Leontiefâ€™s Input-Output Model in R

Common variables

Let X be the input-output matrix, w the wage vector, c the household consumption vector, d the total final demand vector, and e the employment coefficient.

library(leontief)

X <- transaction_matrix
w <- wage_demand_matrix[, "wage"]
c <- wage_demand_matrix[, "household_consumption"]
d <- wage_demand_matrix[, "final_total_demand"]
e <- employment_matrix[, "employees"]

Input requirement matrix

Let A be the direct coefficients matrix.

A <- input_requirement(X, d)
A_aug <- augmented_input_requirement(X,w,c,d)
rownames(A_aug) <- c(rownames(X), "wage_over_demand")
colnames(A_aug) <- c(rownames(X), "consumption_over_demand")
kable(A_aug)
agriculture_fishing mining manufacturing_industry electricity_gas_water construction retail_hotels_restaurants transport_communications_information financial_services real_state business_services personal_services public_administration consumption_over_demand
agriculture_fishing 0.147 0.000 0.125 0.003 0.000 0.007 0.000 0.000 0.000 0.001 0.002 0.002 0.110
mining 0.008 0.071 0.030 0.002 0.005 0.002 0.001 0.002 0.000 0.002 0.001 0.001 0.001
manufacturing_industry 0.185 0.052 0.137 0.050 0.194 0.077 0.061 0.010 0.002 0.026 0.047 0.027 0.261
electricity_gas_water 0.007 0.056 0.028 0.308 0.004 0.014 0.008 0.005 0.006 0.006 0.014 0.035 0.210
construction 0.002 0.001 0.001 0.009 0.123 0.008 0.005 0.002 0.129 0.004 0.011 0.028 0.001
retail_hotels_restaurants 0.049 0.027 0.037 0.023 0.053 0.070 0.057 0.015 0.004 0.031 0.040 0.018 0.522
transport_communications_information 0.036 0.034 0.057 0.027 0.019 0.099 0.144 0.041 0.004 0.044 0.019 0.037 0.311
financial_services 0.028 0.006 0.015 0.018 0.028 0.036 0.020 0.109 0.039 0.019 0.009 0.003 0.438
real_state 0.003 0.003 0.005 0.002 0.003 0.048 0.015 0.010 0.014 0.024 0.023 0.009 0.699
business_services 0.029 0.091 0.065 0.041 0.056 0.094 0.073 0.090 0.019 0.123 0.042 0.047 0.051
personal_services 0.001 0.001 0.003 0.001 0.001 0.004 0.005 0.003 0.001 0.003 0.034 0.003 0.451
public_administration 0.001 0.001 0.002 0.002 0.000 0.004 0.003 0.001 0.000 0.001 0.002 0.003 0.034
wage_over_demand 0.146 0.090 0.104 0.064 0.241 0.251 0.154 0.255 0.030 0.299 0.554 0.541 0.000

Output allocation matrix

Let B be the output allocation matrix.

B <- output_allocation(X, d)
rownames(B) <- rownames(X)
colnames(B) <- rownames(X)
kable(B)
agriculture_fishing mining manufacturing_industry electricity_gas_water construction retail_hotels_restaurants transport_communications_information financial_services real_state business_services personal_services public_administration
agriculture_fishing 0.147 0.000 0.524 0.002 0.001 0.020 0.000 0.000 0.000 0.002 0.003 0.002
mining 0.003 0.071 0.054 0.001 0.004 0.002 0.001 0.001 0.000 0.002 0.001 0.000
manufacturing_industry 0.044 0.029 0.137 0.010 0.086 0.050 0.035 0.002 0.000 0.012 0.021 0.005
electricity_gas_water 0.008 0.153 0.138 0.308 0.009 0.044 0.024 0.006 0.008 0.015 0.031 0.033
construction 0.001 0.001 0.002 0.004 0.123 0.012 0.007 0.001 0.077 0.004 0.011 0.012
retail_hotels_restaurants 0.018 0.024 0.058 0.007 0.037 0.070 0.050 0.006 0.001 0.022 0.028 0.005
transport_communications_information 0.015 0.034 0.100 0.010 0.015 0.113 0.144 0.017 0.002 0.035 0.015 0.013
financial_services 0.029 0.013 0.064 0.015 0.053 0.098 0.047 0.109 0.044 0.036 0.017 0.002
real_state 0.003 0.006 0.019 0.002 0.005 0.115 0.033 0.009 0.014 0.040 0.038 0.006
business_services 0.015 0.111 0.142 0.018 0.055 0.133 0.090 0.047 0.011 0.123 0.041 0.020
personal_services 0.000 0.002 0.006 0.000 0.001 0.005 0.007 0.002 0.000 0.003 0.034 0.001
public_administration 0.002 0.003 0.009 0.002 0.000 0.013 0.009 0.001 0.000 0.002 0.004 0.003

Leontief inverse matrix

Let I be the identity matrix. Leontief inverse is the same as solving I - A.

