REAT (version 3.0.3)

krugman.conc2: Krugman coefficient of spatial industry concentration for more than two industries

Description

Calculating the Krugman coefficient for the spatial concentration of an industry based on regional industry data (normally employment data) compared with a vector of other industries

Usage

krugman.conc2(e_ij, e_uj)

Arguments

e_ij

a numeric vector with the employment of the industry \(i\) in regions \(j\)

e_uj

a data frame with the employment of the industry \(u\) in \(j\) regions

Value

A single numeric value (\(0 < K_{i} < 2\))

Details

The Krugman coefficient of industry concentration (\(K_{i}\)) is a measure for the dissimilarity of the spatial structure of one industry (\(i\)) compared to several others (\(u\)) regarding the employment in the \(j\) regions. The coefficient \(K_{iu}\) varies between 0 (no concentration/same structure) and 2 (maximum difference, that means a complete other spatial structure of the industry compared to the others). The calculation is based on the formulae in Farhauer/Kroell (2013).

References

Farhauer, O./Kroell, A. (2013): “Standorttheorien: Regional- und Stadtoekonomik in Theorie und Praxis”. Wiesbaden : Springer.

Nakamura, R./Morrison Paul, C. J. (2009): “Measuring agglomeration”. In: Capello, R./Nijkamp, P. (eds.): Handbook of Regional Growth and Development Theories. Cheltenham: Elgar. p. 305-328.

See Also

gini.conc, gini.spec, krugman.conc, krugman.spec, krugman.spec2, locq

Examples

# NOT RUN {
# Example from Farhauer/Kroell (2013):
Chemie <- c(20000,11000,31000,8000,20000)
Sozialwesen <- c(40000,10000,25000,9000,16000)
Elektronik <- c(10000,11000,14000,14000,13000)
Holz <- c(7000,7500,11000,1500,36000)
Bergbau <- c(4320, 7811, 3900, 2300, 47560)
# five industries
industries <- data.frame(Chemie, Sozialwesen, Elektronik, Holz)
# data frame with all comparison industries
krugman.conc2(Bergbau, industries)
# returns the Krugman coefficient for the concentration
# of the mining industry (Bergbau) compared to 
# chemistry (Chemie), social services (Sozialwesen), 
# electronics (Elektronik) and wood industry (Holz)
# 0.8619
# }