R6 class for input-output matrix.
A new instance of the iom class.
id(character)
Identifier of the new instance.
intermediate_transactions(matrix)
Intermediate transactions matrix.
total_production(matrix)
Total production vector.
household_consumption(matrix)
Household consumption vector.
government_consumption(matrix)
Government consumption vector.
exports(matrix)
Exports vector.
final_demand_others(matrix)
Other vectors of final demand that doesn't have dedicated slots.
final_demand_matrix(matrix)
Aggregates final demand vectors into a matrix.
imports(matrix)
Imports vector.
taxes(matrix)
Taxes vector.
wages(matrix)
Wages vector.
operating_income(matrix)
Operating income vector.
value_added_others(matrix)
Other vectors of value-added that doesn't have dedicated slots.
value_added_matrix(matrix)
Aggregates value-added vectors into a matrix.
occupation(matrix)
Occupation vector.
technical_coefficients_matrix(matrix)
Technical coefficients matrix.
leontief_inverse_matrix(matrix)
Leontief inverse matrix.
multiplier_output(data.frame)
Output multiplier dataframe.
multiplier_employment(data.frame)
Employment multiplier dataframe.
multiplier_taxes(data.frame)
Taxes multiplier dataframe.
multiplier_wages(data.frame)
Wages multiplier dataframe.
field_influence(matrix)
Influence field matrix.
key_sectors(data.frame)
Key sectors dataframe.
allocation_coefficients_matrix(matrix)
Allocation coefficients matrix.
ghosh_inverse_matrix(matrix)
Ghosh inverse matrix.
hypothetical_extraction(matrix)
Absolute and relative backward and forward differences in total output after a hypothetical extraction
new()Creates a new instance of this R6 class.
iom$new(
id,
intermediate_transactions,
total_production,
household_consumption = NULL,
government_consumption = NULL,
exports = NULL,
final_demand_others = NULL,
imports = NULL,
taxes = NULL,
wages = NULL,
operating_income = NULL,
value_added_others = NULL,
occupation = NULL
)id(character)
Identifier for the input-output matrix.
intermediate_transactions(matrix)
Intermediate transactions matrix.
total_production(matrix)
Total production vector.
household_consumption(matrix)
Household consumption vector.
government_consumption(matrix)
Government consumption vector.
exports(matrix)
Exports vector.
final_demand_others(matrix)
Other vectors of final demand that doesn't have dedicated slots.
Setting column names is advised for better readability.
imports(matrix)
Imports vector.
taxes(matrix)
Taxes vector.
wages(matrix)
Wages vector.
operating_income(matrix)
Operating income vector.
value_added_others(matrix)
Other vectors of value-added that doesn't have dedicated slots.
Setting row names is advised for better readability.
occupation(matrix)
Occupation matrix.
add()Adds a matrix to the iom object.
matrix_name(character)
One of household_consumption, government_consumption, exports, final_demand_others,
imports, taxes, wages, operating income, value_added_others or occupation matrix to be added.
matrix(matrix)
Matrix object to be added.
# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
# instantiate iom object
my_iom <- iom$new("mock", intermediate_transactions, total_production)
# Create a dummy matrix
exports_data <- matrix(as.numeric(1:3), 3, 1)
# Add the matrix
my_iom$add("exports", exports_data)remove()Removes a matrix from the iom object.
matrix_name(character)
One of household_consumption, government_consumption, exports, final_demand_others,
imports, taxes, wages, operating_income, value_added_others or occupation matrix to be removed.
# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
exports_data <- matrix(as.numeric(1:3), 3, 1)
# instantiate iom object
my_iom <- iom$new("mock", intermediate_transactions, total_production, exports = exports_data)
# Remove the matrix
my_iom$remove("exports")update_final_demand_matrix()Aggregates final demand vectors into the final_demand_matrix field.
Some methods, as $compute_hypothetical_extraction(), require the final demand and value-added vectors
to be aggregated into a matrix. This method does this aggregation, binding the vectors into
$final_demand_matrix.
# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
exports_data <- matrix(c(10, 20, 30), 3, 1)
households <- matrix(as.numeric(4:6), 3, 1)
# instantiate iom object
my_iom <- iom$new(
"mock",
intermediate_transactions,
total_production,
exports = exports_data,
household_consumption = households
)
# aggregate all final demand vectors
my_iom$update_final_demand_matrix()
# check final demand matrix
my_iom$final_demand_matrixupdate_value_added_matrix()Aggregates value-added vectors into the value_added_matrix field.
Some methods, as $compute_hypothetical_extraction(), require the final demand and value-added vectors
to be aggregated into a matrix. This method does this aggregation, binding the vectors into
$value_added_matrix.
# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
imports_data <- matrix(c(5, 10, 15), 1, 3)
taxes_data <- matrix(c(2, 5, 10), 1, 3)
# instantiate iom object
my_iom <- iom$new(
"mock",
intermediate_transactions,
total_production,
imports = imports_data,
taxes = taxes_data
)
# aggregate all value-added vectors
my_iom$update_value_added_matrix()
# check value-added matrix
my_iom$value_added_matrixcompute_tech_coeff()Computes the technical coefficients matrix and populate the technical_coefficients_matrix field with the
resulting (matrix).
It computes the technical coefficients matrix, a \(n x n\) matrix known as A matrix which is the column-wise
ratio of intermediate transactions to total production (Leontief 1983)
.
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
# instantiate iom object
my_iom <- iom$new("test", intermediate_transactions, total_production)
# Calculate the technical coefficients
my_iom$compute_tech_coeff()
# show the technical coefficients
my_iom$technical_coefficients_matrixcompute_leontief_inverse()Computes the Leontief inverse matrix and populate the leontief_inverse_matrix field with the resulting
(matrix).
It computes the Leontief inverse matrix (Leontief 1983) , which is the inverse of the Leontief matrix, defined as:
$$L = I - A$$
where I is the identity matrix and A is the technical coefficients matrix. The Leontief inverse matrix is calculated by solving the following equation:
$$L^{-1} = (I - A)^{-1}$$
Since the Leontief matrix is a square matrix and the subtraction of the technical coefficients matrix from the identity matrix guarantees that the Leontief matrix is invertible, underlined Rust function uses LU decomposition to solve the equation.
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new("test", intermediate_transactions, total_production)
# calculate the technical coefficients
my_iom$compute_tech_coeff()
# calculate the Leontief inverse
my_iom$compute_leontief_inverse()
# show the Leontief inverse
my_iom$leontief_inverse_matrixcompute_multiplier_output()Computes the output multiplier and populate the multiplier_output field with the resulting (data.frame).
An output multiplier for sector j is defined as the total value of production in all sectors of the economy that is necessary in order to satisfy a monetary unit (e.g., a dollar) worth of final demand for sector j's output (Miller and Blair 2009) .
This method computes the simple output multiplier, defined as the column sums of the Leontief inverse matrix, the direct and indirect output multipliers, which are the column sums of the technical coefficients matrix and the difference between total and direct output multipliers, respectively (Vale and Perobelli 2020) .
Miller RE, Blair PD (2009).
Input-Output Analysis: Foundations and Extensions, 2 edition.
Cambridge University Press.
ISBN 978-0-521-73902-3 978-0-521-51713-3 978-0-511-62698-2, doi:10.1017/CBO9780511626982
, https://www.cambridge.org/core/product/identifier/9780511626982/type/book.
Vale VdA, Perobelli FS (2020).
Análise de Insumo-Produto: teoria e aplicações no R.
Edição Independente, Curitiba, PR.
ISBN 9786500103649.
# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new("test", intermediate_transactions, total_production)
# calculate the technical coefficients
my_iom$compute_tech_coeff()
# calculate the Leontief inverse
my_iom$compute_leontief_inverse()
# calculate the output multiplier
my_iom$compute_multiplier_output()
# show the output multiplier
my_iom$multiplier_outputcompute_multiplier_employment()Computes the employment multiplier and populate the multiplier_employment field with the resulting
(data.frame).
The employment multiplier for sector j relates the jobs created in each sector in response to a initial exogenous shock (Miller and Blair 2009) .
Current implementation follows (Vale and Perobelli 2020) .
Miller RE, Blair PD (2009).
Input-Output Analysis: Foundations and Extensions, 2 edition.
Cambridge University Press.
