REAT (version 3.0.3)

locq2: Location quotient

Description

Calculating the location quotient (a.k.a. Hoover-Balassa quotient) for a given number of \(I\) industries and \(J\) regions

Usage

locq2(e_ij, industry.id, region.id, LQ.norm = "none", 
LQ.output = "mat", na.rm = TRUE)

Arguments

e_ij

a vector with the employment of industry \(i\) in region \(j\)

industry.id

a vector containing the IDs of the industries \(i\)

region.id

a vector containing the IDs of the regions \(j\)

LQ.norm

Type of normalization of the location quotients: no normalization (default: LQ.norm = "none"), z values (LQ.norm = "OG") or z values of logged location quotients (LQ.norm = "T")

LQ.output

Type of output: matrix (default: CI.output = "mat") or data frame (CI.output = "df")

na.rm

logical argument that indicates whether NA values should be excluded before computing results

Value

A matrix or data frame containing \(IxJ\) values of \(LQ\)

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).

Two types of normalization are available: z values of the location quotients (O'Donoghue/Gleave 2004) or z values of logged location quotients (Tian 2013).

References

Farhauer, O./Kroell, A. (2014): “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

litzenberger, gini.conc, gini.spec, locq, hoover, ellison.a, ellison.a2, ellison.c, ellison.c2

Examples

# NOT RUN {
data (G.regions.industries)

lqs <- locq2(e_ij = G.regions.industries$emp_all, 
G.regions.industries$ind_code, G.regions.industries$region_code, 
LQ.output = "df")
# output as data frame

lqs_sort <- lqs[order(lqs$LQ, decreasing = TRUE),]
# Sort decreasing by size of LQ

lqs_sort[1:5,]
# }