00000000    0
## [3,] 0.0000000000    0 0.01538079 0.00000000    0 0.0170271 0.04873222    0
## [4,] 0.0000000000    0 0.00000000 0.03828127    0 0.0000000 0.00000000    0
##      [,9] [,10]          [,11]        [,12] [,13]     [,14]     [,15]
## [1,]    0     0 0.000000000000 0.0000000000     0 0.0000000 0.0000000
## [2,]    0     0 0.000000000000 0.0000000000     0 0.0000000 0.0000000
## [3,]    0     0 0.000002709117 0.0001164731     0 0.1624212 0.0000000
## [4,]    0     0 0.000000000000 0.0000000000     0 0.0000000 0.2385924
##            [,16] [,17] [,18]      [,19]       [,20] [,21] [,22] [,23] [,24]
## [1,] 0.006960395     0     0 0.00000000 0.000000000     0     0     0     0
## [2,] 0.000000000     0     0 0.03289641 0.004344639     0     0     0     0
## [3,] 0.055879172     0     0 0.00000000 0.000000000     0     0     0     0
## [4,] 0.000000000     0     0 0.00000000 0.000000000     0     0     0     0
##      [,25] [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35]
## [1,]     0     0     0     0     0     0     0     0     0     0     0
## [2,]     0     0     0     0     0     0     0     0     0     0     0
## [3,]     0     0     0     0     0     0     0     0     0     0     0
## [4,]     0     0     0     0     0     0     0     0     0     0     0
##           [,36] [,37] [,38] [,39] [,40]       [,41]
## [1,] 0.00000000     0     0     0     0 0.000000000
## [2,] 0.00000000     0     0     0     0 0.000000000
## [3,] 0.02576303     0     0     0     0 0.004543355
## [4,] 0.00000000     0     0     0     0 0.000000000

Matriz técnologica “1990 y 2006”

library(knitr)

## Warning: package 'knitr' was built under R version 4.4.1

# Para el año 1990
matriz_T1990<-mip_matriz_tecnologica(matriz_coeficientes_tecnicos = A1990)
head(matriz_T1990,4)

##      [,1]        [,2]        [,3]        [,4]         [,5]         [,6]
## [1,]    1  0.00000000  0.00000000  0.00000000  0.000000000  0.000000000
## [2,]    0  1.00000000 -0.01278693  0.00000000  0.000000000 -0.009552252
## [3,]    0  0.00000000  1.00000000 -0.03793955  0.000000000  0.000000000
## [4,]    0 -0.03454185 -0.04299281  0.99325579 -0.007411221  0.000000000
##             [,7] [,8] [,9] [,10]           [,11]          [,12]        [,13]
## [1,]  0.00000000    0    0     0  0.000000000000  0.00000000000  0.000000000
## [2,] -0.03825109    0    0     0 -0.000003552546 -0.00007609264  0.000000000
## [3,]  0.00000000    0    0     0  0.000000000000  0.00000000000  0.000000000
## [4,]  0.00000000    0    0     0 -0.000046183100 -0.00054714229 -0.002867843
##           [,14]      [,15]       [,16]        [,17]      [,18]       [,19]
## [1,]  0.0000000  0.0000000  0.00000000  0.000000000  0.0000000 -0.09133329
## [2,] -0.2496263  0.0000000 -0.08980084  0.000000000  0.0000000  0.00000000
## [3,]  0.0000000 -0.3799551  0.00000000  0.000000000  0.0000000  0.00000000
## [4,]  0.0000000  0.0000000 -0.02852157 -0.007891027 -0.1023182 -0.03071086
##             [,20] [,21]        [,22] [,23] [,24] [,25] [,26] [,27] [,28] [,29]
## [1,] -0.014688976     0  0.000000000     0     0     0     0     0     0     0
## [2,]  0.000000000     0  0.000000000     0     0     0     0     0     0     0
## [3,]  0.000000000     0  0.000000000     0     0     0     0     0     0     0
## [4,] -0.002985359     0 -0.002471997     0     0     0     0     0     0     0
##      [,30]        [,31] [,32] [,33] [,34] [,35]       [,36]          [,37]
## [1,]     0  0.000000000     0     0     0     0  0.00000000  0.00000000000
## [2,]     0  0.000000000     0     0     0     0 -0.04047362  0.00000000000
## [3,]     0  0.000000000     0     0     0     0  0.00000000  0.00000000000
## [4,]     0 -0.001230708     0     0     0     0  0.00000000 -0.00000665244
##      [,38]          [,39] [,40]        [,41]
## [1,]     0  0.00000000000     0  0.000000000
## [2,]     0  0.00000000000     0 -0.007502471
## [3,]     0  0.00000000000     0  0.000000000
## [4,]     0 -0.00003812297     0 -0.002060970

# Usar kable para mostrar la matriz
kable(matriz_T1990, caption = "Matriz Ejemplo", format = "html", digits = 2, align = 'c', escape = FALSE)

