Help for package decompr

Version:

6.9.0

Title:

Global Value Chain Decomposition

Description:

Four global value chain (GVC) decompositions are implemented. The Leontief decomposition derives the value added origin of exports by country and industry as in Hummels, Ishii and Yi (2001). The Koopman, Wang and Wei (2014) decomposition splits country-level exports into 9 value added components, and the Wang, Wei and Zhu (2013) decomposition splits bilateral exports into 16 value added components. The Borin and Mancini (2019) decomposition splits country-, sector- or bilateral-level exports into up to 13 value added and GVC components. Various GVC indicators based on these decompositions are computed in the complimentary 'gvc' package. — References: — Hummels, D., Ishii, J., & Yi, K. M. (2001). The nature and growth of vertical specialization in world trade. Journal of international Economics, 54(1), 75-96. Koopman, R., Wang, Z., & Wei, S. J. (2014). Tracing value-added and double counting in gross exports. American Economic Review, 104(2), 459-94. Wang, Z., Wei, S. J., & Zhu, K. (2013). Quantifying international production sharing at the bilateral and sector levels (No. w19677). National Bureau of Economic Research. Borin, A., & Mancini, M. (2019). Measuring What Matters in Global Value Chains and Value-Added Trade. World Bank Policy Research Working Paper 8804.

Maintainer:

Bastiaan Quast <bquast@gmail.com>

Depends:

R (≥ 3.5.0)

Imports:

matrixStats

License:

GPL-3

URL:

https://github.com/bquast/decompr

BugReports:

https://github.com/bquast/decompr/issues

Suggests:

gvc, testthat, knitr, rmarkdown

VignetteBuilder:

knitr

Encoding:

UTF-8

NeedsCompilation:

yes

Config/roxygen2/version:

8.0.0

RoxygenNote:

7.3.3

Packaged:

2026-06-09 10:14:04 UTC; bquast

Author:

Bastiaan Quast

[aut, cre], Fei Wang [aut], Victor Stolzenburg [aut], Oliver Reiter [ctb], Sebastian Krantz [ctb]

Repository:

CRAN

Date/Publication:

2026-06-09 11:10:08 UTC

Global Value Chain Decomposition

Description

Interface function for quick analysis

decomp()

Function to load ICIO table and create a 'decompr' object

load_tables_vectors()

Functions to perform GVC decompositions on a 'decompr' object

leontief()
kww()
wwz()
bm()

Function to obtain KWW decomposition from WWZ decomposition

wwz2kww()

Example ICIO data

data("leather")

Author(s)

Bastiaan Quast bquast@gmail.com
Fei Wang
Victor Stolzenburg
Sebastian Krantz

References

Hummels, D., Ishii, J., & Yi, K. M. (2001). The nature and growth of vertical specialization in world trade. Journal of international Economics, 54(1), 75-96.

Koopman, R., Wang, Z., & Wei, S. J. (2014). Tracing value-added and double counting in gross exports. American Economic Review, 104(2), 459-94.

Wang, Zhi, Shang-Jin Wei, and Kunfu Zhu (2013). Quantifying international production sharing at the bilateral and sector levels (No. w19677). National Bureau of Economic Research.

Borin, A., & Mancini, M. (2019). Measuring What Matters in Global Value Chains and Value-Added Trade. World Bank Policy Research Working Paper 8804.

Borin-Mancini Decomposition of Gross Exports

Description

Decomposes gross exports into value-added and Global Value Chain (GVC) components following the Borin and Mancini (2019) framework, as implemented in the Stata icio command (Belotti, Borin and Mancini 2021). It is the R counterpart of the decompose() function in the Julia package ICIO.jl, and operates on a decompr object created by load_tables_vectors.

Usage

bm(
  x,
  aggregation = c("country", "sector", "bilateral"),
  perspective = c("exporter", "world"),
  approach = c("source", "sink")
)

Arguments

x

an object of class decompr obtained from load_tables_vectors.

aggregation

character. The level of the decomposition: "country" (one row per exporting country), "sector" (one row per exporting country-industry), or "bilateral" (one row per exporting country-industry and importing country, excluding within-country flows). Default "country".

perspective

character. The accounting perspective defining the perimeter for double counting: "exporter" (exporting-country perspective, additive across sectors and destinations, 13 terms) or "world" (world perspective, 9 terms, only available for aggregation = "country"). Default "exporter".

approach

character. How double-counted items are allocated across shipments: "source" (value added recorded the first time it leaves the country of origin) or "sink" (the last time). "exporter" requires "source"; "world" requires "sink". Default "source".

