REAT (version 3.0.2)

locq: Location quotient

Description

Calculating the location quotient (a.k.a. Hoover-Balassa quotient)

Usage

locq(e_ij, e_j, e_i, e, industry.names = NULL, plot.results = FALSE,
LQ.method = "m", plot.title = "Localization quotients", 
bar.col = "lightblue", line.col = "red", arg.size = 1)

Arguments

e_ij

a single numeric value or vector with the employment of industry/industries \(i\) in region \(j\)

e_j

a single numeric value with the over-all employment in region \(j\)

e_i

a single numeric value or vector with the over-all employment in industry/industries \(i\)

e

a single numeric value with the over-all employment in all regions

industry.names

Industry names (e.g. from the relevant statistical classification of economic activities)

plot.results

Logical argument that indicates if the results have to be plotted (only available if \(i > 1\))

LQ.method

Indicates whether the multiplicative (default: LQ.method = "m") or the additive LQ (LQ.method = "m") is computed

plot.title

If plot.results = TRUE: Plot title

bar.col

If plot.results = TRUE: Bar colour

line.col

If plot.results = TRUE: LQ1-line colour

arg.size

If plot.results = TRUE: Size of industry names in bar plot

Value

A single numeric value of (\(LQ\)) or a matrix with respect to all \(i\) industries. Optional: plot.

Details

The location quotient is a simple measure for the concentration of an industry (\(i\)) in a region (\(j\)) and is also the mathematical basis for other related indicators in regional economics (e.g. gini.conc()). The function returns the value \(LQ\) which is equal to 1 if the concentration of the regarded industry is exactly the same as the over-all concentration (that means, it is proportionally represented in region \(j\)). If the value of \(LQ\) is smaller (bigger) than 1, the industry is underrepresented (overrepresented). The function checks the input values for errors (i.e. if employment in a region is bigger than over-all employment).

References

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

Hoen A.R./Oosterhaven, J. (2006): “On the measure of comparative advantage”. In: The Annals of Regional Science, 40, 3, p. 677-691.

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.

O'Donoghue, D./Gleave, B. (2004): “A Note on Methods for Measuring Industrial Agglomeration”. In: Regional Studies, 38, 4, p. 419-427.

Tian, Z. (2013): “Measuring agglomeration using the standardized location quotient with a bootstrap method”. In: Journal of Regional Analysis and Policy, 43, 2, p. 186-197.

See Also

gini.conc, gini.spec, locq2

Examples

# NOT RUN {
# Example from Farhauer/Kroell (2013):
locq (1714, 79006, 879213, 15593224)
# returns the location quotient (0.3847623)

# Location quotients for Goettingen 2017:
data(Goettingen)
locq (Goettingen$Goettingen2017[2:16], Goettingen$Goettingen2017[1], 
Goettingen$BRD2017[2:16], Goettingen$BRD2017[1])
# }