Matriz Ejemplo
1.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.09	-0.01	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	1.00	-0.01	0.00	0.00	-0.01	-0.04	0.00	0.00	0.00	0.00	0.00	0.00	-0.25	0.00	-0.09	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.04	0.00	0.00	0.00	0.00	-0.01
0.00	0.00	1.00	-0.04	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.38	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	-0.03	-0.04	0.99	-0.01	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.03	-0.01	-0.10	-0.03	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.00	1.00	-0.04	0.00	0.00	0.00	0.00	-0.49	-0.42	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.01	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	1.00	-0.04	0.00	0.00	0.00	-0.03	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.02	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	0.00	1.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.01	0.00	0.00	0.00	0.00	0.00	0.00	-0.10	0.00	0.00	0.00	0.00	-0.02	-0.02	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	0.00	0.00	1.00	-0.04	0.00	0.00	0.00	-0.02	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.01	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	1.00	-0.01	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.01	-0.35	0.00	-0.06	0.00	0.00	0.00	0.00	0.00	-0.03	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	-0.01	0.00	0.00	-0.04	1.00	-0.03	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.03	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.05	0.00	0.00	0.00	0.00	-0.01
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	1.00	-0.08	0.00	-0.01	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.01	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	1.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	-0.04	-0.04	0.00	0.00	0.00	0.00	0.00	1.00	-0.19	0.00	-0.04	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.05	0.00	0.00	0.00	0.00	-0.01
0.00	0.00	0.00	0.00	0.00	-0.01	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.98	0.00	-0.03	-0.05	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.01	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	-0.11	-0.34	0.00	-0.02	0.00	0.00	-0.01	0.00	-0.02	1.00	-0.08	-0.02	0.00	0.00	0.00	0.00	-0.01	0.00	0.00	-0.03	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.20	-0.02	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	1.00	-0.04	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.14	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	1.00	-0.01	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
-0.01	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.01	0.00	0.00	1.00	-0.11	-0.33	-0.03	-0.02	-0.01	0.00	0.00	0.00	-0.02	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.13	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	1.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	1.00	-0.15	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	1.00	-0.06	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.02	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.01	0.00	0.00	0.00	-0.05	0.00	-0.01	-0.01	1.00	-0.14	-0.17	-0.01	0.00	-0.01	-0.02	0.00	0.00	0.00	0.00	0.00	0.00	-0.44	0.00	0.00	-0.01	-0.01	-0.01	-0.01
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.95	-0.04	-0.01	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-1.16	0.00	0.00	-0.01	-0.02	0.00	-0.01
-0.09	-0.11	-0.11	-0.08	-0.02	-0.04	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.01	0.00	-0.01	-0.03	-0.07	0.00	-0.03	-0.02	-0.07	0.97	-0.12	0.00	-0.16	0.00	0.00	-0.01	-0.01	-0.01	-0.01	-0.01	0.00	0.00	0.00	0.00	0.00	0.00	-0.02
-0.01	-0.01	0.00	-0.01	-0.03	-0.01	0.00	0.00	-0.07	0.00	0.00	0.00	0.00	0.00	-0.04	-0.01	-0.01	0.00	-0.02	0.00	-0.01	-0.01	0.00	0.00	1.00	-0.01	-0.01	-0.07	0.00	0.00	0.00	-0.20	-0.01	-0.03	-2.58	-0.01	-0.15	-0.01	0.00	-0.01	-0.02
0.00	0.00	0.00	-0.01	0.00	0.00	-0.01	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.01	-0.01	0.00	0.00	-0.04	-0.01	0.00	0.00	-0.01	1.00	-0.02	0.00	0.00	0.00	0.00	0.00	0.00	-0.02	-0.59	0.00	-0.05	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.01	-0.02	0.00	0.00	0.00	0.00	0.00	-0.02	0.00	-0.01	0.00	1.00	-0.08	-0.01	0.00	0.00	-0.01	0.00	-0.20	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.01	0.00	-0.01	-0.01	-0.02	-0.02	0.00	-0.01	-0.03	-0.02	0.00	-0.01	-0.01	-0.01	-0.01	0.97	-0.15	-0.07	-0.02	-0.02	-0.02	-0.12	0.00	0.00	0.00	-0.01	-0.01	0.00	0.00
0.00	0.00	0.00	0.00	-0.01	0.00	0.00	0.00	-0.01	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.01	0.00	0.00	-0.01	1.00	-0.05	0.00	-0.02	0.00	-0.01	-0.02	0.00	-0.01	0.00	0.00	0.00	-0.03
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.01	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.02	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	1.00	-0.01	-0.01	-0.01	-0.01	-0.01	0.00	-0.06	-0.01	0.00	0.00	-0.01
-0.01	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.01	0.00	0.00	0.00	0.00	0.00	-0.03	-0.01	0.00	0.00	-0.01	0.00	-0.01	0.00	0.00	0.00	0.00	0.00	-0.01	-0.03	0.00	0.00	1.00	0.00	-0.12	0.00	-0.64	-0.01	0.00	-0.01	-0.01	0.00	-0.01
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	1.00	0.00	0.00	-0.11	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	1.00	0.00	-0.10	0.00	0.00	0.00	-0.01	0.00	-0.04
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.03	0.00	0.00	0.00	0.00	1.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.01	-0.01	0.00	0.88	0.00	0.00	0.00	-0.02	0.00	0.00
-0.04	-0.03	-0.02	-0.23	-0.01	-0.02	-0.02	-0.02	-0.02	0.00	0.00	0.00	0.00	0.00	-0.01	-0.01	-0.01	-0.02	-0.01	-0.01	-0.01	-0.01	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.01	0.00	0.00	0.00	-0.02	-10.46	1.00	-0.06	-0.01	-0.01	-0.01	0.00
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.01	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.84	-0.01	0.99	-0.08	0.00	-0.01	0.00
-0.01	-0.01	0.00	0.00	0.00	0.00	0.00	0.00	-0.02	0.00	0.00	0.00	0.00	0.00	-0.01	0.00	0.00	0.00	0.00	0.00	-0.01	0.00	-0.02	0.00	0.00	0.00	-0.01	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-1.82	0.00	-0.01	0.99	-0.05	-0.01	0.00
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.01	0.00	0.00	0.00	0.00	0.00	-0.01	-0.01	-0.01	-0.02	-0.01	-0.01	-0.01	-0.01	0.00	-0.03	-0.01	0.00	-0.01	0.00	0.00	0.00	-0.01	-0.01	-0.07	-0.04	-4.86	-0.01	-0.01	-0.02	0.91	-0.03	-0.06
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	1.00	0.00
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.01	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.01	-1.89	0.00	-0.02	0.00	0.00	-0.04	0.97