Details

For the default exporter / source perspective the decomposition yields 13 terms; the world / sink perspective (country level only, the "corrected KWW" decomposition) yields 9. All terms are in the same units as the input-output table (e.g. millions of USD). The following accounting identities hold: GEXP = DC + FC, DC = DVA + DDC, FC = FVA + FDC, DVA = VAX + REF, and (exporter/source only) GVC = GVCB + GVCF = GEXP - DAVAX and GVCB = FC + DDC.

`GEXP`	Gross exports.
`DC` / `FC`	Domestic / foreign content.
`DVA` / `FVA`	Domestic / foreign value added.
`DDC` / `FDC`	Domestic / foreign double counting.
`VAX`	Domestic value added absorbed abroad (Johnson and Noguera 2012).
`REF`	Reflection: domestic value added returning home.
`DAVAX`	Domestic value added directly absorbed by the importer.
`GVC`	GVC-related trade (value added crossing more than one border).
`GVCB` / `GVCF`	Backward / forward GVC participation.

The exporter / source decomposition is additive: the "sector" result is the sum of the "bilateral" result over importers, and the "country" result is the sum of the "sector" result over industries.

Value

A data.frame with one row per unit and one column per value-added term, preceded by factor identifier columns: Exporting_Country (country); Exporting_Country, Exporting_Industry (sector); or Exporting_Country, Exporting_Industry, Importing_Country (bilateral). The attribute "decomposition" is set to "bm".

Author(s)

Sebastian Krantz

References

Borin, A. and Mancini, M. (2019). Measuring What Matters in Global Value Chains and Value-Added Trade. World Bank Policy Research Working Paper 8804.

Belotti, F., Borin, A. and Mancini, M. (2021). icio: Economic analysis with intercountry input-output tables. The Stata Journal, 21(3), 708-755.

Examples

# Load example data and create a 'decompr' object
data(leather)
dec <- load_tables_vectors(leather)

# Country-level decomposition (exporter perspective, source approach; 13 terms)
bm(dec)

# Country-level "corrected KWW" (world perspective, sink approach; 9 terms)
bm(dec, perspective = "world", approach = "sink")

# Sector- and bilateral-sector-level decompositions
bm(dec, aggregation = "sector")
bm(dec, aggregation = "bilateral")

Interface Function for Decompositions

Description

This function loads an ICIO table and runs a specified decomposition. It provides a compact interface for quick analysis.

Usage

decomp(
  iot,
  x,
  y,
  k,
  i,
  o = NULL,
  v = NULL,
  method = c("leontief", "kww", "wwz", "bm"),
  ...
)

Arguments

iot

a Input Output Table object - a list with elements 'inter' (= x), 'final' (= y), 'output' (= o), 'countries' (= k) and 'industries' (= i) of class 'iot'. Alternatively these objects can be passed directly to the function, at least x, y, k and i need to be supplied.

x

intermediate demand table supplied as a numeric matrix of dimensions GN x GN (G = no. of country, N = no. of industries). Both rows and columns should be arranged first by country, then by industry (e.g. C1I1, C1I2, ..., C2I1, C2I2, ...) and should match (symmetry), such that rows and columns refer to the same country-industries.

y

final demand table supplied as a numeric matrix of dimensions GN x MN (M = no. of final demand categories recorded for each country). The rows of y need to match the rows of x, and the columns should also be arranged first by country, then by final demand category (e.g. C1FD1, C1FD2, ..., C2FD1, C2FD2, ...) with the order of the countries the same as in x.

k

character. A vector of country or region names of length G, arranged in the same order as they occur in the rows and columns of x, y.

i

character. A vector of country or region names of length N, arranged in the same order as they occur in the rows and columns of x and rows of y.

o

numeric. A vector of final outputs for each country-industry matching the rows of x and y. If not provided it will be computed as rowSums(x) + rowSums(y).

v

numeric. A vector of value added for each country-industry matching the columns of x. If not provided it will be computed as o - colSums(x).

method

character. The decomposition method, either "leontief", "kww", "wwz" or "bm".

...

further arguments passed to leontief, kww, wwz or bm.

Details

For more detailed analysis with multiple decompositions consider using load_tables_vectors to create a 'decompr' class object and then run the decomposition functions leontief, kww and wwz on the object.

Value

Depends on the decomposition, see leontief, kww, wwz or bm.

