IDEAS home Printed from https://ideas.repec.org/a/col/000090/019410.html
   My bibliography  Save this article

Analysis of Principal Nonlinear Components for the Construction of a Socioeconomic Stratification Index in Ecuador

Author

Listed:
  • Katherine Morales
  • Miguel Flores
  • Yasmín Salazar Méndez

Abstract

Socio-economic stratification classifies people or groups of people within a society. Although social stratification is a universal characteristic of human societies, the criteria considered to classify individuals is not unique and some methodological approaches are distinguished. In this article, we build an indicator of socioeconomic stratification for Ecuador through a Nonlinear Principal Components Analysis using data from the 2010 Census. This methodology allows the incorporation of numerical and categorical variables, and nonlinear relationships. The main results suggest that the households located in the urban area show better conditions and greater access to basic services. Also, education positively affects social and economic conditions of both individuals and the households. In light of these results, public policy should target education and public investment in the provision of basic services in rural areas.

Suggested Citation

  • Katherine Morales & Miguel Flores & Yasmín Salazar Méndez, 2021. "Analysis of Principal Nonlinear Components for the Construction of a Socioeconomic Stratification Index in Ecuador," Revista Desarrollo y Sociedad, Universidad de los Andes,Facultad de Economía, CEDE, vol. 88(2), pages 43-82, July.
  • Handle: RePEc:col:000090:019410
    as

    Download full text from publisher

    File URL: https://revistas.uniandes.edu.co/doi/pdf/10.13043/DYS.88.2
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. J. Kruskal, 1964. "Nonmetric multidimensional scaling: A numerical method," Psychometrika, Springer;The Psychometric Society, vol. 29(2), pages 115-129, June.
    2. Joseph Kruskal & Roger Shepard, 1974. "A nonmetric variety of linear factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 39(2), pages 123-157, June.
    3. Krijnen, Wim P., 2006. "Convergence of the sequence of parameters generated by alternating least squares algorithms," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 481-489, November.
    4. Forrest Young, 1981. "Quantitative analysis of qualitative data," Psychometrika, Springer;The Psychometric Society, vol. 46(4), pages 357-388, December.
    5. Kamakura, Wagner A. & Mazzon, Jose A., 2013. "Socioeconomic status and consumption in an emerging economy," International Journal of Research in Marketing, Elsevier, vol. 30(1), pages 4-18.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hansohm, Jürgen, 2007. "Algorithms and error estimations for monotone regression on partially preordered sets," Journal of Multivariate Analysis, Elsevier, vol. 98(5), pages 1043-1050, May.
    2. Jerzy Grobelny & Rafal Michalski & Gerhard-Wilhelm Weber, 2021. "Modeling human thinking about similarities by neuromatrices in the perspective of fuzzy logic," WORking papers in Management Science (WORMS) WORMS/21/09, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    3. Masahiro Kuroda & Yuichi Mori & Masaya Iizuka, 2023. "Speeding up the convergence of the alternating least squares algorithm using vector $$\varepsilon $$ ε acceleration and restarting for nonlinear principal component analysis," Computational Statistics, Springer, vol. 38(1), pages 243-262, March.
    4. Peter Verboon & Ivo Lans, 1994. "Robust canonical discriminant analysis," Psychometrika, Springer;The Psychometric Society, vol. 59(4), pages 485-507, December.
    5. Kuroda, Masahiro & Mori, Yuichi & Iizuka, Masaya & Sakakihara, Michio, 2011. "Acceleration of the alternating least squares algorithm for principal components analysis," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 143-153, January.
    6. Michel Tenenhaus, 1988. "Canonical analysis of two convex polyhedral cones and applications," Psychometrika, Springer;The Psychometric Society, vol. 53(4), pages 503-524, December.
    7. Jacqueline Meulman, 1992. "The integration of multidimensional scaling and multivariate analysis with optimal transformations," Psychometrika, Springer;The Psychometric Society, vol. 57(4), pages 539-565, December.
    8. Yoo, Boonghee, 2009. "Developing an overall ranking of 79 marketing journals: An introduction of PRINQUAL to marketing," Australasian marketing journal, Elsevier, vol. 17(3), pages 160-174.
    9. Beniaich, Adnane & Guimarães, Danielle Vieira & Avanzi, Junior Cesar & Silva, Bruno Montoani & Acuña-Guzman, Salvador Francisco & dos Santos, Wharley Pereira & Silva, Marx Leandro Naves, 2023. "Spontaneous vegetation as an alternative to cover crops in olive orchards reduces water erosion and improves soil physical properties under tropical conditions," Agricultural Water Management, Elsevier, vol. 279(C).
    10. Giuseppe Arbia & Giovanni Lafratta, 2002. "Anisotropic spatial sampling designs for urban pollution," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 51(2), pages 223-234, May.
    11. Samuel Shye, 2010. "The Motivation to Volunteer: A Systemic Quality of Life Theory," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 98(2), pages 183-200, September.
    12. Muñoz-Mas, Rafael & Vezza, Paolo & Alcaraz-Hernández, Juan Diego & Martínez-Capel, Francisco, 2016. "Risk of invasion predicted with support vector machines: A case study on northern pike (Esox Lucius, L.) and bleak (Alburnus alburnus, L.)," Ecological Modelling, Elsevier, vol. 342(C), pages 123-134.
    13. Markovsky, Ivan & Niranjan, Mahesan, 2010. "Approximate low-rank factorization with structured factors," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3411-3420, December.
    14. la Grange, Anthony & le Roux, Niël & Gardner-Lubbe, Sugnet, 2009. "BiplotGUI: Interactive Biplots in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 30(i12).
    15. Simensen, Trond & Halvorsen, Rune & Erikstad, Lars, 2018. "Methods for landscape characterisation and mapping: A systematic review," Land Use Policy, Elsevier, vol. 75(C), pages 557-569.
    16. Silvia Vilčeková & Ilija Zoran Apostoloski & Ľudmila Mečiarová & Eva Krídlová Burdová & Jozef Kiseľák, 2017. "Investigation of Indoor Air Quality in Houses of Macedonia," IJERPH, MDPI, vol. 14(1), pages 1-12, January.
    17. Funk, Patrick & Davis, Alex & Vaishnav, Parth & Dewitt, Barry & Fuchs, Erica, 2020. "Individual inconsistency and aggregate rationality: Overcoming inconsistencies in expert judgment at the technical frontier," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
    18. Balabanis, George & Stathopoulou, Anastasia, 2021. "The price of social status desire and public self-consciousness in luxury consumption," Journal of Business Research, Elsevier, vol. 123(C), pages 463-475.
    19. Moris Triventi, 2014. "Higher education regimes: an empirical classification of higher education systems and its relationship with student accessibility," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(3), pages 1685-1703, May.
    20. Jessica Dafflon & Pedro F. Da Costa & František Váša & Ricardo Pio Monti & Danilo Bzdok & Peter J. Hellyer & Federico Turkheimer & Jonathan Smallwood & Emily Jones & Robert Leech, 2022. "A guided multiverse study of neuroimaging analyses," Nature Communications, Nature, vol. 13(1), pages 1-13, December.

    More about this item

    Keywords

    Statistical analysis; social class; social inequality; Ecuador.;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:col:000090:019410. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Universidad De Los Andes-Cede (email available below). General contact details of provider: https://edirc.repec.org/data/ceandco.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.