# Para el año 2006
matriz_T2006<-mip_matriz_tecnologica(matriz_coeficientes_tecnicos = A2006)
head(matriz_T2006,4)

##           [,1] [,2]      [,3]      [,4] [,5]       [,6]        [,7] [,8] [,9]
## [1,] 0.9995282    0 0.0000000 0.0000000    0  0.0000000  0.00000000    0    0
## [2,] 0.0000000    1 0.0000000 0.0000000    0  0.0000000  0.00000000    0    0
## [3,] 0.0000000    0 0.9846192 0.0000000    0 -0.0170271 -0.04873222    0    0
## [4,] 0.0000000    0 0.0000000 0.9617187    0  0.0000000  0.00000000    0    0
##      [,10]           [,11]         [,12] [,13]      [,14]      [,15]
## [1,]     0  0.000000000000  0.0000000000     0  0.0000000  0.0000000
## [2,]     0  0.000000000000  0.0000000000     0  0.0000000  0.0000000
## [3,]     0 -0.000002709117 -0.0001164731     0 -0.1624212  0.0000000
## [4,]     0  0.000000000000  0.0000000000     0  0.0000000 -0.2385924
##             [,16] [,17] [,18]       [,19]        [,20] [,21] [,22] [,23] [,24]
## [1,] -0.006960395     0     0  0.00000000  0.000000000     0     0     0     0
## [2,]  0.000000000     0     0 -0.03289641 -0.004344639     0     0     0     0
## [3,] -0.055879172     0     0  0.00000000  0.000000000     0     0     0     0
## [4,]  0.000000000     0     0  0.00000000  0.000000000     0     0     0     0
##      [,25] [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35]
## [1,]     0     0     0     0     0     0     0     0     0     0     0
## [2,]     0     0     0     0     0     0     0     0     0     0     0
## [3,]     0     0     0     0     0     0     0     0     0     0     0
## [4,]     0     0     0     0     0     0     0     0     0     0     0
##            [,36] [,37] [,38] [,39] [,40]        [,41]
## [1,]  0.00000000     0     0     0     0  0.000000000
## [2,]  0.00000000     0     0     0     0  0.000000000
## [3,] -0.02576303     0     0     0     0 -0.004543355
## [4,]  0.00000000     0     0     0     0  0.000000000

# Usar kable para mostrar la matriz
kable(matriz_T2006, caption = "Matriz Ejemplo", format = "html", digits = 2, align = 'c', escape = FALSE)

Matriz Ejemplo
1.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.01	0.00	0	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	1.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0	-0.03	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.98	0.00	0.00	-0.02	-0.05	0.00	0.00	0.00	0.00	0.00	0.00	-0.16	0.00	-0.06	0.00	0	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.03	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.96	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.24	0.00	0.00	0	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	-0.05	-0.02	0.99	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.03	-0.01	0	-0.03	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	0.97	0.00	0.00	0.00	0.00	-0.24	-0.26	0.00	0.00	0.00	0.00	0.00	0	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	0.00	0.97	0.00	0.00	0.00	-0.01	0.00	0.00	0.00	0.00	0.00	0.00	0	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.01	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	0.00	0.00	1.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.04	0.00	0.00	0	0.00	0.00	0.00	-0.06	0.00	0.00	0.00	0.00	-0.02	-0.02	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.76	0.00	0.00	0.00	-0.02	0.00	0.00	0.00	0.00	0	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.02	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	-0.01	0.00	0.00	0.00	0.97	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0	0.00	0.00	0.00	0.00	0.00	0.00	-0.01	-0.20	0.00	-0.09	0.00	0.00	0.00	0.00	0.00	-0.02	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	-0.01	0.00	0.00	-0.01	0.00	0.98	0.00	0.00	0.00	0.00	0.00	0.00	0	0.00	0.00	-0.03	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.03	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.95	0.00	-0.01	0.00	0.00	0.00	0	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	1.00	0.00	0.00	0.00	0.00	0	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	-0.02	-0.03	0.00	0.00	0.00	0.00	0.00	0.00	0.85	0.00	-0.02	0.00	0	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.02	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.01	1.00	-0.01	-0.02	0	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	-0.13	-0.33	0.00	-0.01	0.00	0.00	-0.01	0.00	-0.02	0.00	0.94	-0.02	0	0.00	0.00	0.00	0.00	0.00	0.00	-0.02	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.11	-0.01	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.96	0	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.06	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	1	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
-0.01	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.02	0.00	0.00	0	0.94	-0.19	-0.02	-0.01	-0.01	0.00	0.00	0.00	-0.01	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.05	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0	0.00	1.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.01	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0	0.00	0.00	0.89	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0	0.00	0.00	0.00	0.97	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.01	-0.01	0.00	0.00	0.00	-0.01	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.01	-0.01	0.00	0.00	0	0.00	0.00	-0.01	0.00	0.87	-0.14	-0.01	0.00	-0.01	-0.01	0.00	0.00	0.00	-0.01	-0.01	0.00	-0.24	0.00	0.00	-0.01	-0.01	-0.01	0.00
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0	0.00	0.00	0.00	0.00	-0.04	0.97	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.01	0.00	0.00	-0.56	0.00	0.00	-0.02	-0.01	0.00	0.00
-0.09	-0.01	-0.09	-0.07	-0.01	-0.03	-0.01	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.01	0.00	-0.01	0	-0.04	0.00	-0.02	-0.01	-0.04	-0.02	0.92	0.00	-0.11	0.00	0.00	0.00	0.00	-0.02	-0.03	0.00	0.00	0.00	0.00	-0.01	0.00	0.00	-0.01
-0.04	0.00	0.00	-0.02	-0.03	-0.01	-0.01	0.00	-0.04	-0.01	0.00	0.00	0.00	-0.01	-0.12	-0.02	-0.02	0	-0.02	-0.01	-0.01	-0.01	0.00	0.00	0.00	0.99	-0.01	-0.10	-0.01	0.00	0.00	-0.23	-0.03	-0.03	-2.05	-0.01	-0.15	-0.02	0.00	-0.01	-0.01
0.00	0.00	0.00	-0.01	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0	0.00	0.00	-0.02	0.00	0.00	0.00	-0.01	0.00	0.99	0.00	0.00	0.00	0.00	-0.01	-0.01	-0.01	-0.19	0.00	-0.02	-0.01	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.01	-0.02	0	0.00	0.00	0.00	0.00	-0.02	0.00	-0.01	0.00	0.00	0.93	-0.01	0.00	0.00	-0.02	-0.01	-0.16	0.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.01	0.00	0.00	0.00	0.00	0.00	-0.01	0.00	-0.02	0	0.00	-0.01	-0.02	-0.01	0.00	0.00	0.00	-0.01	-0.01	-0.02	0.89	-0.03	-0.01	-0.03	-0.04	-0.09	0.00	0.00	0.00	-0.01	-0.01	0.00	0.00
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.99	0.00	-0.04	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.01
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0	0.00	-0.01	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.99	-0.02	-0.01	0.00	0.00	0.00	-0.03	-0.02	0.00	0.00	-0.01
-0.01	0.00	0.00	0.00	0.00	0.00	-0.01	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.09	-0.01	-0.01	0	-0.02	-0.01	-0.01	0.00	0.00	-0.01	0.00	0.00	-0.02	-0.05	-0.01	0.00	0.00	1.00	-0.45	0.00	-0.73	-0.01	0.00	-0.03	0.00	-0.01	-0.01
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	1.00	0.00	-0.05	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	1.00	-0.08	0.00	0.00	0.00	0.00	0.00	-0.02
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.03	0.00	0.00	0.00	0.00	0.00	1.00	0.00	0.00	0.00	0.00	0.00	0.00
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.02	-0.02	0.00	-0.05	1.00	0.00	-0.01	-0.01	0.00	0.00
-0.06	0.00	-0.02	-0.44	-0.01	-0.02	-0.02	-0.02	-0.01	0.00	0.00	0.00	0.00	0.00	-0.02	-0.01	-0.01	0	-0.01	-0.01	-0.01	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.05	-7.21	0.00	0.94	-0.02	-0.01	0.00	-0.01
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.20	0.00	0.00	0.82	-0.02	0.00	0.00
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.01	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.01	0.00	0.00	-0.22	0.00	0.00	-0.04	0.97	0.00	-0.11
0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.01	0.00	0.00	0.00	0.00	0.00	-0.03	-0.01	-0.01	0	-0.01	-0.02	-0.02	0.00	0.00	-0.05	-0.01	0.00	-0.01	0.00	0.00	0.00	-0.01	-0.03	-0.14	-0.05	-3.93	-0.01	-0.01	-0.05	-0.05	0.95	-0.03
0.00	0.00	0.00	0.00	0.00	-0.01	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.01	0.00	0.00	0	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	-0.01	-1.41	0.00	-0.01	-0.01	0.00	-0.04	0.99