ISBN 978-0-521-73902-3 978-0-521-51713-3 978-0-511-62698-2, doi:10.1017/CBO9780511626982
, https://www.cambridge.org/core/product/identifier/9780511626982/type/book.
Vale VdA, Perobelli FS (2020).
Análise de Insumo-Produto: teoria e aplicações no R.
Edição Independente, Curitiba, PR.
ISBN 9786500103649.
# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
jobs_data <- matrix(c(10, 12, 15), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new("test", intermediate_transactions, total_production, occupation = jobs_data)
# calculate the technical coefficients
my_iom$compute_tech_coeff()
# calculate the Leontief inverse
my_iom$compute_leontief_inverse()
# calculate the employment multiplier
my_iom$compute_multiplier_employment()
# show the employment multiplier
my_iom$multiplier_employmentcompute_multiplier_wages()Computes the wages multiplier dataframe and populate the multiplier_wages field with the resulting
(data.frame).
The wages multiplier for sector j relates increases in wages for each sector in response to a initial exogenous shock (Miller and Blair 2009) .
Current implementation follows (Vale and Perobelli 2020) .
Miller RE, Blair PD (2009).
Input-Output Analysis: Foundations and Extensions, 2 edition.
Cambridge University Press.
ISBN 978-0-521-73902-3 978-0-521-51713-3 978-0-511-62698-2, doi:10.1017/CBO9780511626982
, https://www.cambridge.org/core/product/identifier/9780511626982/type/book.
Vale VdA, Perobelli FS (2020).
Análise de Insumo-Produto: teoria e aplicações no R.
Edição Independente, Curitiba, PR.
ISBN 9786500103649.
# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
wages_data <- matrix(c(10, 12, 15), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new("test", intermediate_transactions, total_production, wages = wages_data)
# calculate the technical coefficients
my_iom$compute_tech_coeff()
# calculate the Leontief inverse
my_iom$compute_leontief_inverse()
# calculate the wages multiplier
my_iom$compute_multiplier_wages()
# show the wages multiplier
my_iom$multiplier_wagescompute_multiplier_taxes()Computes the taxes multiplier and populate the multiplier_taxes field with
the resulting (data.frame).
The taxes multiplier for sector j relates the increases on tax revenue from each sector in response to a initial exogenous shock (Miller and Blair 2009) .
Current implementation follows (Vale and Perobelli 2020) .
Miller RE, Blair PD (2009).
Input-Output Analysis: Foundations and Extensions, 2 edition.
Cambridge University Press.
ISBN 978-0-521-73902-3 978-0-521-51713-3 978-0-511-62698-2, doi:10.1017/CBO9780511626982
, https://www.cambridge.org/core/product/identifier/9780511626982/type/book.
Vale VdA, Perobelli FS (2020).
Análise de Insumo-Produto: teoria e aplicações no R.
Edição Independente, Curitiba, PR.
ISBN 9786500103649.
# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
tax_data <- matrix(c(10, 12, 15), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new("test", intermediate_transactions, total_production, taxes = tax_data)
# calculate the technical coefficients
my_iom$compute_tech_coeff()
# calculate the Leontief inverse
my_iom$compute_leontief_inverse()
# calculate the tax multiplier
my_iom$compute_multiplier_taxes()
# show the taxes multiplier
my_iom$multiplier_taxescompute_field_influence()Computes the field of influence for all sectors and populate the
field_influence field with the resulting (matrix).
epsilon(numeric)
Epsilon value. A technical change in the input-output matrix, caused by a variation of size epsilon into each
element of technical coefficients matrix.
The field of influence shows how changes in direct coefficients are distributed throughout the entire economic system, allowing for the determination of which relationships between sectors are most important within the production process.
It determines which sectors have the greatest influence over others, specifically, which coefficients, when altered, would have the greatest impact on the system as a whole (Vale and Perobelli 2020) .
# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new("test", intermediate_transactions, total_production)
# calculate the technical coefficients
my_iom$compute_tech_coeff()
# calculate the Leontief inverse
my_iom$compute_leontief_inverse()
# calculate field of influence
my_iom$compute_field_influence(epsilon = 0.01)
# show the field of influence
my_iom$field_influencecompute_key_sectors()Computes the key sectors dataframe, based on it's power and sensitivity of dispersion,
and populate the key_sectors field with the resulting (data.frame).
