krugman.spec2: Krugman coefficient of regional specialization for more than...

Description Usage Arguments Details Value Author(s) References See Also Examples

Description

Calculating the Krugman coefficient for the specialization of one region based on regional industry data (normally employment data) compared with a vector of other regions

Usage

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krugman.spec2(e_ij, e_il)

Arguments

e_ij

a numeric vector with the employment of the industries i in region j

e_il

a data frame with the employment of the industries i in l regions

Details

The Krugman coefficient of regional specialization (K_{jl}) is a measure for the dissimilarity of the industrial structure of regions (j and other regions, l) regarding the employment in the i industries in these regions. The coefficient K_{jl} varies between 0 (no specialization/same structure) and 2 (maximum difference, that means there is no single industry localized in both regions).

Value

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

Author(s)

Thomas Wieland

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.spec, krugman.conc, krugman.conc2, locq

Examples

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# Example from Farhauer/Kroell (2013):
Sweden <- c(45000, 15000, 32000, 10000, 30000)
Norway <- c(35000, 12000, 30000, 8000, 22000)
Denmark <- c(40000, 10000, 25000, 9000, 18000)
Finland <- c(30000, 11000, 18000, 3000, 13000)
Island <- c(40000, 6000, 11000, 2000, 12000)
# industry jobs in five industries for five countries
countries <- data.frame(Norway, Denmark, Finland, Island)
# data frame with all comparison countries
krugman.spec2(Sweden, countries)
# returns the Krugman coefficient for the specialization
# of sweden compared to Norway, Denmark, Finland and Island
# 0.1595

REAT documentation built on Nov. 21, 2019, 5:08 p.m.