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 $I x J$ values of $L Q$

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 $L Q$ 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 $L Q$ 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.

Examples

Run this code

# 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,]
# }

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