Matriz de Leontief “1990 y 2006”

#Para el año 1990
Leontief_1990<-mip_matriz_leontief(matriz_tecnologica = matriz_T1990)
head(Leontief_1990,n = 4)

##             [,1]          [,2]          [,3]          [,4]           [,5]
## [1,] 1.000016995 0.00001832023 0.00001475006 0.00006976799 0.000007088685
## [2,] 0.002822331 1.00274061824 0.01519879734 0.01487980732 0.001041762883
## [3,] 0.001460364 0.00252489952 1.00298310170 0.04216529113 0.000991248810
## [4,] 0.002039731 0.03557821587 0.04445116258 1.01041266531 0.007829184976
##               [,6]          [,7]           [,8]          [,9]          [,10]
## [1,] 0.00002202034 0.00003837024 0.000004773383 0.00008929172 0.000004236436
## [2,] 0.01337329914 0.04251594803 0.001037364251 0.00211400493 0.000205205495
## [3,] 0.04365044786 0.13209925871 0.000270221862 0.00818873013 0.000225418454
## [4,] 0.00322072894 0.00819787680 0.000131161299 0.00093342116 0.000262450880
##               [,11]         [,12]           [,13]         [,14]         [,15]
## [1,] 0.000007864457 0.00001033078 0.0000008964961 0.00001511778 0.00007667556
## [2,] 0.001045035330 0.00139564702 0.0000979093434 0.25567115363 0.00768691011
## [3,] 0.002836030232 0.00372996544 0.0009718368018 0.00788957476 0.38345673710
## [4,] 0.004040655704 0.00438919046 0.0029488838271 0.01036431384 0.01885142050
##             [,16]         [,17]         [,18]       [,19]       [,20]
## [1,] 0.0000472613 0.00003966203 0.00005211865 0.091364138 0.015002093
## [2,] 0.0983831598 0.01901145245 0.00416566657 0.002445141 0.003294667
## [3,] 0.0319359255 0.01003252287 0.00636026270 0.003019738 0.003108288
## [4,] 0.0342258129 0.01055552252 0.10424214897 0.043277750 0.037517655
##            [,21]        [,22]         [,23]         [,24]         [,25]
## [1,] 0.002239687 0.0001534217 0.00004031158 0.00004621064 0.00002986296
## [2,] 0.002394268 0.0052103709 0.00173122588 0.00262917479 0.00170286379
## [3,] 0.002084148 0.0158420197 0.00311777628 0.00360502724 0.01083309240
## [4,] 0.008705811 0.0061315579 0.00238753893 0.00151294731 0.00142289242
##              [,26]         [,27]         [,28]         [,29]         [,30]
## [1,] 0.00003225493 0.00007859386 0.00003642692 0.00002172376 0.00004147753
## [2,] 0.00080978690 0.00235456338 0.00171647431 0.00091432548 0.00122458525
## [3,] 0.00303028072 0.00446305880 0.00456313894 0.00133704743 0.00127151814
## [4,] 0.00035751777 0.00283497679 0.00080922145 0.00037153205 0.00048668901
##              [,31]         [,32]        [,33]         [,34]       [,35]
## [1,] 0.00001725223 0.00008488757 0.0004845071 0.00008406671 0.005758801
## [2,] 0.00069069899 0.00996088264 0.0097448563 0.00336039694 0.883383167
## [3,] 0.00053229379 0.00657586598 0.0046005089 0.00319097366 0.373154814
## [4,] 0.00172185633 0.00219565244 0.0022989247 0.00132512388 0.156349240
##             [,36]         [,37]         [,38]        [,39]         [,40]
## [1,] 0.0002705782 0.00007961621 0.00005555265 0.0002602103 0.00002649925
## [2,] 0.0591283334 0.00573864404 0.00526582060 0.0213336530 0.00211266919
## [3,] 0.0133948262 0.00350656092 0.00251939087 0.0105874767 0.00111850978
## [4,] 0.0073351743 0.00114895374 0.00119522883 0.0043198937 0.00053315833
##             [,41]
## [1,] 0.0002630334
## [2,] 0.0152661994
## [3,] 0.0051533253
## [4,] 0.0045782235