Author(s)

Bastiaan Quast

References

Timmer, Marcel P. (ed) (2012), "The World Input-Output Database (WIOD): Contents Sources and Methods", WIOD Working Paper Number 10, downloadable at http://www.wiod.org/publications/papers/wiod10.pdf

Wang, Zhi, Shang-Jin Wei, and Kunfu Zhu (2013). Quantifying international production sharing at the bilateral and sector levels. No. w19677. National Bureau of Economic Research.

Examples

# Load leather example data
data(leather)

# Explore the data
str(leather)

## Decomposing gross exports:

# Perform the Leontief decomposition
decomp(leather, method = "leontief")

# Perform the KWW decomposition
decomp(leather, method = "kww")

# Perform the WWZ decomposition
decomp(leather, method = "wwz")

# Perform the Borin-Mancini decomposition
decomp(leather, method = "bm")
decomp(leather, method = "bm", aggregation = "bilateral")

Koopman-Wang-Wei Decomposition of Gross Exports

Description

This function performs the Koopman-Wang-Wei (2014) decomposition of a countries gross exports into 9 separate value added components.

Usage

kww(x)

Arguments

x

an object of the class 'decompr' obtained from load_tables_vectors.

Value

A data frame where a countries gross exports is decomposed into 9 components (columns), as detailed in Figure 1 of the AER paper:

Term			Description


DVA_FIN			Domestic VA in final goods exports.


DVA_INT			Domestic VA in intermediate exports absorbed by direct importers (used to produce a locally consumed final good).


DVA_INTrex			Domestic VA in intermediate exports reexported to third countries and absorbed there.


RDV_FIN			Domestic VA in intermediate exports that returns home via final imports.


RDV_INT			Domestic VA in intermediate exports that returns home via intermediate imports (used to produce a domestically consumed final good).


DDC			Double counted DVA in intermediate exports (arising from 2-way trade in intermediate goods).


FVA_FIN			Foreign VA in final goods exports.


FVA_INT			Foreign VA in intermediate exports.


FDC			Double counted FVA in intermediate exports (arising from 2-way trade in intermediate goods).

Note

The KWW decomposition is known to be biased. As shown by Borin and Mancini (2019), it systematically underestimates the foreign value added in exports – and correspondingly overstates foreign double counting – because the entire foreign content that the direct importer re-exports to third countries is classified as 'foreign double counted', including the part (value added generated in the importing country and re-exported onwards) that is never recorded as foreign value added in any other flow. KWW also overlooks the bilateral dimension of trade, so it cannot correctly split domestic value added between absorption by the direct importer and by third markets (hence indicators such as DAVAX cannot be derived from it). Borin and Mancini (2019) correct these issues using a sink-based, world-level perspective for the foreign content of exports; this corrected KWW decomposition is available as bm(x, perspective = "world", approach = "sink").

Author(s)

Sebastian Krantz

References

Koopman, R., Wang, Z., & Wei, S. J. (2014). Tracing value-added and double counting in gross exports. American Economic Review, 104(2), 459-94.

Borin, A., & Mancini, M. (2019). Measuring What Matters in Global Value Chains and Value-Added Trade. World Bank Policy Research Working Paper 8804.

Examples

# Load example data
data(leather)

# Create intermediate object (class 'decompr')
decompr_object <- load_tables_vectors(leather)
 
# Perform the KWW decomposition
kww(decompr_object)

Leather Example ICIO Data

Description

An example 3 x 3 ICIO table describing a GVC for leather products with industries 'Agriculture', 'Textile and Leather' and 'Transport Equipment' for the countries 'Argentina', 'Turkey' and 'Germany'.

Usage

data("leather")

Format

A list of class 'iot' with the following elements:

inter: 9 x 9 input output matrix where each column gives the value of inputs supplied to the corresponding country-industry by each row country-industry.
final: 9 x 3 final demand matrix showing the final demand in each country (column) for each country-industry's (rows) produce.
countries: character vector of country names (matching columns of final).
industries: character vector of industries, such that as.vector(t(outer(countries, industries, FUN = paste, sep = "."))) generates the row- and column-names of inter and the rownames of final.
out: A vector of gross country-industry output. In a complete productive system it should be equal to rowSums(inter) + rowSums(final).

Leontief Decomposition

Description

The Leontief decomposition of gross flows (exports, final demand, output) into their value added origins.

