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icio

License CRAN Version R build status

icio decomposes gross exports from inter-country input-output (ICIO) tables into value-added and double-counting components, implementing four decompositions from the global value chain (GVC) literature:

Function Method Level Terms
bm() Borin & Mancini (2019) country / sector / bilateral up to 13
leontief() Hummels, Ishii & Yi (2001) country × industry continuous VA origin
kww() Koopman, Wang & Wei (2014) country 9
wwz() Wang, Wei & Zhu (2013) bilateral country × sector 16

bm() is the recommended state-of-the-art decomposition and reproduces the Stata icio command (Belotti, Borin & Mancini 2021). It also provides a corrected version of the KWW decomposition (perspective = "world", approach = "sink"), which fixes a known systematic bias in kww().

Installation 

# install.packages("remotes")
remotes::install_github("SebKrantz/icio")

Usage 

library(icio)

# The built-in 3x3 leather-sector ICIO table
data(leather)

# Build an 'icio' object: the expensive step (inverting I - A) is done once here
m <- load_icio(leather)

# ... or from the raw matrices, or from the Stata 'icio' CSV format
m <- load_icio(leather$inter, leather$final, leather$countries, leather$industries)
m <- load_icio_csv("EM_2015.csv", "EM_countrylist.csv")

# Borin-Mancini (2019): up to 13 terms, exporter perspective
bm(m)
bm(m, aggregation = "sector")
bm(m, aggregation = "bilateral", approach = "sink")

# Corrected KWW (world / sink perspective)
bm(m, perspective = "world", approach = "sink")

# Importer-perspective decomposition of gross imports
bm(m, flow = "imports")

# The other decompositions
leontief(m)
kww(m)
wwz(m)

# Unified interface, also for several tables at once (e.g. one per year)
decomp(m, aggregation = "bilateral")
decomp(list(`2015` = m, `2016` = m), method = "kww", idcol = "Year")

All decompositions return a data.table. See vignette("icio") for a detailed walk-through.

  • icio (Stata) — the reference implementation of the Borin-Mancini decompositions, by Belotti, Borin & Mancini (2021), and the origin of this package’s name. bm() reproduces its output; its perspective() and approach() options map onto the arguments of the same name here.
  • GlobalValueChains.jl (Julia) — the counterpart to this package; bm() mirrors its decompose().
  • icio is derived from the CRAN package decompr, which is no longer maintained.

References

  • Borin, A., & 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., & Mancini, M. (2021). icio: Economic analysis with inter-country input-output tables. The Stata Journal, 21(3), 708–755.
  • Koopman, R., Wang, Z., & Wei, S.-J. (2014). Tracing value-added and double counting in gross exports. American Economic Review, 104(2), 459–494.
  • Wang, Z., Wei, S.-J., & Zhu, K. (2013). Quantifying International Production Sharing at the Bilateral and Sector Levels. NBER Working Paper 19677.
  • 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.