Tarea_6_AT21006

1 Importacion de base de datos

1.1 Base de datos 2006

library(dplyr)
library(readxl)

# Consumo Intermedio

MIP_2006_CI<-read_excel("C:/Users/Walter Alemán/Desktop/UES VI/MAE/TAREA N6/MIP_1990_2006.xls",
                     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

MIP_2006_H<-read_excel("C:/Users/Walter Alemán/Desktop/UES VI/MAE/TAREA N6/MIP_1990_2006.xls",
                     sheet = "MIP 2006", range = "bd15:be60",
                     col_names = FALSE)
names(MIP_2006_H)<-as.character(1:2)
print(MIP_2006_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

MIP_2006_X<-read_excel("C:/Users/Walter Alemán/Desktop/UES VI/MAE/TAREA N6/MIP_1990_2006.xls",
                     sheet = "MIP 2006", range = "bi15:bi60",
                     col_names = FALSE)
names(MIP_2006_X)<-as.character("x_2006")
print(MIP_2006_X)

## # A tibble: 46 × 1
##      x_2006
##       <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

1.2 Agregacion de servicios (2006)

Ser_ROW_2006<-colSums(MIP_2006_CI[41:46,])
Tem_2006<-rbind(MIP_2006_CI[1:40,], Ser_ROW_2006)
Ser_COL_2006<-rowSums(Tem_2006[,41:46])
MIP2006_CI_COR<-cbind(Tem_2006[,1:40], Ser_COL_2006)
names(MIP2006_CI_COR)<-as.character(1:41)
X_2006<-rbind(MIP_2006_X[1:40,], colSums(MIP_2006_X[41:46,]))

1.3 Base de datos 1990

library(dplyr)
library(readxl)

# Consumo Intermedio

MIP_1990_CI<-read_excel("C:/Users/Walter Alemán/Desktop/UES VI/MAE/TAREA N6/MIP_1990_2006.xls",
                     sheet = "MIP 1990", range = "i14:bb59",
                     col_names = FALSE)
names(MIP_1990_CI)<-as.character(1:46)
print(MIP_1990_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

MIP_1990_H<-read_excel("C:/Users/Walter Alemán/Desktop/UES VI/MAE/TAREA N6/MIP_1990_2006.xls",
                     sheet = "MIP 1990", range = "bd14:be59",
                     col_names = FALSE)
names(MIP_1990_H)<-as.character(1:2)
print(MIP_1990_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

MIP_1990_X<-read_excel("C:/Users/Walter Alemán/Desktop/UES VI/MAE/TAREA N6/MIP_1990_2006.xls",
                     sheet = "MIP 1990", range = "bi14:bi59",
                     col_names = FALSE)
names(MIP_1990_X)<-as.character("x_1990")
print(MIP_1990_X)

## # A tibble: 46 × 1
##     x_1990
##      <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

1.4 Agregacion de servicios (1990)

Ser_ROW_1990<-colSums(MIP_1990_CI[41:46,])
Tem_1990<-rbind(MIP_1990_CI[1:40,], Ser_ROW_1990)
Ser_COL_1990<-rowSums(Tem_1990[,41:46])
MIP1990_CI_COR<-cbind(Tem_1990[,1:40], Ser_COL_1990)
names(MIP1990_CI_COR)<-as.character(1:40)
X_1990<-rbind(MIP_1990_X[1:40,], colSums(MIP_1990_X[41:46,]))

2 Funciones

2.1 Matriz de coeficientes tecnicos

MIP_COEF_TEC<-function(matriz_consumo_intermerdio,
                       vector_demanda_final){
  filas_ci<-nrow(matriz_consumo_intermerdio)
  columnas_ci<-ncol(matriz_consumo_intermerdio)
  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 (difernte dimension)", call. = FALSE)
  }
  v<-solve(diag(as.vector(vector_demanda_final)))
  A<-matriz_consumo_intermerdio %*% v
  list(A=A, V=v)
}

2.2 Matriz Tecnologica

library(dplyr)

MIP_MATR_TEC<-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 coeficientes tecnicos. Cuadrada", call. = FALSE)
  }
  TIP_MATR<-typeof(matriz_coeficientes_tecnicos)
  if(TIP_MATR!="double"){
    stop("La matriz ingresada no es numerica", call. = FALSE)
  }
  T<-diag(1,filas_A)-matriz_coeficientes_tecnicos
  T
}

MIP_MATR_Leontief<-function(matriz_tecnologica){
  L<-solve(matriz_tecnologica)
  L
}

MIP_MUL_Pro_MP<-function(matriz_leontief){
  MP<-rowSums(matriz_leontief)
  MP
}

MIP_MUL_Exp_ME<-function(matriz_leontief){
  ME<-colSums(matriz_leontief)
  ME
}

MIP_ENCA_PD<-function(matriz_leontief){
  MP<-MIP_MUL_Pro_MP(matriz_leontief)
  MP/mean(MP)
}

MIP_ENCA_SD<-function(matriz_leontief){
  ME<-MIP_MUL_Exp_ME(matriz_leontief)
  ME/mean(ME)
}

MIP_TABLA_Rasmussen<-function(matriz_leontief){
  PD<-MIP_ENCA_PD(matriz_leontief)
  SD<-MIP_ENCA_SD(matriz_leontief)
  rasmussen<-data.frame(PD=PD, SD=SD)
  rasmussen_CLA<-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_CLA
}

3 Análisis de la MIP en R

3.1 Ejercicio_1

Realice el cálculo de lo siguiente (de ser necesario haga las agregaciones de servicios propuestas en la clase):