Usage

leontief(x, post = c("exports", "output", "final_demand", "none"), long = TRUE)

Arguments

x

an object of class decompr.

post

post-multiply the value added multiplier matrix [VB = V(I-A)^{-1}] with something to deduce the value added origins thereof. The default is "exports" VAE = V(I-A)^{-1}E, where E is a diagonal matrix with exports along the diagonal yielding the country-industry level sources of value added (rows) for each using (column) country-industry; similarly for "output". Option "final_demand" computes value added origins of final demand by source country-industry and importing country, by computing VAY = V(I-A)^{-1}Y where Y is the corresponding GN x G matrix contained in x. Option "none" just returns VB which gives the value added shares.

long

logical. Transform the output data into a long (tidy) data set or not, default is TRUE.

Details

The Leontief decomposition is obtained by pre-multiplying the flow measure (e.g. exports) with the value added multiplier matrix [VB = V(I-A)^{-1}], obtained by pre-multiplying the Leontief Inverse matrix [B = (I-A)^{-1}] with a diagonal matrix [V] containing the direct value added share in each industries output.

V is obtained as diag(v / o) where o is total industry output. v is either supplied to load_tables_vectors or computed as o - colSums(x) with x the raw IO matrix. If o is not supplied to load_tables_vectors, it is computed as rowSums(x) + rowSums(y) where y is the matrix of final demands. If both o and v are not supplied to load_tables_vectors, this is equivalent to computing V as diag(1 - colSums(A)), with A is the row-normalized IO matrix also used to compute the Leontief Inverse [B].

Value

If long = TRUE a molten data frame containing the elements of the decomposed flows matrix in the final column, preceded by several identifier columns. If long = FALSE the decomposed flows matrix is simply returned.

Author(s)

Bastiaan Quast

References

Leontief, W. (Ed.). (1986). Input-output economics. Oxford University Press.

Hummels, D., Ishii, J., & Yi, K. M. (2001). The nature and growth of vertical specialization in world trade. Journal of international Economics, 54(1), 75-96.

Wang, Zhi, Shang-Jin Wei, and Kunfu Zhu (2013). Quantifying international production sharing at the bilateral and sector levels (No. w19677). National Bureau of Economic Research.

Examples

# Load example data
data(leather)

# Create intermediate object (class 'decompr')
decompr_object <- load_tables_vectors(leather)

# Perform the Leontief decomposition of each country-industries 
# exports into their value added origins by country-industry
leontief(decompr_object)

Load the Input-Output and Final Demand Tables: Depreciated Interface

Description

This function loads the demand tables and defines all variables for the decomposition. It is kept in the package for backward compatibility. New users should use load_tables_vectors instead.

Usage

load_tables(x, y)

Arguments

x

the intermediate demand table, it has dimensions GN x GN (G = no. of country, N = no. of industries), excluding the first row and the first column which contains the country names, and the second row and second column which contain the industry names for each country. In addition, an extra row at the end should contain final demand.

y

the final demand table it has dimensions GN x MN, excluding the first row and the first column which contains the country names, the second column which contains the industry names for each country, and second row which contains the five decomposed final demands (M).

Details

Adapted from code by Fei Wang.

Value

A 'decompr' class object.

Author(s)

Bastiaan Quast

Load the Input-Output and Final Demand Tables

Description

This function loads the demand tables and creates all matrices and variables required for the GVC decompositions.

Usage

load_tables_vectors(
  iot,
  x,
  y,
  k,
  i,
  o = NULL,
  v = NULL,
  null_inventory = FALSE
)

Arguments

iot

x

y

k

character. A vector of country or region names of length G, arranged in the same order as they occur in the rows and columns of x, y.

i

character. A vector of industry names of length N, arranged in the same order as they occur in the rows and columns of x and rows of y.

o

numeric. A vector of final outputs for each country-industry matching the rows of x and y. If not provided it will be computed as rowSums(x) + rowSums(y).

v

numeric. A vector of value added for each country-industry matching the columns of x. If not provided it will be computed as o - colSums(x).

null_inventory

logical. TRUE sets the inventory (last final demand category for each country) to zero.

Details

Adapted from code by Fei Wang.

