The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Type: Package
Title: Global Value Chain Decomposition for Value-Added Trade
Version: 0.1.1
Description: Provides tools for decomposing Global Value Chain (GVC) participation and value-added trade. It implements the frameworks proposed by Borin and Mancini (2023) 10.1080/09535314.2022.2153221> for source-based and sink-based decompositions, and by Borin, Mancini, and Taglioni (2025) 10.1093/wber/lhaf017> for tripartite and output-based GVC measures.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Depends: R (≥ 4.0.0)
Imports: Matrix, methods, stats
Suggests: knitr, rmarkdown, testthat (≥ 3.0.0)
VignetteBuilder: knitr
RoxygenNote: 7.3.3
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2025-12-02 09:36:15 UTC; ballavbabu
Author: Lila Ballav Bhusal ORCID iD [aut, cre], Alessandro Borin [ctb] (Methodology: Borin and Mancini (2023); Borin, Mancini and Taglioni (2025)), Michele Mancini [ctb] (Methodology: Borin and Mancini (2023); Borin, Mancini and Taglioni (2025)), Daria Taglioni [ctb] (Methodology: Borin, Mancini and Taglioni (2025))
Maintainer: Lila Ballav Bhusal <krish.bhula@gmail.com>
Repository: CRAN
Date/Publication: 2025-12-09 07:40:01 UTC

BM_2023 pure bilateral decomposition of exports from s to r

Description

BM_2023 pure bilateral decomposition of exports from s to r

Usage

bm_2023_bilateral_pure(io, s, r)

Arguments

io

A bm_io object.

s

Exporter country (name or index).

r

Importer country (name or index).

Value

A data frame with the pure bilateral value-added decomposition.

BM_2023 pure bilateral (/sr) decomposition for all pairs

Description

BM_2023 pure bilateral (/sr) decomposition for all pairs

Usage

bm_2023_bilateral_pure_all(io)

Arguments

io

A bm_io object.

Value

Data frame of pure bilateral decomposition for all pairs.

BM_2023 sink-based bilateral decomposition of exports from s to r

Description

BM_2023 sink-based bilateral decomposition of exports from s to r

Usage

bm_2023_bilateral_sink(io, s, r)

Arguments

io

A bm_io object.

s

Exporter country (name or index).

r

Importer country (name or index).

Value

A data frame with the sink-based value-added decomposition.

BM_2023 sink-based bilateral decomposition for all pairs

Description

BM_2023 sink-based bilateral decomposition for all pairs

Usage

bm_2023_bilateral_sink_all(io)

Arguments

io

A bm_io object.

Value

Data frame of sink-based decomposition for all pairs.

BM_2023 source-based bilateral decomposition of exports from s to r

Description

BM_2023 source-based bilateral decomposition of exports from s to r

Usage

bm_2023_bilateral_source(io, s, r)

Arguments

io

A bm_io object.

s

Exporter country (name or index).

r

Importer country (name or index).

Value

A data frame with the source-based value-added decomposition.

BM_2023 source-based bilateral decomposition for all pairs

Description

BM_2023 source-based bilateral decomposition for all pairs

Usage

bm_2023_bilateral_source_all(io)

Arguments

io

A bm_io object.

Value

Data frame of source-based decomposition for all pairs.

BM_2023 exporter-perspective decomposition of total exports of s

Description

BM_2023 exporter-perspective decomposition of total exports of s

Usage

bm_2023_exporter_total(io, s)

Arguments

io

A bm_io object.

s

Exporter country (name or index).

Value

A data frame with the exporter-total decomposition.

BM_2023 exporter totals for all countries

Description

BM_2023 exporter totals for all countries

Usage

bm_2023_exporter_total_all(io)

Arguments

io

A bm_io object.

Value

Data frame of exporter totals for all countries.

BM_2025 output-based GVC components by exporter

Description

BM_2025 output-based GVC components by exporter

Usage

bm_2025_output_components(io)

Arguments

io

A bm_io object.

Value

Data frame with output-based GVC components.

BM 2025 output components by country and sector

Description

BM 2025 output components by country and sector

Usage

bm_2025_output_components_sector(io)

Arguments

io

A bm_io object.

Value

Data frame with sectoral output components.

BM_2025 output-based GVC participation indicators

Description

BM_2025 output-based GVC participation indicators

Usage

bm_2025_output_measures(io)

Arguments

io

A bm_io object.

Value

Data frame with output-based GVC participation measures.

BM 2025 output participation measures by country and sector

Description

BM 2025 output participation measures by country and sector

Usage

bm_2025_output_measures_sector(io)

Arguments

io

A bm_io object.

Value

Data frame with sectoral GVC measures.

BM_2025 exporter-level GVC trade totals

Description

BM_2025 exporter-level GVC trade totals

Usage

bm_2025_trade_exporter(io)

Arguments

io

A bm_io object.

Value

Data frame of exporter totals.

BM_2025 trade-based GVC participation indicators

Description

BM_2025 trade-based GVC participation indicators

Usage

bm_2025_trade_measures(io)

Arguments

io

A bm_io object.

Value

Data frame of trade-based indicators.

BM_2025 tripartite GVC trade decomposition for one pair (s,r)

Description

BM_2025 tripartite GVC trade decomposition for one pair (s,r)

Usage

bm_2025_tripartite_trade(io, s, r)

Arguments

io

A bm_io object.

s

Exporter country (name or index).

r

Importer country (name or index).

Value

Data frame for the pair (s,r).

BM_2025 tripartite GVC trade decomposition for all pairs

Description

BM_2025 tripartite GVC trade decomposition for all pairs

Usage

bm_2025_tripartite_trade_all(io)

Arguments

io

A bm_io object.

Value

Data frame for all pairs.

Build a bm_io object from IO table blocks

Description

Build a bm_io object from IO table blocks

Usage

bm_build_io(Z, Y, VA, X, countries, sectors)

Arguments

Z

Intermediate demand matrix (GN x GN).

Y

Final demand matrix. Can be (GN x G) OR (GN x (G * FD_categories)).

VA

Value added. Can be a vector (length GN) or matrix (Rows x GN).

X

Output vector (length GN).

countries

Character vector of country names/codes (length G).

sectors

Character vector of sector names/codes (length N).

Value

An object of class "bm_io".

Exports from s to r (sectoral)

Description

Exports from s to r (sectoral)

Usage

bm_get_e_sr(io, exporter, importer)

Arguments

io

bm_io object.

exporter

Exporter country (name or index).

importer

Importer country (name or index).

Value

Numeric vector of exports.

Total exports of s to all foreign destinations

Description

Total exports of s to all foreign destinations

Usage

bm_get_e_star(io, exporter)

Arguments

io

bm_io object.

exporter

Exporter country (name or index).

Value

Numeric vector of total exports.

Toy 4-country, 3-sector IO table for bmGVC

Description

A small multi-country input–output data set used in bmGVC examples and vignettes. It contains four countries (China, India, Japan, ROW) and three sectors (Primary, Manufacturing, Service).

Format

bm_toy_Z

numeric matrix 12 x 12

bm_toy_Y

numeric matrix 12 x 4

bm_toy_VA

numeric vector of length 12

bm_toy_X

numeric vector of length 12

bm_toy_countries

character vector of length 4

bm_toy_sectors

character vector of length 3

Details

The data are stored in six objects:

The ordering of industries is (China P,M,S; India P,M,S; Japan P,M,S; ROW P,M,S).

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.