L <- leontief_inverse(A);
rownames(L) <- rownames(X)
colnames(L) <- rownames(X)
kable(L)
agriculture_fishing mining manufacturing_industry electricity_gas_water construction retail_hotels_restaurants transport_communications_information financial_services real_state business_services personal_services public_administration
agriculture_fishing 1.214 0.014 0.181 0.021 0.043 0.029 0.017 0.005 0.007 0.009 0.014 0.011
mining 0.020 1.080 0.042 0.007 0.016 0.007 0.005 0.003 0.003 0.004 0.004 0.003
manufacturing_industry 0.283 0.088 1.225 0.106 0.286 0.128 0.106 0.029 0.043 0.051 0.077 0.055
electricity_gas_water 0.030 0.094 0.059 1.454 0.025 0.033 0.023 0.013 0.013 0.016 0.027 0.055
construction 0.007 0.005 0.007 0.018 1.145 0.022 0.013 0.006 0.151 0.011 0.019 0.035
retail_hotels_restaurants 0.087 0.048 0.074 0.050 0.090 1.100 0.085 0.029 0.019 0.048 0.055 0.032
transport_communications_information 0.090 0.066 0.110 0.067 0.067 0.150 1.195 0.067 0.018 0.071 0.041 0.059
financial_services 0.052 0.018 0.037 0.039 0.051 0.058 0.037 1.129 0.053 0.031 0.019 0.011
real_state 0.014 0.011 0.016 0.010 0.014 0.061 0.027 0.017 1.018 0.032 0.030 0.014
business_services 0.087 0.137 0.126 0.094 0.120 0.152 0.124 0.129 0.045 1.162 0.070 0.073
personal_services 0.003 0.003 0.005 0.003 0.003 0.006 0.008 0.005 0.001 0.004 1.036 0.004
public_administration 0.003 0.002 0.003 0.003 0.001 0.005 0.004 0.001 0.001 0.002 0.002 1.004

Equilibrium output

The required output is given by L * d.

eq <- equilibrium_output(L, d)
rownames(eq) <- rownames(X)
colnames(eq) <- "output"
kable(eq)
output
agriculture_fishing 25832
mining 31674
manufacturing_industry 81363
electricity_gas_water 23428
construction 28817
retail_hotels_restaurants 47168
transport_communications_information 50606
financial_services 21588
real_state 18686
business_services 51363
personal_services 23148
public_administration 9746

Multipliers

Output multiplier

The output multiplier is the column sum of L.

out <- output_multiplier(L)

Income multiplier

Let W be a matrix where each column is w with the same dimension as L. The income multiplier is the column sum of the element-wise multiplication of L and W element-wise divided by w.

inc <- income_multiplier(L, w/d)

Employment multiplier

Let E be a matrix where each column is e with the same dimension as L. The employment multiplier is the column sum of the element-wise multiplication of L and E element-wise divided by e.

emp <- employment_multiplier(L, e/d)

Summary of multipliers

sm <- round(cbind(out,inc,emp),4)
rownames(sm) <- rownames(X)
colnames(sm) <- c("output_multiplier", "income_multiplier", "employment_multiplier")
kable(sm)
output_multiplier income_multiplier employment_multiplier
agriculture_fishing 1.89 0.291 94.3
mining 1.57 0.187 21.4
manufacturing_industry 1.88 0.250 46.2
electricity_gas_water 1.87 0.177 22.3
construction 1.86 0.400 55.3
retail_hotels_restaurants 1.75 0.393 78.3
transport_communications_information 1.64 0.278 41.3
financial_services 1.44 0.354 24.9
real_state 1.37 0.109 14.5
business_services 1.44 0.393 29.1
personal_services 1.40 0.639 90.8
public_administration 1.36 0.607 53.9

Linkages

Backward and forward linkage

bl <- backward_linkage(A)
fl <- forward_linkage(A)
bfl <- cbind(bl,fl)
rownames(bfl) <- rownames(X)
colnames(bfl) <- c("backward_linkage", "forward_linkage")
kable(bfl)
backward_linkage forward_linkage
agriculture_fishing 0.496 0.288
mining 0.343 0.123
manufacturing_industry 0.506 0.869
electricity_gas_water 0.486 0.491
construction 0.487 0.322
retail_hotels_restaurants 0.462 0.424
transport_communications_information 0.393 0.561
financial_services 0.288 0.330
real_state 0.217 0.159
business_services 0.283 0.770
personal_services 0.243 0.059
public_administration 0.211 0.019

