morc: Compute an index of knowledge complexity of regions using the...

In EconGeo: Computing Key Indicators of the Spatial Distribution of Economic Activities

Compute an index of knowledge complexity of regions using the method of reflection

Description

This function computes an index of knowledge complexity of regions using the method of reflection from regions - industries (incidence) matrices. The index has been developed by Hidalgo and Hausmann (2009) for country - product matrices and adapted by Balland and Rigby (2016) to city - technology matrices.

Usage

morc(mat, rca = FALSE, steps = 20)

Arguments

mat	An incidence matrix with regions in rows and industries in columns
rca	Logical; should the index of relative comparative advantage (RCA - also refered to as location quotient) first be computed? Defaults to FALSE (a binary matrix - 0/1 - is expected as an input), but can be set to TRUE if the index of relative comparative advantage first needs to be computed
steps	Number of iteration steps. Defaults to 20, but can be set to 0 to give diversity (number of industry in which a region has a RCA), to 1 to give the average ubiquity of the industries in which a region has a RCA, to 2 to give the average diversity of regions that have similar industrial structures, or to any other number of steps < or = to 22. Note that above steps = 2 the index will be rescaled from 0 (minimum relative complexity) to 100 (maximum relative complexity).

Value

If 'steps' is set to 0, the function returns a numeric vector representing the diversification of regions. Otherwise, it returns

Author(s)

Pierre-Alexandre Balland [e-mail]

References

Hidalgo, C. and Hausmann, R. (2009) The building blocks of economic complexity, Proceedings of the National Academy of Sciences 106: 10570 - 10575.

Balland, P.A. and Rigby, D. (2017) The Geography of Complex Knowledge, Economic Geography 93 (1): 1-23.

See Also

location_quotient, ubiquity, diversity, kci, tci, mort

Examples

## generate a region - industry matrix with full count
set.seed(31)
mat <- matrix(sample(0:10, 20, replace = TRUE), ncol = 4)
rownames(mat) <- c("R1", "R2", "R3", "R4", "R5")
colnames(mat) <- c("I1", "I2", "I3", "I4")

## run the function
morc(mat, rca = TRUE)
morc(mat, rca = TRUE, steps = 0)
morc(mat, rca = TRUE, steps = 1)
morc(mat, rca = TRUE, steps = 2)

## generate a region - industry matrix in which cells represent the presence/absence of an RCA
set.seed(32)
mat <- matrix(sample(0:1, 20, replace = TRUE), ncol = 4)
rownames(mat) <- c("R1", "R2", "R3", "R4", "R5")
colnames(mat) <- c("I1", "I2", "I3", "I4")

## run the function
morc(mat)
morc(mat, steps = 0)
morc(mat, steps = 1)
morc(mat, steps = 2)

## generate the simple network of Hidalgo and Hausmann (2009) presented p.11 (Fig. S4)
countries <- c("C1", "C1", "C1", "C1", "C2", "C3", "C3", "C4")
products <- c("P1", "P2", "P3", "P4", "P2", "P3", "P4", "P4")
my_data <- data.frame(countries, products)
my_data$freq <- 1
mat <- get_matrix(my_data)

## run the function
morc(mat)
morc(mat, steps = 0)
morc(mat, steps = 1)
morc(mat, steps = 2)

EconGeo documentation built on July 9, 2023, 6:58 p.m.