Analyzing industry-specific regional growth with the shift-share analysis
shifti(e_ij1, e_ij2, e_i1, e_i2, industry.names = NULL,
shift.method = "Dunn", print.results = TRUE, plot.results = FALSE,
plot.colours = NULL, plot.title = NULL, plot.portfolio = FALSE, ...)a numeric vector with \(i\) values containing the employment in \(i\) industries in region \(j\) at time 1
a numeric vector with \(i\) values containing the employment in \(i\) industries in region \(j\) at time 2
a numeric vector with \(i\) values containing the total employment in \(i\) industries at time 1
a numeric vector with \(i\) values containing the total employment in \(i\) industries at time 2
Industry names (e.g. from the relevant statistical classification of economic activities)
Method of shift-share-analysis to be used ("Dunn", "Gerfin") (default: shift.method = "Dunn")
Logical argument that indicates if the function shows the results or not
Logical argument that indicates if the results have to be plotted
If plot.results = TRUE: Plot colours
If plot.results = TRUE: Plot title
Logical argument that indicates if the results have to be plotted in a portfolio matrix additionally
Additional arguments for the portfolio plot (see the function portfolio)
A list containing the following objects:
A matrix containing the shift-share components related to the chosen method
A matrix containing the shift-share components for each industry
A matrix containing the industry-specific growth values
The chosen method, e.g. "Dunn"
The shift-share analysis (Dunn 1960) adresses the regional growth (or decline) regarding the over-all development in the national economy. The aim of this analysis model is to identify which parts of the regional economic development can be traced back to national trends, effects of the regional industry structure and (positive) regional factors. The growth (or decline) of regional employment consists of three factors: \(l_{t+1}-l_t = nps + nds + nts\), where \(l\) is the employment in the region at time \(t\) and \(t+1\), respectively, and \(nps\) is the net proportionality shift, \(nds\) is the net differential shift and \(nts\) is the net total shift. Other variants are e.g. the shift-share method by Gerfin (Index method) and the dynamic shift-share analysis (Barff/Knight 1988).
As there is more than one way to calculate a Dunn-type shift-share analysis and the terms are not used consequently in the regional economic literature, this function and the documentation use the formulae and terms given in Farhauer/Kroell (2013). If shift.method = "Dunn", this function calculates the net proportionality shift (\(nps\)), the net differential shift (\(nds\)) and the net total shift (\(nts\)) where the last one represents the residuum of (positive) regional factors.
This function calculates a shift-share analysis for at least two years and results industry-specific shift-share components.
Arcelus, F. J. (1984): “An Extension of Shift-Share Analysis”. In: In: Growth and Change, 15, 1, p. 3-8.
Barff, R. A./Knight, P. L. (1988): “Dynamic Shift-Share Analysis”. In: Growth and Change, 19, 2, p. 1-10.
Casler, S. D. (1989): “A Theoretical Context for Shift and Share Analysis”. In: Regional Studies, 23, 1, p. 43-48.
Dunn, E. S. Jr. (1960): “A statistical and analytical technique for regional analysis”. In: Papers and Proceedings of the Regional Science Association, 6, p. 97-112.
Esteban-Marquillas, J. M. (1972): “Shift- and share analysis revisited”. In: Regional and Urban Economics, 2, 3, p. 249-261.
Farhauer, O./Kroell, A. (2013): “Standorttheorien: Regional- und Stadtoekonomik in Theorie und Praxis”. Wiesbaden : Springer.
Gerfin, H. (1964): “Gesamtwirtschaftliches Wachstum und regionale Entwicklung”. In: Kyklos, 17, 4, p. 565-593.
Schoenebeck, C. (1996): “Wirtschaftsstruktur und Regionalentwicklung: Theoretische und empirische Befunde fuer die Bundesrepublik Deutschland”. Dortmunder Beitraege zur Raumplanung, 75. Dortmund.
# NOT RUN {
# Example from Farhauer/Kroell (2013):
region_A_t <- c(90,20,10,60)
region_A_t1 <- c(100,40,10,55)
# data for region A (time t and t+1)
nation_X_t <- c(400,150,150,400)
nation_X_t1 <- c(440,210,135,480)
# data for the national economy (time t and t+1)
shifti(region_A_t, region_A_t1, nation_X_t, nation_X_t1,
plot.results = TRUE, plot.portfolio = TRUE, psize = region_A_t1)
# }
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