Increased production from a sector j means that the sector j will need to purchase more goods from other sectors. At the same time, it means that more goods from sector j will be available for other sectors to purchase. Sectors that are above average in the demand sense (stronger backward linkage) have power of dispersion indices greater than 1. Sectors that are above average in the supply sense (stronger forward linkage) have sensitivity of dispersion indices greater than 1 (Miller and Blair 2009) .
As both power and sensitivity of dispersion are related to average values on the economy, coefficients of variation are also calculated for both indices. The lesser the coefficient of variation, greater the number of sectors on the demand or supply structure of that sector (Vale and Perobelli 2020) .
Miller RE, Blair PD (2009).
Input-Output Analysis: Foundations and Extensions, 2 edition.
Cambridge University Press.
ISBN 978-0-521-73902-3 978-0-521-51713-3 978-0-511-62698-2, doi:10.1017/CBO9780511626982
, https://www.cambridge.org/core/product/identifier/9780511626982/type/book.
Vale VdA, Perobelli FS (2020).
Análise de Insumo-Produto: teoria e aplicações no R.
Edição Independente, Curitiba, PR.
ISBN 9786500103649.
# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new("test", intermediate_transactions, total_production)
# calculate the technical coefficients
my_iom$compute_tech_coeff()
# calculate the Leontief inverse
my_iom$compute_leontief_inverse()
# calculate key sectors
my_iom$compute_key_sectors()
# show the key sectors
my_iom$key_sectorscompute_allocation_coeff()Computes the allocation coefficients matrix and populate the allocation_coefficients_matrix field with the
resulting (matrix).
It computes the allocation coefficients matrix, a \(n x n\) matrix known as B matrix which is the row-wise
ratio of intermediate transactions to total production (Miller and Blair 2009)
.
Miller RE, Blair PD (2009). Input-Output Analysis: Foundations and Extensions, 2 edition. Cambridge University Press. ISBN 978-0-521-73902-3 978-0-521-51713-3 978-0-511-62698-2, doi:10.1017/CBO9780511626982 , https://www.cambridge.org/core/product/identifier/9780511626982/type/book.
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new("test", intermediate_transactions, total_production)
# Calculate the allocation coefficients
my_iom$compute_allocation_coeff()
# show the allocation coefficients
my_iom$allocation_coefficients_matrixcompute_ghosh_inverse()Computes the Ghosh inverse matrix and populate the ghosh_inverse_matrix field with the resulting (matrix).
It computes the Ghosh inverse matrix (Miller and Blair 2009) , defined as: $$G = (I - B)^{-1}$$ where I is the identity matrix and B is the allocation coefficients matrix.
Miller RE, Blair PD (2009). Input-Output Analysis: Foundations and Extensions, 2 edition. Cambridge University Press. ISBN 978-0-521-73902-3 978-0-521-51713-3 978-0-511-62698-2, doi:10.1017/CBO9780511626982 , https://www.cambridge.org/core/product/identifier/9780511626982/type/book.
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new("test", intermediate_transactions, total_production)
# Calculate the allocation coefficients
my_iom$compute_allocation_coeff()
# Calculate the Ghosh inverse
my_iom$compute_ghosh_inverse()
# show the Ghosh inverse
my_iom$ghosh_inverse_matrixcompute_hypothetical_extraction()Computes total impact after extracting a each sector and populate the hypothetical_extraction field with the
resulting (data.frame).
Computes impact on demand and supply structures after extracting each sector (Miller and Blair 2009) .
The total impact is calculated by the sum of the direct and indirect impacts.
Miller RE, Blair PD (2009). Input-Output Analysis: Foundations and Extensions, 2 edition. Cambridge University Press. ISBN 978-0-521-73902-3 978-0-521-51713-3 978-0-511-62698-2, doi:10.1017/CBO9780511626982 , https://www.cambridge.org/core/product/identifier/9780511626982/type/book.
# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
exports_data <- matrix(c(5, 10, 15), 3, 1)
holsehold_consumption_data <- matrix(c(20, 25, 30), 3, 1)
operating_income_data <- matrix(c(2, 5, 10), 1, 3)
taxes_data <- matrix(c(1, 2, 3), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new(
"test",
intermediate_transactions,
total_production,
exports = exports_data,
household_consumption = holsehold_consumption_data,
operating_income = operating_income_data,
taxes = taxes_data
)
# update value-added matrix
my_iom$update_value_added_matrix()
# update final demand matrix
my_iom$update_final_demand_matrix()
# calculate the technical coefficients
my_iom$compute_tech_coeff()
# calculate the Leontief inverse
my_iom$compute_leontief_inverse()
# calculate allocation coefficients
my_iom$compute_allocation_coeff()
# calculate Ghosh inverse
my_iom$compute_ghosh_inverse()
# calculate hypothetical extraction
my_iom$compute_hypothetical_extraction()
# show results
my_iom$hypothetical_extractionset_max_threads()Sets max number of threads used by fio and populate the threads field with the resulting (integer).
max_threads(integer)
Number of threads enabled for parallel computing. Defaults to 0, meaning all
threads available.
Calling this function sets a global limit of threads to Rayon crate, affecting all computations that runs in parallel by default.
Default behavior of Rayon is to use all available threads (including logical). Setting to 1 will result in single threaded (sequential) computations.
Initialization of the global thread pool happens exactly once. Once started, the configuration cannot be changed
in the current session. If $set_max_threads() is called again in the same session, it'll result in an error.
Methods that deals with linear algebra computations, like $compute_leontief_inverse() and
$compute_ghosh_inverse(), will try to use all available threads by default, so they also initializes global
thread pool. In order to choose a maximum number of threads other than default, $set_max_threads() must be
called before any computation, preferably right after iom$new().
\dontrun{
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new("test", intermediate_transactions, total_production)
# to run single threaded (sequential)
my_iom$set_max_threads(1L)
}
# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
exports <- matrix(c(10, 20, 30), 3, 1)
households <- matrix(as.numeric(4:6), 3, 1)
imports <- matrix(c(5, 10, 15), 1, 3)
jobs <- matrix(c(10, 12, 15), 1, 3)
taxes <- matrix(c(2, 5, 10), 1, 3)
wages <- matrix(c(11, 12, 13), 1, 3)
# a new iom instance can be created by passing just intermediate transactions and total production
my_iom <- iom$new(
"example_1",
intermediate_transactions,
total_production
)
# or by passing optional arguments
my_iom <- iom$new(
"example_2",
intermediate_transactions,
total_production,
household_consumption = households,
exports = exports,
imports = imports,
taxes = taxes,
wages = wages,
occupation = jobs
)
## ------------------------------------------------
## Method `iom$add`
## ------------------------------------------------
# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
# instantiate iom object
my_iom <- iom$new("mock", intermediate_transactions, total_production)
# Create a dummy matrix
exports_data <- matrix(as.numeric(1:3), 3, 1)
# Add the matrix
my_iom$add("exports", exports_data)
## ------------------------------------------------
## Method `iom$remove`
## ------------------------------------------------
# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
exports_data <- matrix(as.numeric(1:3), 3, 1)
# instantiate iom object
my_iom <- iom$new("mock", intermediate_transactions, total_production, exports = exports_data)
# Remove the matrix
my_iom$remove("exports")
## ------------------------------------------------
## Method `iom$update_final_demand_matrix`
## ------------------------------------------------
# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
exports_data <- matrix(c(10, 20, 30), 3, 1)
households <- matrix(as.numeric(4:6), 3, 1)
# instantiate iom object
my_iom <- iom$new(
"mock",
intermediate_transactions,
total_production,
exports = exports_data,
household_consumption = households
)
# aggregate all final demand vectors
my_iom$update_final_demand_matrix()
# check final demand matrix
my_iom$final_demand_matrix
#> household_consumption exports
#> [1,] 4 10
#> [2,] 5 20
#> [3,] 6 30