# Tabla Rasmussen para 1990
tabla1990<-mip_tabla_rasmussen(Leontief_1990) %>% print()

##    sector        pd         sd      clasificacion
## 1       1 0.4073780  0.4640866        Sector Isla
## 2       2 0.9211716  0.4799408        Sector Isla
## 3       3 0.7886315  0.4743523        Sector Isla
## 4       4 0.5804252  0.5815688        Sector Isla
## 5       5 0.8026386  0.4137988        Sector Isla
## 6       6 0.5484198  0.5229184        Sector Isla
## 7       7 0.5517426  0.6578392        Sector Isla
## 8       8 0.5081093  0.3760181        Sector Isla
## 9       9 0.7743665  0.4928028        Sector Isla
## 10     10 0.7438844  0.3829394        Sector Isla
## 11     11 0.4753361  0.6013041        Sector Isla
## 12     12 0.3960443  0.6020453        Sector Isla
## 13     13 0.8708867  0.3807448        Sector Isla
## 14     14 0.5239815  0.6074921        Sector Isla
## 15     15 1.0577706  0.6421779    Sector Impulsor
## 16     16 1.2963048  0.5470338    Sector Impulsor
## 17     17 0.3896545  0.5008094        Sector Isla
## 18     18 0.7458434  0.5374569        Sector Isla
## 19     19 0.3966796  0.5639672        Sector Isla
## 20     20 0.4386216  0.6164393        Sector Isla
## 21     21 0.4651084  0.5886462        Sector Isla
## 22     22 1.2230343  0.5266551    Sector Impulsor
## 23     23 1.2698015  0.5539839    Sector Impulsor
## 24     24 1.3837096  0.5454449    Sector Impulsor
## 25     25 2.3803880  0.5055704    Sector Impulsor
## 26     26 0.8816180  0.5490069        Sector Isla
## 27     27 0.6071360  0.5295628        Sector Isla
## 28     28 0.8007163  0.5342742        Sector Isla
## 29     29 0.5361012  0.4922131        Sector Isla
## 30     30 0.5371458  0.4639269        Sector Isla
## 31     31 0.9902137  0.4100248        Sector Isla
## 32     32 0.4614537  0.7207236        Sector Isla
## 33     33 0.5120130  0.6890521        Sector Isla
## 34     34 0.3838923  0.6582776        Sector Isla
## 35     35 0.5358975 19.0303247 Sector Estrategico
## 36     36 6.4565654  0.5830180    Sector Impulsor
## 37     37 1.0445382  0.5982490    Sector Impulsor
## 38     38 1.6962454  0.5560409    Sector Impulsor
## 39     39 3.7219641  0.9175136    Sector Impulsor
## 40     40 0.3647051  0.4574183        Sector Isla
## 41     41 1.5298621  0.6443371    Sector Impulsor

#Para el año 2006
Leontief_2006<-mip_matriz_leontief(matriz_tecnologica = matriz_T2006)
head(Leontief_2006,n = 4)

