ioanalysis (version 0.3.4)

output.decomposition: Decomposition of Output Changes

Description

Performs decomposition of output changes given two periods of data. You can decompose by origin over internal, external, or total and you can additionally decompose by changes due to final demand, technical change, or total. This follows the technique of Sonis et al (1996).

Usage

output.decomposition(io1, io2, origin = "all", cause = "all")

Arguments

io1

The first period InputOutput class object from as.inputoutput

io2

An InputOutput class object from as.inputoutput

origin

Character. Choosing to decompose changes to the sectors due to internal changes, external changes, and/or total

cause

Character. Choosing to decompose changes to the sectors due to changes in fianldemand (f), technical changes leontief (L), or total changes

Value

The function always outputs a named row of some variant of delta.X. A prefix indicates the changes origin where total is blank. A suffix indicates the cause of the change where total is also blank.

int

A prefix for internal

ext

A prefix for external

f

A suffix for final demand

L

A suffix for technical or Leontief

Details

A superscript of \(f\) indicates changes due to final demand, \(l\) indicates changes due to the Leontief inverse, and no superscript indicates total. A subscript of \(s\) indicates changes in output originating internally of the sectors, \(n\) indicates externally, and no subscript indicates total. \(L\) is the Leontief inverse and \(f\) is aggregated final demand. Analysis is over changes from period 1 to period 2. The values are calculated as follows:

Originating: Total $$\Delta X^f = L_1\Delta f$$ $$\Delta X^l = \Delta L f_1$$ $$\Delta X = \Delta L \Delta f$$ Originating: Internal $$\Delta X_s^f = diag(L_1)\Delta f$$ $$\Delta X_s^l = diag(\Delta L) f_1$$ $$\Delta X_s = diag(\Delta L) \Delta f$$ Originating: External $$\Delta X_n^f = \Delta X^f - \Delta X_s^f$$ $$\Delta X_n^l = \Delta X^l - \Delta X_s^l$$ $$\Delta X_n = \Delta X - \Delta x_s$$

References

Nazara, Suahasil & Guo, Dong & Hewings, Geoffrey J.D., & Dridi, Chokri, 2003. PyIO. Input-Output Analysis with Python. REAL Discussion Paper 03-t-23. University of Illinois at Urbana-Champaign. (http://www.real.illinois.edu/d-paper/03/03-t-23.pdf)

Sonis, Michael & Geoffrey JD Hewings, & Jiemin Guo. Sources of structural change in input-output systems: a field of influence approach. Economic Systems Research 8, no. 1 (1996): 15-32.

See Also

as.inputoutput

Examples

# NOT RUN {
data(toy.IO)
data(toy.IO2)
class(toy.IO)
class(toy.IO) == class(toy.IO2)

OD1 <- output.decomposition(toy.IO, toy.IO2)
OD1$Hogwarts

OD2 <- output.decomposition(toy.IO, toy.IO2, origin = "external", 
                            cause = c("finaldemand","leontief"))
OD2
# }