1. Realice el cálculo de lo siguiente (de ser necesario haga las agregaciones de servicios propuestas en la clase):
a. Multiplicadores Expansión de la demanda (me) para la MIP 1990 y para la MIP 2006.
importacion de datos 1990
# Consumo intermedio
library(readxl)
library(dplyr)
mip1990_ci<- read_excel("C:/Users/USUARIO/Desktop/Metodos/Mip_90_2006.xls",
sheet = "MIP 1990", range = "I14:BB59",
col_names = FALSE)
names(mip1990_ci)<-as.character(1:46)
print(mip1990_ci)
## # A tibble: 46 × 46
## `1` `2` `3` `4` `5` `6` `7` `8` `9` `10` `11`
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 127. 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 3753. 0 0 1901. 7.00e3 0 0 0 5.26e-1
## 4 0 0 0 1678. 0 0 0 0 0 0 0
## 5 0 1095. 12618. 298. 1.83e3 0 0 0 0 0 6.84e+0
## 6 0 0 0 0 0 7085. 5.53e0 0 0 0 7.21e+4
## 7 0 0 0 0 0 0 6.77e3 0 9.21e0 0 4.48e+3
## 8 584. 20.5 884. 90.7 1.63e2 31.7 0 16.6 0 0 1.27e+2
## 9 0 0 0 0 0 0 0 0 1.74e3 0 0
## 10 1.05 2.63 33.3 19.6 6.18e0 984. 1.18e0 0 1.84e0 2010. 2.5 e+0
## # ℹ 36 more rows
## # ℹ 35 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>, …
#Demanda Final
mip1990_h <- read_excel("C:/Users/USUARIO/Desktop/Metodos/Mip_90_2006.xls",
sheet = "MIP 1990", range = "bd14:be59",
col_names = FALSE)
names(mip1990_h)<-as.character(1:2)
print(mip1990_h)
## # 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
#Vector de producción
mip1990_X <- read_excel("C:/Users/USUARIO/Desktop/Metodos/Mip_90_2006.xls",
sheet = "MIP 1990", range = "bi14:bi59",
col_names = FALSE)
names(mip1990_X)<-c("X")
library(dplyr)
servicios_row<-colSums(mip1990_ci[41:46,])
temporal<-rbind(mip1990_ci[1:40,],servicios_row)
servicios_col<-rowSums(temporal[,41:46])
mip1990_ci_corregida<-cbind(temporal[,1:40],servicios_col)
names(mip1990_ci_corregida)<-as.character(1:41)
X<-rbind(mip1990_X[1:40,],colSums(mip1990_X[41:46,]))
#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
}
calcular los multiplicadores de la expansion de la demanda de 1990.
A<-mip_coeficientes_tecnicos(as.matrix(mip1990_ci_corregida),as.matrix(X))[[1]]
matriz_T<-mip_matriz_tecnologica(matriz_coeficientes_tecnicos = A)
L_1990<-mip_matriz_leontief(matriz_tecnologica = matriz_T)
# calcular los multiplicadores de la expansion de la demanda
me1990<-colSums(L_1990)
print(me1990)
## [1] 1.255178 1.267747 1.261599 1.568111 1.124569 1.445002 1.768326
## [8] 1.029765 1.383834 1.047445 1.834384 1.788561 1.059243 1.799649
## [15] 1.840519 1.553178 1.365915 1.384794 1.505731 1.642505 1.578688
## [22] 1.361625 1.665210 1.447935 1.327641 1.408787 1.395278 1.459492
## [29] 2.366807 1.299373 1.109323 1.451591 1.392119 1.826914 37.440804
## [36] 1.636206 1.517817 1.258089 1.353294 1.170121 1.301949
importacion de datos de 2006
#Consumo Intermedio
mip2006_ci <- read_excel("C:/Users/USUARIO/Desktop/Metodos/Mip_90_2006.xls",
sheet = "MIP 2006", range = "i15:bb60",
col_names = FALSE)
names(mip2006_ci)<-as.character(1:46)
print(mip2006_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
mip2006_h <- read_excel("C:/Users/USUARIO/Desktop/Metodos/Mip_90_2006.xls",
sheet = "MIP 2006", range = "bd15:be60",
col_names = FALSE)
names(mip2006_h)<-as.character(1:2)
print(mip2006_h)
## # 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
mip2006_X <- read_excel("C:/Users/USUARIO/Desktop/Metodos/Mip_90_2006.xls",
sheet = "MIP 2006", range = "bi15:bi60",
col_names = FALSE)
names(mip2006_X)<-c("X")
library(dplyr)
servicios_row<-colSums(mip2006_ci[41:46,])
temporal<-rbind(mip2006_ci[1:40,],servicios_row)
servicios_col<-rowSums(temporal[,41:46])
mip2006_ci_corregida<-cbind(temporal[,1:40],servicios_col)