##                [,1]            [,2]           [,3]          [,4]           [,5]
## [1,] 1.000484009858 0.0000010950830 0.000011873987 0.00001080351 0.000001546251
## [2,] 0.000482282783 1.0000062461514 0.000052792836 0.00003043432 0.000022180586
## [3,] 0.000140402889 0.0000127198852 1.015744219797 0.00019105794 0.000029748185
## [4,] 0.000006101966 0.0000005418368 0.000005400592 1.03981456214 0.000001436693
##               [,6]          [,7]            [,8]          [,9]           [,10]
## [1,] 0.00103374522 0.00252753950 0.0000001344039 0.00005709858 0.0000004409393
## [2,] 0.00004079086 0.00004335019 0.0000005282561 0.00001806658 0.0000555728588
## [3,] 0.03191373527 0.07980787697 0.0000039017882 0.00088598073 0.0000244912459
## [4,] 0.00106289989 0.00073871317 0.0000002084464 0.00002388120 0.0000011845548
##              [,11]         [,12]          [,13]         [,14]          [,15]
## [1,] 0.00029577579 0.00033974677 0.000010050534 0.00014999011 0.000006580779
## [2,] 0.00001148598 0.00001391379 0.000000890339 0.00008199149 0.000561282353
## [3,] 0.00897636530 0.00947175271 0.000097523745 0.19525494518 0.000250751303
## [4,] 0.00027589555 0.00050180852 0.000002943422 0.00195731404 0.248100933682
##              [,16]         [,17] [,18]          [,19]          [,20]
## [1,] 0.00742120524 0.00013300133     0 0.000006181578 0.000002268890
## [2,] 0.00004705581 0.00001946489     0 0.035120539349 0.011196493927
## [3,] 0.06528116245 0.00124747429     0 0.000092346158 0.000095668582
## [4,] 0.00197530235 0.00441402042     0 0.000003919285 0.000003968055
##              [,21]          [,22]          [,23]          [,24]         [,25]
## [1,] 0.00001415935 0.000022622862 0.000010562204 0.000004522926 0.00012796041
## [2,] 0.00063528673 0.000236118326 0.000261343003 0.000053519511 0.00010485969
## [3,] 0.00039547566 0.000212234260 0.000178114900 0.000096206447 0.00126713533
## [4,] 0.00001420111 0.000006762595 0.000008458475 0.000004151422 0.00005494582
##                [,26]          [,27]          [,28]         [,29]          [,30]
## [1,] 0.0000002796106 0.000014988594 0.000001847944 0.00003420785 0.000010040821
## [2,] 0.0000119847586 0.000438066297 0.000021142894 0.00009480160 0.000022993723
## [3,] 0.0000120022892 0.000184796003 0.000087462669 0.00075668330 0.000116289528
## [4,] 0.0000005707357 0.000007900024 0.000003677405 0.00003206015 0.000004000451
##               [,31]         [,32]         [,33]          [,34]        [,35]
## [1,] 0.000004922921 0.00001074485 0.00001232843 0.000004726151 0.0008940134
## [2,] 0.000053783232 0.00006615302 0.00017888175 0.000023962092 0.0024399186
## [3,] 0.000144480611 0.00076141036 0.00104247177 0.000164105363 0.0203159977
## [4,] 0.000005029754 0.00002544875 0.00003553883 0.000007580445 0.0008680058
##              [,36]          [,37]          [,38]         [,39]          [,40]
## [1,] 0.00011773602 0.000001450845 0.000006545757 0.00001727103 0.000001486887
## [2,] 0.00001659903 0.000020720558 0.000153501432 0.00006062634 0.000009175735
## [3,] 0.03165437995 0.000148529142 0.000561289204 0.00060733433 0.000281800521
## [4,] 0.00101000938 0.000008668130 0.000023663058 0.00002285547 0.000014684759
##              [,41]
## [1,] 0.00002372046
## [2,] 0.00010594535
## [3,] 0.00583765664
## [4,] 0.00031129545

# Tabla Rasmussen para 2006
tabla2006<-mip_tabla_rasmussen(Leontief_2006) %>% print()

##    sector        pd         sd      clasificacion
## 1       1 0.5266371  0.6742102        Sector Isla
## 2       2 0.5468883  0.5299542        Sector Isla
## 3       3 0.7658552  0.6415055        Sector Isla
## 4       4 0.6759963  0.9598921        Sector Isla
## 5       5 0.6467097  0.5630764        Sector Isla
## 6       6 0.8433116  0.7275592        Sector Isla
## 7       7 0.5510189  0.8757175        Sector Isla
## 8       8 0.6216461  0.5334481        Sector Isla
## 9       9 0.7062793  0.7790059        Sector Isla
## 10     10 1.3339695  0.5564918    Sector Impulsor
## 11     11 0.5974393  0.7277986        Sector Isla
## 12     12 0.5599095  0.7645337        Sector Isla
## 13     13 0.5205150  0.5406925        Sector Isla
## 14     14 0.7042717  0.7928421        Sector Isla
## 15     15 0.5476486  1.0085354 Sector Estrategico
## 16     16 0.9963777  0.6984426        Sector Isla
## 17     17 0.5891340  0.6633745        Sector Isla
## 18     18 0.5197275  0.5194535        Sector Isla
## 19     19 0.7641142  0.6759362        Sector Isla
## 20     20 0.5289720  0.7124806        Sector Isla
## 21     21 0.5869791  0.7247208        Sector Isla
## 22     22 0.5751913  0.6113676        Sector Isla
## 23     23 1.0484997  0.7445995    Sector Impulsor
## 24     24 0.9808336  0.7083989        Sector Isla
## 25     25 1.1170017  0.6300139    Sector Impulsor
## 26     26 3.2965709  0.6444126    Sector Impulsor
## 27     27 0.8121867  0.6631067        Sector Isla
## 28     28 0.7841251  0.7772573        Sector Isla
## 29     29 0.9306187  1.0340246 Sector Estrategico
## 30     30 0.6495298  0.5760867        Sector Isla
## 31     31 0.7959562  0.5654301        Sector Isla
## 32     32 1.4531322  0.8251255    Sector Impulsor
## 33     33 0.5619995  1.1035588 Sector Estrategico
## 34     34 0.6165996  0.8700713        Sector Isla
## 35     35 0.5516892 12.3434144 Sector Estrategico
## 36     36 0.6218298  0.6749219        Sector Isla
## 37     37 5.3517201  0.7258272    Sector Impulsor
## 38     38 0.8262533  0.8751212        Sector Isla
## 39     39 0.9175119  0.6423195        Sector Isla
## 40     40 3.3777885  0.6086018    Sector Impulsor
## 41     41 1.5975616  0.7066691    Sector Impulsor

A) Realización del cálculo de los siguientes Multiplicadores:

A1 Multiplicadores Expansión de la demanda (me) para la MIP 1990 y para la MIP 2006.

library(dplyr)
library(tidyr)
library(kableExtra)
# Multiplicador expansión de la demanda 1990
me_1990 <- round(colSums(Leontief_1990), 3)

# Multiplicador expansión de la demanda 2006
me_2006 <- round(colSums(Leontief_2006), 3)

tabla_unida_multiplicador_demanda <- cbind(1:41, me_1990, me_2006)
tabla_unida_multiplicador_demanda %>% head(41) %>%
 kable(caption = "Multiplicador Expansión de la Demanda apartir de la Matriz Insumo Producto de El Salvador 1990 y 2006", digits = 4) %>%
  kable_classic(html_font = "Times New Roman", font_size = 14) %>%
  add_footnote(label = "Elaboración propia con base de datos del BCR", notation = "symbol") %>%
  kable_styling()

