krugman.conc2 {REAT} | R Documentation |
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
krugman.conc2(e_ij, e_uj)
e_ij |
a numeric vector with the employment of the industry |
e_uj |
a data frame with the employment of the industry |
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).
A single numeric value (0 < K_{i} < 2
)
Thomas Wieland
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.
gini.conc
, gini.spec
, krugman.conc
, krugman.spec
, krugman.spec2
, locq
# 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