Colignatus, Thomas (2018): Comparing the Aitchison distance and the angular distance for use as inequality or disproportionality measures for votes and seats.
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Abstract
Votes and seats satisfy only two of seven criteria for application of the Aitchison distance. Vectors of votes and seats, say for elections for political parties the House of Representatives, can be normalised to 1 or 100%, and then have the outward appearance of compositional data. The Aitchison geometry and distance for compositional data then might be considered for votes and seats too. However, there is an essential zero when a party gets votes but doesn't gain a seat, and a zero gives an undefined logratio. In geology, changing from weights to volumes affects the percentages but not the Aitchison distance. For votes and seats there are no different scales or densities per party component however, and thus reportioning (perturbation) would be improper. Another key issue is subcompositional dominance. For votes {10, 20, 70} and seats {20, 10, 70} it is essential that we consider three parties. For a disproportionality measure we would value it positively that there is a match on 70. The Aitchison distance looks at the ratio {10, 20, 70} / {20, 10, 70} = {1/2, 2, 1} and then neglects a ratio equal to 1. In this case it essentially compares the subcompositions, i.e. votes {10, 20} and seats {20, 10}, rescales to {1/3, 2/3} and {2/3, 1/3}, and finds high disproportionality. This means that it essentially looks at a two party outcome instead of a three party outcome. It follows that votes and seats are better served by another distance measure. Suggested is the angular distance and the Sine-Diagonal Inequality / Disproportionality (SDID) measure based upon this. Users may of course apply both the angular and the Aitchison measures while being aware of the crucial differences in properties.
Item Type: | MPRA Paper |
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Institution: | Thomas Cool Consultancy & Econometrics |
Original Title: | Comparing the Aitchison distance and the angular distance for use as inequality or disproportionality measures for votes and seats |
Language: | English |
Keywords: | Votes, Seats, Electoral System, Distance, Disproportionality, Aitchison Geometry, Angular Distance, Sine-Diagonal Inequality / Disproportionality, Loosemore-Hanby, Gallagher, Descriptive Statistics, Education, Reportion |
Subjects: | A - General Economics and Teaching > A1 - General Economics > A10 - General D - Microeconomics > D6 - Welfare Economics > D63 - Equity, Justice, Inequality, and Other Normative Criteria and Measurement D - Microeconomics > D7 - Analysis of Collective Decision-Making > D71 - Social Choice ; Clubs ; Committees ; Associations D - Microeconomics > D7 - Analysis of Collective Decision-Making > D72 - Political Processes: Rent-Seeking, Lobbying, Elections, Legislatures, and Voting Behavior |
Item ID: | 84334 |
Depositing User: | Thomas Colignatus |
Date Deposited: | 04 Feb 2018 08:12 |
Last Modified: | 01 Oct 2019 06:27 |
References: | Colignatus is the name in science of Thomas Cool, econometrician and teacher of mathematics, Scheveningen, Holland, http://econpapers.repec.org/RAS/pco170.htm This paper is also available at: https://www.wolframcloud.com/objects/thomas-cool/Voting/2018-01-18-Aitchison.nb (updated but still the same name). Aitchison, J., and J.A.C. Brown (1957), "The lognormal distribution", CUP Aitchison, J. (undated, perhaps latest version 2002), "A concise guide to compositional data analysis", http://ima.udg.edu/activitats/codawork05/A_concise_guide_to_compositional_data_analysis.pdf Barcelo-Vidal, C., and J.A. Martin-Fernandez (2016), "The mathematics of compositional analysis", Austrian J. of Statistics, Vol 45, No 4, http://dx.doi.org/10.17713/ajs.v45i4.142 Colignatus, Th. (2017a), "Comparing votes and seats with cosine, sine and sign, with attention for the slope and enhanced sensitivity to disproportionality", https://mpra.ub.uni-muenchen.de/81389/ Colignatus, Th. (2017b), "One woman, one vote. Though not in the USA, UK and France", https://mpra.ub.uni-muenchen.de/82513/ Colignatus, Th. (2017c), "Statistics, slope, cosine, sine, sign, significance and R-squared", https://boycottholland.wordpress.com/2017/10/21/statistics-slope-cosine-sine-sign-significance-and-r-squared/ Colignatus, Th. (2017d), "Karl Pearson's curious construction of spurious correlation", https://boycottholland.wordpress.com/2017/11/19/karl-pearsons-curious-construction-of-spurious-correlation/ Colignatus, Th. (2018), "Measures of policy distance and inequality / disproportionality of votes and seats", MPRA_paper_84324.pdf, https://www.wolframcloud.com/objects/thomas-cool/Voting/2018-02-02-PolicyDistance.nb Dongen, S. van & A.J. Enright (2012), "Metric distances derived from cosine similarity and Pearson and Spearman correlations", https://arxiv.org/abs/1208.3145 Golder, M. & Stramski, J. (2010), "Ideological congruence and electoral institutions", American Journal of Political Science, 54(1), 90-106 Hron, K. (2008), "Remark on properties of bases for additive logratio transformations of compositional data", Acta Univ. Palacki. Olomuc. Fac. rer. nat., Mathematica 47, 77-82, http://kma.upol.cz/data/xinha/ULOZISTE/ActaMath/0708.pdf Katz, J.N, and G. King (1999), "A statistical model for multiparty electoral data", American political science review, Vol 93, No 1, p15-32,https://gking.harvard.edu/files/gking/files/multiparty.pdf Maligranda, L. (2006), "Simple norm inequalities", Amer. Math. Monthly 113 (3), 256 \[Dash] 260 Martin-Fernandez, J.A., and C. Barcelo-Vidal, V. Pawlowsky-Glahn (undated), "Measures of difference for compositional data and hierarchical clusering methods", http://ima.udg.edu/~barcelo/index_archivos/Measures_of_difference__Clustering.pdf Pawlowsky-Glahn, V., J.J. Egozcue, and R. Meziat (2007), "The statistical analysis of compositional data: The Aitchison geometry", (sheets), https://laboratoriomatematicas.uniandes.edu.co/cursocoda/04-Vera-geometry.pdf Pawlowsky-Glahn, V., and J.J. Egozcue (2011), "The closure problem: one hundred years of debate", http://slon.diamo.cz/hpvt/2011/_Mat/M%2002.pdf Tolosana, R. (2008), "Compositional Data Analysis in a Nutshell", http://www.sediment.uni-goettingen.de/staff/tolosana/extra/CoDaNutshell.pdf Vallis, R.A., H. Nunez, and J. C. Martin (2008), "Why, and How, Should Geologists Use Compositional Data Analysis", https://en.wikibooks.org/wiki/Why,_and_How,_Should_Geologists_Use_Compositional_Data_Analysis Wang, H., Q. Liu, H.M.K. Mok, L. Fu, W. Man Tse (2007), "A hyperspherical transformation forcasting model for compositional data", Eur. J. of Oper. Res. 179, 459-468, https://pdfs.semanticscholar.org/67e5/fa4a7cc9c833109722eed64fc3611c4e15f4.pdf |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/84334 |
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