Multiplicador Expansión de la Demanda apartir de la Matriz Insumo Producto de El Salvador 1990 y 2006
	me_1990	me_2006
1	1.272	1.298
2	1.316	1.020
3	1.301	1.235
4	1.595	1.848
5	1.135	1.084
6	1.434	1.401
7	1.804	1.686
8	1.031	1.027
9	1.351	1.500
10	1.050	1.071
11	1.649	1.401
12	1.651	1.472
13	1.044	1.041
14	1.666	1.526
15	1.761	1.942
16	1.500	1.345
17	1.373	1.277
18	1.474	1.000
19	1.546	1.301
20	1.690	1.372
21	1.614	1.395
22	1.444	1.177
23	1.519	1.433
24	1.496	1.364
25	1.386	1.213
26	1.505	1.241
27	1.452	1.277
28	1.465	1.496
29	1.350	1.991
30	1.272	1.109
31	1.124	1.089
32	1.976	1.588
33	1.889	2.124
34	1.805	1.675
35	52.180	23.762
36	1.599	1.299
37	1.640	1.397
38	1.525	1.685
39	2.516	1.237
40	1.254	1.172
41	1.767	1.360
^* Elaboración propia con base de datos del BCR

A2 Multiplicadores de la producción (mp) para la MIP 1990 y para la MIP 2006.

# Multiplicadores de la producción para 1990
mp_1990 <- round(rowSums(Leontief_1990),3)

# Multiplicadores de la producción para 2006
mp_2006 <- round(rowSums(Leontief_2006),3)

tabla_unida_multiplicador_produccion <- cbind(1:41,mp_1990, mp_2006) 
 tabla_unida_multiplicador_produccion %>% head(41) %>% 
kable(caption = "Multiplicador de la Producción apartir de la Matriz Insumo Producto de El Salvador 1990 y 2006", digits = 4) %>%
  kable_classic(html_font = "Times New Roman", font_size = 14) %>%
  add_footnote(label = "Elaboración propia con base de datos del BCR", notation = "symbol") %>%
  kable_styling()

Multiplicador de la Producción apartir de la Matriz Insumo Producto de El Salvador 1990 y 2006
	mp_1990	mp_2006
1	1.117	1.014
2	2.526	1.053
3	2.162	1.474
4	1.591	1.301
5	2.201	1.245
6	1.504	1.623
7	1.513	1.061
8	1.393	1.197
9	2.123	1.360
10	2.040	2.568
11	1.303	1.150
12	1.086	1.078
13	2.388	1.002
14	1.437	1.356
15	2.900	1.054
16	3.554	1.918
17	1.068	1.134
18	2.045	1.001
19	1.088	1.471
20	1.203	1.018
21	1.275	1.130
22	3.353	1.107
23	3.482	2.018
24	3.794	1.888
25	6.527	2.150
26	2.417	6.346
27	1.665	1.564
28	2.196	1.510
29	1.470	1.792
30	1.473	1.250
31	2.715	1.532
32	1.265	2.797
33	1.404	1.082
34	1.053	1.187
35	1.469	1.062
36	17.704	1.197
37	2.864	10.303
38	4.651	1.591
39	10.205	1.766
40	1.000	6.503
41	4.195	3.075
^* Elaboración propia con base de datos del BCR

A3 Tasa de cambio para ambos multiplicadores (por ejemplo, para me: me2006/me1990

# Tasas de cambio  para ambos multiplicadores
tasa_me <- round(((me_2006/me_1990)-1),2)

tasa_mp <- round(((mp_2006/mp_1990)-1),2)

tabla_tasas <- cbind(1:41,tasa_me, tasa_mp) 
tabla_tasas %>% head(41) %>%
 kable(caption = "Tasas de cambio  para ambos multiplicadores", digits = 4) %>%
  kable_classic(html_font = "Times New Roman", font_size = 14) %>%
  add_footnote(label = "Elaboración propia con base de datos del BCR", notation = "symbol") %>%
  kable_styling()

Tasas de cambio para ambos multiplicadores
	tasa_me	tasa_mp
1	0.02	-0.09
2	-0.22	-0.58
3	-0.05	-0.32
4	0.16	-0.18
5	-0.04	-0.43
6	-0.02	0.08
7	-0.07	-0.30
8	0.00	-0.14
9	0.11	-0.36
10	0.02	0.26
11	-0.15	-0.12
12	-0.11	-0.01
13	0.00	-0.58
14	-0.08	-0.06
15	0.10	-0.64
16	-0.10	-0.46
17	-0.07	0.06
18	-0.32	-0.51
19	-0.16	0.35
20	-0.19	-0.15
21	-0.14	-0.11
22	-0.18	-0.67
23	-0.06	-0.42
24	-0.09	-0.50
25	-0.12	-0.67
26	-0.18	1.63
27	-0.12	-0.06
28	0.02	-0.31
29	0.47	0.22
30	-0.13	-0.15
31	-0.03	-0.44
32	-0.20	1.21
33	0.12	-0.23
34	-0.07	0.13
35	-0.54	-0.28
36	-0.19	-0.93
37	-0.15	2.60
38	0.10	-0.66
39	-0.51	-0.83
40	-0.07	5.50
41	-0.23	-0.27
^* Elaboración propia con base de datos del BCR

A4 Presentación de los resultados en una tabla que incluya los nombres para todos los sectores.

tabla_total_datos<- data.frame(me_1990=me_1990,me_2006=me_2006, mp_1990=mp_1990, mp_2006=mp_2006) %>% 
  mutate(dif_me=round((me_2006/me_1990-1)*100,2),
         dif_mp=round((mp_2006/mp_1990-1)*100,2)) %>%
  mutate(sector=row_number()) %>% 
         select(sector, everything())
tabla_total_datos %>%
  kable(caption = "Presentación de los resultados totales en una tabla para todos los sectores", digits = 4) %>%
  kable_classic(html_font = "Times New Roman", font_size = 14) %>%
  add_footnote(label = "Elaboración propia con base de datos del BCR", notation = "symbol") %>%
  kable_styling()

