krugman.spec: Krugman coefficient of regional specialization for two...

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

Description

Calculating the Krugman coefficient for the specialization of two regions based on regional industry data (normally employment data)

Usage

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krugman.spec(e_ij, e_il)

Arguments

e_ij

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

e_il

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

Details

The Krugman coefficient of regional specialization (K_{jl}) is a measure for the dissimilarity of the industrial structure of two regions (j and 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). The calculation is based on the formulae in Farhauer/Kroell (2013).

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.conc, krugman.conc2, krugman.spec2, locq

Examples

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# Example from Farhauer/Kroell (2013), modified:
E_ij <- c(20,10,70,0,0)
# employment of five industries in region j
E_il <- c(0,0,0,60,40)
# employment of five industries in region l
krugman.spec(E_ij, E_il)
# results the specialization coefficient (2)

# Example Goettingen:
data(Goettingen)
krugman.spec(Goettingen$Goettingen2017[2:16], Goettingen$BRD2017[2:16])
# Returns the Krugman coefficient of regional specialization 2017 (0.4508469)

Example output

[1] 2
[1] 0.4508469

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