REAT (version 3.0.2)

gini.conc: Gini coefficient of spatial industry concentration

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))

Value

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

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).

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

# NOT RUN {
# 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)
# }