gini.conc: Gini coefficient of spatial industry concentration

In REAT: Regional Economic Analysis Toolbox

Description

Calculating the Gini coefficient of spatial industry concentration based on regional industry data (normally employment data)

Usage

gini.conc(e_ij, e_j, lc = FALSE, lcx = "% of objects", 
lcy = "% of regarded variable", lctitle = "Lorenz curve", 
le.col = "blue", lc.col = "black", lsize = 1, ltype = "solid",
bg.col = "gray95", bgrid = TRUE, bgrid.col = "white", 
bgrid.size = 2, bgrid.type = "solid", lcg = FALSE, lcgn = FALSE, 
lcg.caption = NULL, lcg.lab.x = 0, lcg.lab.y = 1, 
add.lc = FALSE, plot.lc = TRUE)

Arguments

e_ij	a numeric vector with the employment of the industry i in region j
e_j	a numeric vector with the employment in region j
lc	logical argument that indicates if the Lorenz curve is plotted additionally (default: lc = FALSE, so no Lorenz curve is displayed)
lcx	if lc = TRUE (plot of Lorenz curve), lcx defines the x axis label
lcy	if lc = TRUE (plot of Lorenz curve), lcy defines the y axis label
lctitle	if lc = TRUE (plot of Lorenz curve), lctitle defines the overall title of the Lorenz curve plot
le.col	if lc = TRUE (plot of Lorenz curve), le.col defines the color of the diagonale (line of equality)
lc.col	if lc = TRUE (plot of Lorenz curve), lc.col defines the color of the Lorenz curve
lsize	if lc = TRUE (plot of Lorenz curve), lsize defines the size of the lines (default: 1)
ltype	if lc = TRUE (plot of Lorenz curve), ltype defines the type of the lines (default: "solid")
bg.col	if lc = TRUE (plot of Lorenz curve), bg.col defines the background color of the plot (default: "gray95")
bgrid	if lc = TRUE (plot of Lorenz curve), the logical argument bgrid defines if a grid is shown in the plot
bgrid.col	if lc = TRUE (plot of Lorenz curve) and bgrid = TRUE (background grid), bgrid.col defines the color of the background grid (default: "white")
bgrid.size	if lc = TRUE (plot of Lorenz curve) and bgrid = TRUE (background grid), bgrid.size defines the size of the background grid (default: 2)
bgrid.type	if lc = TRUE (plot of Lorenz curve) and bgrid = TRUE (background grid), bgrid.type defines the type of lines of the background grid (default: "solid")
lcg	if lc = TRUE (plot of Lorenz curve), the logical argument lcg defines if the non-standardized Gini coefficient is displayed in the Lorenz curve plot
lcgn	if lc = TRUE (plot of Lorenz curve), the logical argument lcgn defines if the standardized Gini coefficient is displayed in the Lorenz curve plot
lcg.caption	if lcg = TRUE (displaying the Gini coefficient in the plot), lcg.caption specifies the caption above the coefficients
lcg.lab.x	if lcg = TRUE (displaying the Gini coefficient in the plot), lcg.lab.x specifies the x coordinate of the label
lcg.lab.y	if lcg = TRUE (displaying the Gini coefficient in the plot), lcg.lab.y specifies the y coordinate of the label
add.lc	if lc = TRUE (plot of Lorenz curve), add.lc specifies if a new Lorenz curve is plotted (add.lc = "FALSE") or the plot is added to an existing Lorenz curve plot (add.lc = "TRUE")
plot.lc	logical argument that indicates if the Lorenz curve itself is plotted (if plot.lc = FALSE, only the line of equality is plotted))

Details

The Gini coefficient of spatial industry concentration (G_{i}) is a special spatial modification of the Gini coefficient of inequality (see the function gini()). It represents the rate of spatial concentration of the industry i referring to j regions (e.g. cities, counties, states). The coefficient G_{i} varies between 0 (perfect distribution, respectively no concentration) and 1 (complete concentration in one region). Optionally a Lorenz curve is plotted (if lc = TRUE).

Value

A single numeric value (0 < G_{i} < 1)

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, gini.spec

Examples

# Example from Farhauer/Kroell (2013):
E_ij <- c(500,500,1000,7000,1000)
# employment of the industry in five regions
E_j <- c(20000,15000,20000,40000,5000)
# employment in the five regions
gini.conc (E_ij, E_j)
# Returns the Gini coefficient of industry concentration (0.4068966)

data(G.regions.emp)
# Concentration of construction industry in Germany
# based on 16 German regions (Bundeslaender) for the year 2008
construction2008 <- G.regions.emp[(G.regions.emp$industry == "Baugewerbe (F)" | 
G.regions.emp$industry == "Insgesamt") & G.regions.emp$year == "2008",]
# only data for construction industry (Baugewerbe) and all-over (Insgesamt)
# for the 16 German regions in the year 2008
construction2008 <- construction2008[construction2008$region != "Insgesamt",]
# delete all-over data for all industries
gini.conc(construction2008[construction2008$industry=="Baugewerbe (F)",]$emp, 
construction2008[construction2008$industry=="Insgesamt",]$emp)

# Concentration of financial industry in Germany 2008 vs. 2014
# based on 16 German regions (Bundeslaender) for 2008 and 2014
finance2008 <- G.regions.emp[(G.regions.emp$industry == 
"Erbringung von Finanz- und Vers.leistungen (K)" | 
G.regions.emp$industry == "Insgesamt") & G.regions.emp$year == "2008",]
finance2008 <- finance2008[finance2008$region != "Insgesamt",]
# delete all-over data for all industries
gini.conc(finance2008[finance2008$industry == 
"Erbringung von Finanz- und Vers.leistungen (K)",]$emp, 
finance2008[finance2008$industry=="Insgesamt",]$emp)
finance2014 <- G.regions.emp[(G.regions.emp$industry == 
"Erbringung von Finanz- und Vers.leistungen (K)" | G.regions.emp$industry ==
"Insgesamt") & G.regions.emp$year == "2014",]
finance2014 <- finance2014[finance2014$region != "Insgesamt",]
# delete all-over data for all industries
gini.conc(finance2014[finance2014$industry == 
"Erbringung von Finanz- und Vers.leistungen (K)",]$emp, 
finance2014[finance2014$industry=="Insgesamt",]$emp)

Example output

[1] 0.4068966
[1] 0.1355832
[1] 0.2171468
[1] 0.2228562

REAT documentation built on Sept. 5, 2021, 5:18 p.m.