Abstract
The paper introduces PARSEC, a new software package implementing basic partial order tools for multidimensional poverty evaluation with ordinal variables. The package has been developed in the R environment and is freely available from the authors. Its main goal is to provide socio-economic scholars with an integrated set of elementary functions for multidimensional poverty evaluation, based on ordinal information. The package is organized in four main parts. The first two comprise functions for data management and basic partial order analysis; the third and the fourth are devoted to evaluation and implement both the poset-based approach and a more classical counting procedure. The paper briefly sketches the two evaluation methodologies, illustrates the structure and the main functionalities of PARSEC, and provides some examples of its use.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
PARtial orders in Socio-EConomics.
- 2.
In a multidimensional setting, the threshold need not be composed of just one profile, but may comprise several profiles, since the shapes of poverty can be different and incomparable. It may be proved that a threshold can be always chosen as an antichain (Fattore et al. 2011a).
- 3.
The OPHI approach can be applied also when cardinal variables are of concern, but here we limit the discussion to the ordinal case.
- 4.
Precisely, for any linear extension, the differences between the rank of the higher ranked element of the threshold and the ranks of the other profiles are computed.
- 5.
The OPHI approach can be applied also when cardinal variables are of concern. PARSEC implements the methodology for ordinal variables only.
References
Alkire S, Foster J (2011) Counting and multidimensional poverty measurement. J Public Econ 96(7–8):476–487
Alkire S, Foster J (2011) Understandings and misunderstandings of multidimensional poverty measurement. J Econ Inequal 9(2):289–314
Bubley R, Dyer M (1999) Faster random generation of linear extensions. Discrete Math 201: 81–88
De Loof K (2010) Efficient computation of rank probabilities in posets. Ph.D. dissertation. https://biblio.ugent.be/publication/874495
De Loof K, De Baets B, De Meyer H (2008) Properties of mutual rank probabilities in partially ordered sets. In: Owsinski JW, Brueggemann R (eds) Multicriteria ordering and ranking: partial orders, ambiguities and applied issues. Polish Academy of Sciences, Warsaw
Fattore M, Brueggemann R, Owsiński J (2011) Using poset theory to compare fuzzy multidimensional material deprivation across regions. In: Ingrassia S, Rocci R, Vichi M (eds) New perspectives in statistical modeling and data analysis. Springer, Berlin
Fattore M, Maggino F, Greselin F (2011) Socio-economic evaluation with ordinal variables: integrating counting and poset approaches. Stat Appl, Special Issue, 31–42
Fattore M, Maggino F, Colombo E (2012) From composite indicators to partial order: evaluating socio-economic phenomena through ordinal data. In: Maggino F, Nuvolati G (eds) Quality of life in Italy: research and reflections. Social indicators research series, vol. 48. Springer, Berlin
Patil GP, Taillie C (2004) Multiple indicators, partially ordered sets, and linear extensions: multi-criterion ranking and prioritization. Environ Ecol Stat 11:199–228
R Core Team (2012) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. ISBN 3-900051-07-0. http://www.R-project.org/
Ravaillon M (2011) On multidimensional indices of poverty. J Econ Inequal 9(2):235–248
Werhens R, Buydens LMC (2007) Self- and super-organizing maps in R: the kohonen package. J Stat Softw 21(5)
Williams GJ (2009) Rattle: a data mining GUI for R. R J 1/2:45–55
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media New York
About this chapter
Cite this chapter
Fattore, M., Arcagni, A. (2014). PARSEC: An R Package for Poset-Based Evaluation of Multidimensional Poverty. In: Brüggemann, R., Carlsen, L., Wittmann, J. (eds) Multi-indicator Systems and Modelling in Partial Order. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8223-9_15
Download citation
DOI: https://doi.org/10.1007/978-1-4614-8223-9_15
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-8222-2
Online ISBN: 978-1-4614-8223-9
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)