names(mip2006_ci_corregida)<-as.character(1:41)
X<-rbind(mip2006_X[1:40,],colSums(mip2006_X[41:46,]))
calcular los multiplicadores de la expansion de la demanda de 2006
source("C:/Users/USUARIO/Desktop/Metodos/mip.R")
A<-mip_coeficientes_tecnicos(as.matrix(mip2006_ci_corregida),as.matrix(X))[[1]]
matriz_T<-mip_matriz_tecnologica(matriz_coeficientes_tecnicos = A)
L_2006<-mip_matriz_leontief(matriz_tecnologica = matriz_T)
# calcular los multiplicadores de la expansion de la demanda
me2006<-colSums(L_2006)
print(me2006)
## [1] 1.297922 1.020215 1.234962 1.847889 1.083978 1.400624 1.685844
## [8] 1.026941 1.499664 1.071302 1.401085 1.471804 1.040887 1.526300
## [15] 1.941532 1.344572 1.277062 1.000000 1.301245 1.371596 1.395160
## [22] 1.176944 1.433429 1.363739 1.212840 1.240559 1.276547 1.496298
## [29] 1.990601 1.109025 1.088510 1.588449 2.124461 1.674974 23.762309
## [36] 1.299292 1.397290 1.684696 1.236529 1.171619 1.360409
b. Multiplicadores de la producción (mp) para la MIP 1990 y para la MIP 2006
mp 1990
#Calcular los multiplicadores de la produccion
mp_1990 <- rowSums(L_1990)
print(mp_1990)
## [1] 1.105885 1.191285 1.750077 1.511243 1.389578 2.181171 1.119400
## [8] 1.246299 1.083152 3.671425 1.223539 1.133089 1.006271 1.592563
## [15] 1.194635 2.224389 1.253500 1.015813 1.928648 1.030379 1.189543
## [22] 1.146679 2.696695 2.739024 3.150917 6.985378 2.603476 1.606020
## [29] 2.197902 1.495203 2.066329 2.185642 1.177725 1.239212 1.067583
## [36] 1.291981 13.816987 2.262777 3.717322 7.533336 3.673047
mp 2006.
#Calcular los multiplicadores de la produccion
mp_2006 <- rowSums(L_2006)
print(mp_2006)
## [1] 1.013829 1.052815 1.474348 1.301361 1.244981 1.623459 1.060766
## [8] 1.196731 1.359658 2.568025 1.150131 1.077882 1.002044 1.355794
## [15] 1.054278 1.918127 1.134142 1.000527 1.470996 1.018324 1.129994
## [22] 1.107301 2.018467 1.888203 2.150340 6.346229 1.563541 1.509519
## [29] 1.791534 1.250410 1.532295 2.797425 1.081905 1.187016 1.062057
## [36] 1.197085 10.302597 1.590620 1.766302 6.502581 3.075466
c. Tasa de cambio para ambos multiplicadores (por ejemplo, para me: me2006/me1990-1)
Tasa de cambio me
tasa_de_cambio_me<-me2006/me1990-1
print(tasa_de_cambio_me)
## [1] 0.034053910 -0.195253394 -0.021113105 0.178416600 -0.036093947
## [6] -0.030710963 -0.046644285 -0.002742203 0.083702802 0.022777299
## [11] -0.236209344 -0.177101522 -0.017328929 -0.151889740 0.054882473
## [16] -0.134309142 -0.065050033 -0.277871073 -0.135805402 -0.164936051
## [21] -0.116253539 -0.135633166 -0.139190604 -0.058148876 -0.086470059
## [26] -0.119413382 -0.085095253 0.025218482 -0.158950892 -0.146492609
## [31] -0.018762181 0.094281625 0.526062912 -0.083167223 -0.365336575
## [36] -0.205911755 -0.079408189 0.339091521 -0.086281707 0.001280605
## [41] 0.044901570
Tasa de cambio mp
tasa_de_cambio_mp<-mp_2006/mp_1990-1
print(tasa_de_cambio_mp)
## [1] -0.083241979 -0.116236057 -0.157552595 -0.138880926 -0.104058154
## [6] -0.255693743 -0.052379626 -0.039771981 0.255279837 -0.300537226
## [11] -0.059997035 -0.048722602 -0.004201206 -0.148671768 -0.117488893
## [16] -0.137683601 -0.095219718 -0.015047927 -0.237291624 -0.011699801
## [21] -0.050060911 -0.034340801 -0.251503562 -0.310629203 -0.317550983
## [26] -0.091498080 -0.399441022 -0.060086690 -0.184889040 -0.163718910
## [31] -0.258445453 0.279910220 -0.081359642 -0.042120529 -0.005176405
## [36] -0.073449996 -0.254352824 -0.297049512 -0.524845471 -0.136825786
## [41] -0.162693486
d. Presente los resultados en una tabla que incluya los nombres para todos los sectores.