Value

A 'decompr' class object - a list with the following elements:

Am			Imported / Exported goods IO shares matrix (`x` column-normalized by output `o`, with domestic entries set to 0).
B			Leontief Inverse matrix `(I - A)^{-1}` where `A` is `x` column-normalized by output `o`.
Bd			Domestic part of Leontief Inverse matrix (inter-country elements of `B` set to 0, needed for WWZ decomposition).
Bm			Imported / Exported part of Leontief Inverse matrix (domestic elements of `B` set to 0, needed for WWZ decomposition).
L			Domestic economy Leontief Inverse matrix `(I - Ad)^{-1}` where `Ad` is `A` with all inter-country elements set to 0.
E			Total Exports (output of each country-industry servicing foreign production or foreign final demand).
ESR			Total Exports by destination country.
Eint			Exports for intermediate production by destination country.
Efd			Exports for final demand by destination country.
Vc			Value added content of output (`v / o`).
G			Number of countries.
N			Number of industries.
GN			Number of country-industries.
k			Vector of country names.
i			Vector of industry names.
rownam			Unique country-industry names identifying the rows / columns of x and rows of y.
X			Total Output ( `= o`).
Y			Total Final Demand by destination country.
Yd			Domestic Final Demand.
Ym			Foreign Final Demand.

Author(s)

Bastiaan Quast

Examples

# Load example data
data(leather)

# Create intermediate object (class 'decompr')
decompr_object <- load_tables_vectors(leather)

# Examine the object                                    
str(decompr_object)

Wang-Wei-Zhu Decomposition of Gross Exports

Description

This function performs the Wang-Wei-Zhu decomposition of country-sector level gross exports into 16 value added components by importing country.

Usage

wwz(x, verbose = FALSE)

Arguments

x

an object of the class 'decompr' obtained from load_tables_vectors.

verbose

logical, should timings of the calculation be displayed? Default is FALSE

Details

Adapted from code by Fei Wang.

Value

A data frame with exports decomposed into 16 components (columns), as detailed in Table E1 in the appendix of the paper, and additional diagnostic items:

Term			Description


DVA_FIN			Domestic VA in final goods exports.


DVA_INT			Domestic VA in intermediate exports used by direct importer to produce domestic final goods consumed at home.


DVA_INTrexI1			Domestic VA in intermediate exports used by the direct importer to produce intermediate exports for production of final goods in third countries that are then imported and consumed by the direct importer.


DVA_INTrexF			Domestic VA in intermediate exports used by the direct importer to produce final goods exports to third countries.


DVA_INTrexI2			Domestic VA in Intermediate exports used by the direct importer to produce intermediate exports to third countries.


RDV_INT			Domestic VA in intermediate exports that returns via intermediate imports (i.e. is used to produce a locally consumed final good).


RDV_FIN			Domestic VA in intermediate exports that returns home via final goods imports from the direct importer.


RDV_FIN2			Domestic VA in intermediate exports that returns home via in final goods imports from third countries.


OVA_FIN			Third countries’ VA in final goods exports.


MVA_FIN			Direct importer’s VA in final goods exports.


OVA_INT			Third countries’ VA in intermediate exports.


MVA_INT			Direct importer’s VA in intermediate exports.


DDC_FIN			Double counted Domestic VA used to produce final goods exports.


DDC_INT			Double counted Domestic VA used to produce intermediate exports.


ODC			Double counted third countries’ VA in home country’s exports production.


MDC			Double counted direct importer’s VA in home country’s exports production.






Diagnostic Item			Description


texp			Total Exports (matrix 'ESR' from `load_tables_vectors`).


texpint			Exports for intermediate production (matrix 'Eint' from `load_tables_vectors`).


texpfd			Exports for final demand (matrix 'Efd' from `load_tables_vectors`).


texpdiff			Difference between Total Exports and the sum of the 16 terms.


texpdiffpercent			... in percent of total exports.


texpfddiff			Difference between Final Exports and the sum of terms DVA_FIN, OVA_FIN and MVA_FIN.


texpfddiffpercent			... in percent of final exports.


texpintdiff			Difference between Intermediate Exports and the sum of all the remaining terms (except DVA_FIN, OVA_FIN and MVA_FIN).


texpintdiffpercent			... in percent of intermediate exports.


DViX_Fsr			DVA embodied in gross exports based on forward linkage.

Author(s)

Bastiaan Quast

References

Wang, Zhi, Shang-Jin Wei, and Kunfu Zhu (2013). Quantifying international production sharing at the bilateral and sector levels (No. w19677). National Bureau of Economic Research.

Examples

# Load example data
data(leather)

# Create intermediate object (class 'decompr')
decompr_object <- load_tables_vectors(leather)

# Perform the WWZ decomposition
wwz(decompr_object)

Koopman-Wang-Wei from Wang-Wei-Zhu Decomposition

Description

This function by default returns a disaggregated version of the the Koopman-Wang-Wei (KWW) decomposition breaking up sector-level gross exports into 9 value added terms, from an already computed and more detailed (16 term) Wang-Wei-Zhu decomposition of sector-level gross exports. An aggregation option also allows obtaining the aggregate KWW decomposition.