## ------------------------------------------------
## Method `iom$update_value_added_matrix`
## ------------------------------------------------
# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
imports_data <- matrix(c(5, 10, 15), 1, 3)
taxes_data <- matrix(c(2, 5, 10), 1, 3)
# instantiate iom object
my_iom <- iom$new(
"mock",
intermediate_transactions,
total_production,
imports = imports_data,
taxes = taxes_data
)
# aggregate all value-added vectors
my_iom$update_value_added_matrix()
# check value-added matrix
my_iom$value_added_matrix
#> [,1] [,2] [,3]
#> imports 5 10 15
#> taxes 2 5 10
## ------------------------------------------------
## Method `iom$compute_tech_coeff`
## ------------------------------------------------
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
# instantiate iom object
my_iom <- iom$new("test", intermediate_transactions, total_production)
# Calculate the technical coefficients
my_iom$compute_tech_coeff()
# show the technical coefficients
my_iom$technical_coefficients_matrix
#> 1 2 3
#> 1 0.01 0.020 0.02333333
#> 2 0.02 0.025 0.02666667
#> 3 0.03 0.030 0.03000000
## ------------------------------------------------
## Method `iom$compute_leontief_inverse`
## ------------------------------------------------
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new("test", intermediate_transactions, total_production)
# calculate the technical coefficients
my_iom$compute_tech_coeff()
# calculate the Leontief inverse
my_iom$compute_leontief_inverse()
# show the Leontief inverse
my_iom$leontief_inverse_matrix
#> 1 2 3
#> 1 1.01129067 0.02151113 0.02491795
#> 2 0.02161815 1.02696918 0.02875285
#> 3 0.03194563 0.03242723 1.03258776
## ------------------------------------------------
## Method `iom$compute_multiplier_output`
## ------------------------------------------------
# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new("test", intermediate_transactions, total_production)
# calculate the technical coefficients
my_iom$compute_tech_coeff()
# calculate the Leontief inverse
my_iom$compute_leontief_inverse()
# calculate the output multiplier
my_iom$compute_multiplier_output()
# show the output multiplier
my_iom$multiplier_output
#> sector multiplier_simple multiplier_direct multiplier_indirect
#> 1 1 1.064854 0.060 1.004854
#> 2 2 1.080908 0.075 1.005908
#> 3 3 1.086259 0.080 1.006259
## ------------------------------------------------
## Method `iom$compute_multiplier_employment`
## ------------------------------------------------
# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
jobs_data <- matrix(c(10, 12, 15), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new("test", intermediate_transactions, total_production, occupation = jobs_data)
# calculate the technical coefficients
my_iom$compute_tech_coeff()
# calculate the Leontief inverse
my_iom$compute_leontief_inverse()
# calculate the employment multiplier
my_iom$compute_multiplier_employment()
# show the employment multiplier
my_iom$multiplier_employment
#> sector multiplier_simple multiplier_direct multiplier_indirect
#> 1 1 0.10402344 0.10 0.004023438
#> 2 2 0.06539063 0.06 0.005390625
#> 3 3 0.05584635 0.05 0.005846354
## ------------------------------------------------
## Method `iom$compute_multiplier_wages`
## ------------------------------------------------
# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
wages_data <- matrix(c(10, 12, 15), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new("test", intermediate_transactions, total_production, wages = wages_data)
# calculate the technical coefficients
my_iom$compute_tech_coeff()
# calculate the Leontief inverse
my_iom$compute_leontief_inverse()
# calculate the wages multiplier
my_iom$compute_multiplier_wages()
# show the wages multiplier
my_iom$multiplier_wages
#> sector multiplier_simple multiplier_direct multiplier_indirect
#> 1 1 0.10402344 0.10 0.004023438
#> 2 2 0.06539063 0.06 0.005390625
#> 3 3 0.05584635 0.05 0.005846354
## ------------------------------------------------
## Method `iom$compute_multiplier_taxes`
## ------------------------------------------------
# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
tax_data <- matrix(c(10, 12, 15), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new("test", intermediate_transactions, total_production, taxes = tax_data)
# calculate the technical coefficients
my_iom$compute_tech_coeff()
# calculate the Leontief inverse
my_iom$compute_leontief_inverse()
# calculate the tax multiplier
my_iom$compute_multiplier_taxes()