Presentación de los resultados totales en una tabla para todos los sectores
sector	me_1990	me_2006	mp_1990	mp_2006	dif_me	dif_mp
1	1.272	1.298	1.117	1.014	2.04	-9.22
2	1.316	1.020	2.526	1.053	-22.49	-58.31
3	1.301	1.235	2.162	1.474	-5.07	-31.82
4	1.595	1.848	1.591	1.301	15.86	-18.23
5	1.135	1.084	2.201	1.245	-4.49	-43.43
6	1.434	1.401	1.504	1.623	-2.30	7.91
7	1.804	1.686	1.513	1.061	-6.54	-29.87
8	1.031	1.027	1.393	1.197	-0.39	-14.07
9	1.351	1.500	2.123	1.360	11.03	-35.94
10	1.050	1.071	2.040	2.568	2.00	25.88
11	1.649	1.401	1.303	1.150	-15.04	-11.74
12	1.651	1.472	1.086	1.078	-10.84	-0.74
13	1.044	1.041	2.388	1.002	-0.29	-58.04
14	1.666	1.526	1.437	1.356	-8.40	-5.64
15	1.761	1.942	2.900	1.054	10.28	-63.66
16	1.500	1.345	3.554	1.918	-10.33	-46.03
17	1.373	1.277	1.068	1.134	-6.99	6.18
18	1.474	1.000	2.045	1.001	-32.16	-51.05
19	1.546	1.301	1.088	1.471	-15.85	35.20
20	1.690	1.372	1.203	1.018	-18.82	-15.38
21	1.614	1.395	1.275	1.130	-13.57	-11.37
22	1.444	1.177	3.353	1.107	-18.49	-66.98
23	1.519	1.433	3.482	2.018	-5.66	-42.04
24	1.496	1.364	3.794	1.888	-8.82	-50.24
25	1.386	1.213	6.527	2.150	-12.48	-67.06
26	1.505	1.241	2.417	6.346	-17.54	162.56
27	1.452	1.277	1.665	1.564	-12.05	-6.07
28	1.465	1.496	2.196	1.510	2.12	-31.24
29	1.350	1.991	1.470	1.792	47.48	21.90
30	1.272	1.109	1.473	1.250	-12.81	-15.14
31	1.124	1.089	2.715	1.532	-3.11	-43.57
32	1.976	1.588	1.265	2.797	-19.64	121.11
33	1.889	2.124	1.404	1.082	12.44	-22.93
34	1.805	1.675	1.053	1.187	-7.20	12.73
35	52.180	23.762	1.469	1.062	-54.46	-27.71
36	1.599	1.299	17.704	1.197	-18.76	-93.24
37	1.640	1.397	2.864	10.303	-14.82	259.74
38	1.525	1.685	4.651	1.591	10.49	-65.79
39	2.516	1.237	10.205	1.766	-50.83	-82.69
40	1.254	1.172	1.000	6.503	-6.54	550.30
41	1.767	1.360	4.195	3.075	-23.03	-26.70
^* Elaboración propia con base de datos del BCR

B) Análisis de Rasmussen para las MIP 1990 y 2006

B1 Análisis de Rasmussen para 1990

analisis_rasmussen_1990<-mip_tabla_rasmussen(Leontief_1990) 

summatoria_rasmussen_1990 <- analisis_rasmussen_1990 %>% 
  group_by(clasificacion) %>% summarise(total=n()) %>% mutate(porcentaje=round(prop.table(total)*100,2))

summatoria_rasmussen_1990 %>%
  kable(caption = "Análisis de Rasmussen para la MIP de 1990 de El Salvador", digits = 4) %>%
  kable_classic(html_font = "Times New Roman", font_size = 14) %>%
  add_footnote(label = "Elaboración propia con base de datos del BCR", notation = "symbol") %>%
  kable_styling()

Análisis de Rasmussen para la MIP de 1990 de El Salvador
clasificacion	total	porcentaje
Sector Estrategico	1	2.44
Sector Impulsor	11	26.83
Sector Isla	29	70.73
^* Elaboración propia con base de datos del BCR

B2 Análisis de Rasmussen para 2006

analisis_rasmussen_2006<-mip_tabla_rasmussen(Leontief_2006) 

summatoria_rasmussen_2006 <- analisis_rasmussen_2006 %>% 
  group_by(clasificacion) %>% summarise(total=n()) %>% mutate(porcentaje=round(prop.table(total)*100,2))

summatoria_rasmussen_2006 %>%
  kable(caption = "Análisis de Rasmussen para la MIP de 2006 de El Salvador", digits = 4) %>%
  kable_classic(html_font = "Times New Roman", font_size = 14) %>%
  add_footnote(label = "Elaboración propia con base de datos del BCR", notation = "symbol") %>%
  kable_styling()

Análisis de Rasmussen para la MIP de 2006 de El Salvador
clasificacion	total	porcentaje
Sector Estrategico	4	9.76
Sector Impulsor	8	19.51
Sector Isla	29	70.73
^* Elaboración propia con base de datos del BCR

C) Tabla comparativa con los resultados porcentuales y variaciones porcentuales por tipo de sectores entre 1990 y 2006.

tabla_comparativa<-left_join(summatoria_rasmussen_1990, summatoria_rasmussen_2006, by="clasificacion", suffix=c("_1990","_2006"))

tabla_comparativa %>% 
  mutate(dif_variacion_porcentual= round((porcentaje_2006/porcentaje_1990-1)*100,2)) %>% 
  kable(caption="Tabla comparativa",
        align = "c",
        digits = 3) %>% 
  kable_material(html_font = "sans-serif") %>% 
  kable_styling(bootstrap_options = c("striped", "hover"))

Tabla comparativa
clasificacion	total_1990	porcentaje_1990	total_2006	porcentaje_2006	dif_variacion_porcentual
Sector Estrategico	1	2.44	4	9.76	300.00
Sector Impulsor	11	26.83	8	19.51	-27.28
Sector Isla	29	70.73	29	70.73	0.00