ioanalysis (version 0.3.4)

key.sector: Impact Analysis via Backward and Forward Linkages

Description

Uses backward and forward linkages to identify key sectors in the system. Can calculate total and direct linkages. If the data is multiregional, intraregional and interregional linkages can be calculated. Can also be used on a specified subset of all regions.

Usage

key.sector(io, ES = NULL, crit = 1, regions = "all", sectors = "all", 
           type = c("direct"), intra.inter = FALSE)

Arguments

io

An object of class InputOutput calculated from as.inputoutput.

ES

An object of class EasySelect from easy.select

crit

Integer. The value to compare linkages above or below to classify sectors. Default is 1.

regions

Character or Integer. Specific regions to be used. Can either be a character that exactly matches the name of the region in RS_label or the number of the region in the order it appears in RS_label.

sectors

Character or Integer. Specific sectors to be used. Can either be a character that exactly matches the name of the sector in RS_label or the number of the sector in the order it RS_label.

type

Character. Identifying the type of backward and forward linkages to be calculated. Options are "total" and "direct".

intra.inter

Logical. Only applies to multiregional systems. Determines whether or not to calculate intraregional and interregional backward and forward linkages in addition to aggregate linkages.

Value

If there is only one region, key sector binds to the output from linkages to make a table. Otherwise, it produces a list of key sector codes for each country using the names of regions provided. See Examples for more details.

Details

Uses the (various) specified backward and forward linkages to calculate a key to identify dependence using the specified critical value.

I BL < crit, FL < crit - Generally independent

II BL < crit, FL > crit - Dependent on interindustry demand

III BL > crit, FL > crit - Generally dependent

IV BL > crit, FL < crit - Dependent on interindustry supply

References

Blair, P.D. and Miller, R.E. (2009). "Input-Output Analysis: Foundations and Extensions". Cambridge University Press

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)

See Also

linkages, as.inputoutput

Examples

# NOT RUN {
data(toy.IO)
class(toy.IO)
key1 <- key.sector(toy.IO)
key1$Narnia

# A more detailed example
# Using critical value of 2 because this is randomly generated data and better 
# illustrates functionality
key2 <- key.sector(toy.IO, intra.inter = TRUE, type = c("direct"), crit = 2)
key2

key3 <- key.sector(toy.IO, regions = c(1:2), sectors = c(1:3,5))
key3
# }