Power of dispersion

bl <- power_dispersion(L)
bl_cv <- power_dispersion_cv(L)
bl_t <- cbind(bl,bl_cv)
rownames(bl_t) <- rownames(X)
colnames(bl_t) <- c("power_dispersion", "power_dispersion_cv")
kable(bl_t)
power_dispersion power_dispersion_cv
agriculture_fishing 1.165 2.14
mining 0.965 2.40
manufacturing_industry 1.161 2.10
electricity_gas_water 1.154 2.59
construction 1.147 2.07
retail_hotels_restaurants 1.079 2.05
transport_communications_information 1.014 2.38
financial_services 0.884 2.68
real_state 0.845 2.60
business_services 0.888 2.64
personal_services 0.860 2.65
public_administration 0.836 2.66

Sensitivity of dispersion

sl <- sensitivity_dispersion(L)
sl_cv <- sensitivity_dispersion_cv(L)
sl_t <- cbind(sl,sl_cv)
rownames(sl_t) <- rownames(X)
colnames(sl_t) <- c("power_dispersion", "power_dispersion_cv")
kable(sl_t)
power_dispersion power_dispersion_cv
agriculture_fishing 0.965 2.60
mining 0.736 3.12
manufacturing_industry 1.526 1.54
electricity_gas_water 1.136 2.63
construction 0.887 2.68
retail_hotels_restaurants 1.058 2.09
transport_communications_information 1.234 1.94
financial_services 0.947 2.52
real_state 0.779 2.84
business_services 1.430 1.61
personal_services 0.667 3.39
public_administration 0.636 3.46

Multiplier product matrix

mp <- multiplier_product_matrix(L)
rownames(mp) <- rownames(X)
colnames(mp) <- rownames(X)
kable(mp)
agriculture_fishing mining manufacturing_industry electricity_gas_water construction retail_hotels_restaurants transport_communications_information financial_services real_state business_services personal_services public_administration
agriculture_fishing 0.152 0.116 0.240 0.179 0.140 0.167 0.194 0.149 0.123 0.225 0.105 0.100
mining 0.126 0.096 0.199 0.148 0.116 0.138 0.161 0.124 0.102 0.187 0.087 0.083
manufacturing_industry 0.151 0.116 0.240 0.178 0.139 0.166 0.194 0.149 0.122 0.225 0.105 0.100
electricity_gas_water 0.151 0.115 0.238 0.177 0.138 0.165 0.192 0.148 0.122 0.223 0.104 0.099
construction 0.150 0.114 0.237 0.176 0.138 0.164 0.191 0.147 0.121 0.222 0.103 0.099
retail_hotels_restaurants 0.141 0.107 0.223 0.166 0.129 0.154 0.180 0.138 0.114 0.209 0.097 0.093
transport_communications_information 0.132 0.101 0.209 0.156 0.122 0.145 0.169 0.130 0.107 0.196 0.091 0.087
financial_services 0.115 0.088 0.182 0.136 0.106 0.126 0.148 0.113 0.093 0.171 0.080 0.076
real_state 0.110 0.084 0.174 0.130 0.101 0.121 0.141 0.108 0.089 0.163 0.076 0.073
business_services 0.116 0.088 0.183 0.136 0.107 0.127 0.148 0.114 0.094 0.172 0.080 0.076
personal_services 0.112 0.086 0.177 0.132 0.103 0.123 0.143 0.110 0.091 0.166 0.078 0.074
public_administration 0.109 0.083 0.172 0.128 0.100 0.120 0.139 0.107 0.088 0.162 0.075 0.072

Induced effects (labor/consumption)

bli <- backward_linkage(A_aug)
fli <- forward_linkage(A_aug)
bfli <- cbind(bli,fli)
rownames(bfli) <- c(rownames(X), "wage")
# wie = with induced effect
colnames(bfli) <- c("backward_linkage_wie", "forward_linkage_wie")
kable(bfli)
backward_linkage_wie forward_linkage_wie
agriculture_fishing 0.643 0.398
mining 0.433 0.123
manufacturing_industry 0.609 1.130
electricity_gas_water 0.550 0.702
construction 0.728 0.323
retail_hotels_restaurants 0.713 0.947
transport_communications_information 0.547 0.872
financial_services 0.543 0.768
real_state 0.247 0.858
business_services 0.582 0.821
personal_services 0.797 0.510
public_administration 0.752 0.053
wage 3.090 2.729

References

Schuschny, Andres Ricardo. Topicos sobre el modelo de insumo-producto: teoria y aplicaciones. Cepal, 2005.

Pino Arriagada, Andres y Fuentes Navarro, Silvia. Derivacion y analisis de los multiplicadores de empleo para la economia nacional. Universidad del Bio-Bio, 2018.