3.1.1 Literal A)

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

Multiplicador (ME) MIP 2006

A_2006<-MIP_COEF_TEC(as.matrix(MIP2006_CI_COR), as.matrix(X_2006))[[1]]
MAT_T_2006<-MIP_MATR_TEC(matriz_coeficientes_tecnicos = A_2006)
L_2006<-MIP_MATR_Leontief(matriz_tecnologica = MAT_T_2006)
ME_2006<-MIP_MUL_Exp_ME(matriz_leontief = L_2006)
ME_2006

##  [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

Multiplicador (ME) MIP 1990

A_1990<-MIP_COEF_TEC(as.matrix(MIP1990_CI_COR), as.matrix(X_1990))[[1]]
MAT_T_1990<-MIP_MATR_TEC(matriz_coeficientes_tecnicos = A_1990)
L_1990<-MIP_MATR_Leontief(matriz_tecnologica = MAT_T_1990)
ME_1990<-MIP_MUL_Exp_ME(matriz_leontief = L_1990)
ME_1990

##  [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

3.1.2 Literal B)

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

Multiplicador (MP) MIP 2006

A_2006<-MIP_COEF_TEC(as.matrix(MIP2006_CI_COR), as.matrix(X_2006))[[1]]
MAT_T_2006<-MIP_MATR_TEC(matriz_coeficientes_tecnicos = A_2006)
L_2006<-MIP_MATR_Leontief(matriz_tecnologica = MAT_T_2006)
MP_2006<-MIP_MUL_Pro_MP(matriz_leontief = L_2006)
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

Multiplicador (MP) MIP 1990

A_1990<-MIP_COEF_TEC(as.matrix(MIP1990_CI_COR), as.matrix(X_1990))[[1]]
MAT_T_1990<-MIP_MATR_TEC(matriz_coeficientes_tecnicos = A_1990)
L_1990<-MIP_MATR_Leontief(matriz_tecnologica = MAT_T_1990)
MP_1990<-MIP_MUL_Pro_MP(matriz_leontief = L_1990)
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

3.1.3 Literal C)

Tasa de cambio para ambos multiplicadores (por ejemplo, para me:me2006/me1990-1)

Tasa de cambio multplicador ME

TASA_CAMBIO_ME<-ME_2006/ME_1990-1
TASA_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 multplicador MP

TASA_CAMBIO_ME<-MP_2006/MP_1990-1
TASA_CAMBIO_ME

##  [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

3.1.4 Literal D)

Presente los resultados en una tabla que incluya los nombres para todos los sectores.

library(dplyr)
library(kableExtra)

TABLA <- 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 %>% 
  kable(caption = "Tabla de los multiplicadores MIP de 1990 y 2006",
        align = "c",
        digits = 2) %>% 
  kable_material(html_font = "sans-serif") %>% 
  kable_styling(bootstrap_options = c("striped", "hover"))

Tabla de los multiplicadores MIP de 1990 y 2006

sector	ME_1990	ME_2006	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

3.2 Ejercicio_2

Realice el análisis de Rasmussen para para las MIP 1990 y 2006.

Tabla de analisis Rasmussen para MIP 2006

library(dplyr)
library(kableExtra)

rasmussen_2006<-MIP_TABLA_Rasmussen(L_2006)

summary_RAS_2006 <- rasmussen_2006 %>% 
  group_by(clasificacion) %>% 
  summarise(total=n()) %>% 
  mutate(porcentaje=round(prop.table(total)*100,2))

summary_RAS_2006 %>% 
  kable(aption="Analisis de Rasmussen para MIP 2006",
        align = "c",
        digits = 2) %>% 
  kable_material(html_font = "sans-serif") %>% 
  kable_styling(bootstrap_options = c("striped", "hover"))

clasificacion	total	porcentaje
Sector Estrategico	4	9.76
Sector Impulsor	8	19.51
Sector Isla	29	70.73

Tabla de analisis Rasmussen para MIP 1990

library(dplyr)
library(kableExtra)

rasmussen_1990<-MIP_TABLA_Rasmussen(L_1990)

summary_RAS_1990 <- rasmussen_1990 %>% 
  group_by(clasificacion) %>% 
  summarise(total=n()) %>% 
  mutate(porcentaje=round(prop.table(total)*100,2))

summary_RAS_1990 %>% 
  kable(aption="Analisis de Rasmussen para MIP 1990",
        align = "c",
        digits = 2) %>% 
  kable_material(html_font = "sans-serif") %>% 
  kable_styling(bootstrap_options = c("striped", "hover"))

clasificacion	total	porcentaje
Sector Estrategico	2	4.88
Sector Impulsor	10	24.39
Sector Isla	29	70.73

3.3 Ejercicio_3

Presente una tabla comparativa entre los resultados porcentuales por tipo de sector entre 1990 y 2006, incluya una columna que muestre la variación porcentual por tipo.

library(dplyr)

TAB_Comparativa<-left_join(summary_RAS_1990, summary_RAS_2006, by="clasificacion", suffix=c("_1990", "_2006"))

TAB_Comparativa %>% 
  mutate(DIF_VAR_porcentual=round((porcentaje_2006/porcentaje_1990-1)*100,2)) %>% 
  kable(aption = "Tabla Comparativa",
        align = "c",
        digits = 2) %>% 
  kable_material(html_font = "sans-serif") %>% 
  kable_styling(bootstrap_options = c("striped", "hover"))

clasificacion	total_1990	porcentaje_1990	total_2006	porcentaje_2006	DIF_VAR_porcentual
Sector Estrategico	2	4.88	4	9.76	100.00
Sector Impulsor	10	24.39	8	19.51	-20.01
Sector Isla	29	70.73	29	70.73	0.00