# locq: Location quotient In REAT: Regional Economic Analysis Toolbox

## Description

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

## Usage

 ```1 2 3``` ```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

## 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).

## Value

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

Thomas Wieland

## 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.

`gini.conc`, `gini.spec`, `locq2`

## Examples

 ```1 2 3 4 5 6 7 8``` ```# 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]) ```

### Example output

```[1] 0.3847623
[1] 0.08407652 0.40085663 0.53687366 0.34366928 0.74603541 0.67117311
[7] 0.98141916 0.91654277 0.82650178 1.53027645 0.95843423 1.03509027
[13] 2.77790858 1.67459967 0.35317012
```

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