REAT (version 1.3.2)

krugman.conc2:

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

# 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