library(dplyr)
library(dplyr)
library(kableExtra)
tabla <- data.frame(me1990=me1990, me2006=me2006, mp_1990=mp_1990, mp_2006=mp_2006) %>%
mutate(dif_me=round((me2006/me1990-1)*100,2),
dif_mp=round((mp_2006/mp_1990-1)*100,2)) %>%
mutate(sector=row_number()) %>%
select(sector, everything())
tabla %>%
kable(aption="Tabla de los multiplicadores MIP 1990 y 2006",
align = "c",
digits = 2) %>%
kable_material(html_font = "sans-serif") %>%
kable_styling(bootstrap_options = c("striped", "hover"))
|
sector
|
me1990
|
me2006
|
mp_1990
|
mp_2006
|
dif_me
|
dif_mp
|
|
1
|
1.26
|
1.30
|
1.11
|
1.01
|
3.41
|
-8.32
|
|
2
|
1.27
|
1.02
|
1.19
|
1.05
|
-19.53
|
-11.62
|
|
3
|
1.26
|
1.23
|
1.75
|
1.47
|
-2.11
|
-15.76
|
|
4
|
1.57
|
1.85
|
1.51
|
1.30
|
17.84
|
-13.89
|
|
5
|
1.12
|
1.08
|
1.39
|
1.24
|
-3.61
|
-10.41
|
|
6
|
1.45
|
1.40
|
2.18
|
1.62
|
-3.07
|
-25.57
|
|
7
|
1.77
|
1.69
|
1.12
|
1.06
|
-4.66
|
-5.24
|
|
8
|
1.03
|
1.03
|
1.25
|
1.20
|
-0.27
|
-3.98
|
|
9
|
1.38
|
1.50
|
1.08
|
1.36
|
8.37
|
25.53
|
|
10
|
1.05
|
1.07
|
3.67
|
2.57
|
2.28
|
-30.05
|
|
11
|
1.83
|
1.40
|
1.22
|
1.15
|
-23.62
|
-6.00
|
|
12
|
1.79
|
1.47
|
1.13
|
1.08
|
-17.71
|
-4.87
|
|
13
|
1.06
|
1.04
|
1.01
|
1.00
|
-1.73
|
-0.42
|
|
14
|
1.80
|
1.53
|
1.59
|
1.36
|
-15.19
|
-14.87
|
|
15
|
1.84
|
1.94
|
1.19
|
1.05
|
5.49
|
-11.75
|
|
16
|
1.55
|
1.34
|
2.22
|
1.92
|
-13.43
|
-13.77
|
|
17
|
1.37
|
1.28
|
1.25
|
1.13
|
-6.51
|
-9.52
|
|
18
|
1.38
|
1.00
|
1.02
|
1.00
|
-27.79
|
-1.50
|
|
19
|
1.51
|
1.30
|
1.93
|
1.47
|
-13.58
|
-23.73
|
|
20
|
1.64
|
1.37
|
1.03
|
1.02
|
-16.49
|
-1.17
|
|
21
|
1.58
|
1.40
|
1.19
|
1.13
|
-11.63
|
-5.01
|
|
22
|
1.36
|
1.18
|
1.15
|
1.11
|
-13.56
|
-3.43
|
|
23
|
1.67
|
1.43
|
2.70
|
2.02
|
-13.92
|
-25.15
|
|
24
|
1.45
|
1.36
|
2.74
|
1.89
|
-5.81
|
-31.06
|
|
25
|
1.33
|
1.21
|
3.15
|
2.15
|
-8.65
|
-31.76
|
|
26
|
1.41
|
1.24
|
6.99
|
6.35
|
-11.94
|
-9.15
|
|
27
|
1.40
|
1.28
|
2.60
|
1.56
|
-8.51
|
-39.94
|
|
28
|
1.46
|
1.50
|
1.61
|
1.51
|
2.52
|
-6.01
|
|
29
|
2.37
|
1.99
|
2.20
|
1.79
|
-15.90
|
-18.49
|
|
30
|
1.30
|
1.11
|
1.50
|
1.25
|
-14.65
|
-16.37
|
|
31
|
1.11
|
1.09
|
2.07
|
1.53
|
-1.88
|
-25.84
|
|
32
|
1.45
|
1.59
|
2.19
|
2.80
|
9.43
|
27.99
|
|
33
|
1.39
|
2.12
|
1.18
|
1.08
|
52.61
|
-8.14
|
|
34
|
1.83
|
1.67
|
1.24
|
1.19
|
-8.32
|
-4.21
|
|
35
|
37.44
|
23.76
|
1.07
|
1.06
|
-36.53
|
-0.52
|
|
36
|
1.64
|
1.30
|
1.29
|
1.20
|
-20.59
|
-7.34
|
|
37
|
1.52
|
1.40
|
13.82
|
10.30
|
-7.94
|
-25.44
|
|
38
|
1.26
|
1.68
|
2.26
|
1.59
|
33.91
|
-29.70
|
|
39
|
1.35
|
1.24
|
3.72
|
1.77
|
-8.63
|
-52.48
|
|
40
|
1.17
|
1.17
|
7.53
|
6.50
|
0.13
|
-13.68
|
|
41
|
1.30
|
1.36
|
3.67
|
3.08
|
4.49
|
-16.27
|