# show the taxes multiplier
my_iom$multiplier_taxes
#> sector multiplier_simple multiplier_direct multiplier_indirect
#> 1 1 0.10402344 0.10 0.004023438
#> 2 2 0.06539063 0.06 0.005390625
#> 3 3 0.05584635 0.05 0.005846354
## ------------------------------------------------
## Method `iom$compute_field_influence`
## ------------------------------------------------
# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new("test", intermediate_transactions, total_production)
# calculate the technical coefficients
my_iom$compute_tech_coeff()
# calculate the Leontief inverse
my_iom$compute_leontief_inverse()
# calculate field of influence
my_iom$compute_field_influence(epsilon = 0.01)
# show the field of influence
my_iom$field_influence
#> 1 2 3
#> 1 1.070046 1.081996 1.094748
#> 2 1.081797 1.138488 1.129021
#> 3 1.093813 1.128199 1.164467
## ------------------------------------------------
## Method `iom$compute_key_sectors`
## ------------------------------------------------
# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new("test", intermediate_transactions, total_production)
# calculate the technical coefficients
my_iom$compute_tech_coeff()
# calculate the Leontief inverse
my_iom$compute_leontief_inverse()
# calculate key sectors
my_iom$compute_key_sectors()
# show the key sectors
my_iom$key_sectors
#> sector power_dispersion sensitivity_dispersion power_dispersion_cv
#> 1 1 0.9884106 0.9817881 1.617480
#> 2 2 1.0033113 1.0000000 1.602404
#> 3 3 1.0082781 1.0182119 1.588209
#> sensitivity_dispersion_cv key_sectors
#> 1 1.628303 Non-Key Sector
#> 2 1.607718 Strong Backward Linkage
#> 3 1.572583 Key Sector
## ------------------------------------------------
## Method `iom$compute_allocation_coeff`
## ------------------------------------------------
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new("test", intermediate_transactions, total_production)
# Calculate the allocation coefficients
my_iom$compute_allocation_coeff()
# show the allocation coefficients
my_iom$allocation_coefficients_matrix
#> 1 2 3
#> 1 0.01 0.040 0.07
#> 2 0.01 0.025 0.04
#> 3 0.01 0.020 0.03
## ------------------------------------------------
## Method `iom$compute_ghosh_inverse`
## ------------------------------------------------
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new("test", intermediate_transactions, total_production)
# Calculate the allocation coefficients
my_iom$compute_allocation_coeff()
# Calculate the Ghosh inverse
my_iom$compute_ghosh_inverse()
# show the Ghosh inverse
my_iom$ghosh_inverse_matrix
#> 1 2 3
#> 1 1.01129067 0.04302226 0.07475385
#> 2 0.01080908 1.02696918 0.04312928
#> 3 0.01064854 0.02161815 1.03258776
## ------------------------------------------------
## Method `iom$compute_hypothetical_extraction`
## ------------------------------------------------
# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
exports_data <- matrix(c(5, 10, 15), 3, 1)
holsehold_consumption_data <- matrix(c(20, 25, 30), 3, 1)
operating_income_data <- matrix(c(2, 5, 10), 1, 3)
taxes_data <- matrix(c(1, 2, 3), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new(
"test",
intermediate_transactions,
total_production,
exports = exports_data,
household_consumption = holsehold_consumption_data,
operating_income = operating_income_data,
taxes = taxes_data
)
# update value-added matrix
my_iom$update_value_added_matrix()
# update final demand matrix
my_iom$update_final_demand_matrix()
# calculate the technical coefficients
my_iom$compute_tech_coeff()
# calculate the Leontief inverse
my_iom$compute_leontief_inverse()
# calculate allocation coefficients
my_iom$compute_allocation_coeff()
# calculate Ghosh inverse
my_iom$compute_ghosh_inverse()
# calculate hypothetical extraction
my_iom$compute_hypothetical_extraction()
# show results
my_iom$hypothetical_extraction
#> backward_absolute backward_relative forward_absolute forward_relative
#> 1 -488.4068 -0.8140113 -575.6179 -0.9593631
#> 2 -489.6415 -0.8160692 -575.8020 -0.9596700
#> 3 -490.7084 -0.8178473 -576.0795 -0.9601325
#> total_absolute total_relative
#> 1 -1064.025 -1.773374
#> 2 -1065.444 -1.775739
#> 3 -1066.788 -1.777980
## ------------------------------------------------
## Method `iom$set_max_threads`
## ------------------------------------------------
if (FALSE) { # \dontrun{
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new("test", intermediate_transactions, total_production)
# to run single threaded (sequential)
my_iom$set_max_threads(1L)
} # }