Usage

wwz2kww(x, aggregate = FALSE)

Arguments

x

a data frame with the WWZ decomposition obtained from wwz. Alternatively a 'decompr' class object from load_tables_vectors can be supplied, which will toggle calling wwz() first.

aggregate

logical. TRUE aggregates the KWW decomposition to the country level, giving exactly the same output as kww. FALSE maintains the sector level decomposition in KWW format.

Details

The mapping of the 16 terms in the WWZ decomposition to the 9 terms in the KWW decomposition is provided in table E2 in the appendix of the WWZ (2013) paper. The table is reproduced here using the term naming conventions followed in this package.

WWZ Terms	KWW Term	Description


DVA_FIN	DVA_FIN	Domestic VA in final goods exports.


DVA_INT, DVA_INTrexI1	DVA_INT	Domestic VA in intermediate exports absorbed by direct importers. WWZ separates VA in final goods produced and consumed by direct importer from VA used by direct importer to produce intermediate exports for production of domestically consumed final goods in third countries (i.e. the VA is absorbed by the direct importer, but it may be exported to third countries as intermediates first before returning to direct importer as final goods).


DVA_INTrexF, DVA_INTrexI2	DVA_INTrex	Domestic VA in intermediate exports reexported to third countries and absorbed there. WWZ separates VA in final goods exports of direct importer to third countries from VA in intermediate exports from direct importers to third countries (that is ultimately absorbed in third countries).


RDV_FIN, RDV_FIN2	RDV_FIN	Domestic VA in intermediate exports that returns home via final imports. WWZ separates final imports from the direct importer and third countries.


RDV_INT	RDV_INT	Domestic VA in intermediate exports that returns via intermediate imports (i.e. is used to produce a locally consumed final good).


DDC_FIN, DDC_INT	DDC	Double counted Domestic Value Added in gross exports. WWZ separates double counting due to final and intermediate exports production.


MVA_FIN, OVA_FIN	FVA_FIN	Foreign VA in final goods exports. WWZ separates FVA from direct importer and from third countries.


MVA_INT, OVA_INT	FVA_INT	Foreign VA in intermediate exports. WWZ separates FVA from direct importer and from third countries.


MDC, ODC	FDC	Double counted Foreign Value Added in gross exports. WWZ separates FDC from direct importer and from third countries.

Value

A data frame with exports decomposed into 9 components (columns), see the table above and kww for a shorter description of the 9 terms.

Note

If both WWZ and KWW decompositions are required, it is computationally more efficient to call wwz2kww(x, aggregate = TRUE) on an already computed WWZ decomposition, than to call kww on a 'decompr' object.

Author(s)

Sebastian Krantz

References

Koopman, R., Wang, Z., & Wei, S. J. (2014). Tracing value-added and double counting in gross exports. American Economic Review, 104(2), 459-94.

Wang, Zhi, Shang-Jin Wei, and Kunfu Zhu (2013). Quantifying international production sharing at the bilateral and sector levels (No. w19677). National Bureau of Economic Research.

Examples


# Load example data
data(leather)

# Create intermediate object (class 'decompr')
decompr_object <- load_tables_vectors(leather)
 
# Perform the WWZ decomposition
WWZ <- wwz(decompr_object)

# Obtain a disaggregated KWW decomposition
KWW <- wwz2kww(WWZ)

# Aggregate KWW 
wwz2kww(WWZ, aggregate = TRUE)

# Same as running KWW directly, but the former is more efficient 
# if we already have the WWZ
kww(decompr_object)

Package {decompr}

Global Value Chain Decomposition

Description

Contents

Author(s)

References

See Also

Borin-Mancini Decomposition of Gross Exports

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Interface Function for Decompositions

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Koopman-Wang-Wei Decomposition of Gross Exports

Description

Usage

Arguments

Value

Note

Author(s)

References

See Also

Examples

Leather Example ICIO Data

Description

Usage

Format

See Also

Leontief Decomposition

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Load the Input-Output and Final Demand Tables: Depreciated Interface

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Load the Input-Output and Final Demand Tables

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Wang-Wei-Zhu Decomposition of Gross Exports

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Koopman-Wang-Wei from Wang-Wei-Zhu Decomposition