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Análisis del desempleo urbano a través de un estudio comparativo de métodos de clasificación

Author

Listed:
  • Margarita Díaz

    (Universidad Nacional de Córdoba, Facultad de Ciencias Económicas, Instituto de Estadística y Demografía (Córdoba, Argentina))

  • Fernando Ferrero

    (Universidad Nacional de Córdoba, Facultad de Ciencias Económicas, Instituto de Estadística y Demografía (Córdoba, Argentina))

  • Cecilia Díaz

    (Universidad Nacional de Córdoba, Facultad de Ciencias Económicas, Instituto de Estadística y Demografía (Córdoba, Argentina))

  • Patricia Caro

    (Universidad Nacional de Córdoba, Facultad de Ciencias Económicas, Instituto de Estadística y Demografía (Córdoba, Argentina))

  • María Inés Stimolo

    (Universidad Nacional de Córdoba, Facultad de Ciencias Económicas, Instituto de Estadística y Demografía (Córdoba, Argentina))

Abstract

Este trabajo propende a identificar los factores de riesgo que inciden en la precariedad laboral de la Población Económicamente Activa. Se adoptó como plataforma informativa la base de datos de la Encuesta Permanente de Hogares, octubre 2002, relevada en las ciudades de Córdoba, Rosario y en el gran Buenos Aires. El efecto de las variables predictoras sobre la condición de actividad del encuestado se estimó a través de los Análisis de Regresión Logística y Árboles de Decisión. Adicionalmente, y a los fines de mejorar la performance de la clasificación obtenida, se aplicaron los métodos de Redes Neuronales y Vecino más Cercano. / This work attempts to identify several risk factors underlying the so called precariousness of the argentinian labour force. To this end it was taken into account the database of the Periodically Household Survey, October 2002, a survey regularly carried out in the cities of Córdoba, Rosario and great Buenos Aires. Final effects of predictive variable over the activity condition of the individual being interviewed were modeled through Logit Regression and Tree Decision Models. Additionally, in order to improving the performance of the estimated classification rules two statistical models were also worked out, say, Neuronal Networks and Nearest Neighbour methods.

Suggested Citation

  • Margarita Díaz & Fernando Ferrero & Cecilia Díaz & Patricia Caro & María Inés Stimolo, 2005. "Análisis del desempleo urbano a través de un estudio comparativo de métodos de clasificación," Revista de Economía y Estadística, Universidad Nacional de Córdoba, Facultad de Ciencias Económicas, Instituto de Economía y Finanzas, vol. 43(2), pages 61-85, Diciembre.
    • Handle: RePEc:ief:reveye:v:43:y:2005:i:2:p:61-85
    DOI: 10.55444/2451.7321.2005.v43.n2.3818
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    References listed on IDEAS

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    1. M. Hills, 1967. "Discrimination and Allocation with Discrete Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 16(3), pages 237-250, November.
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    More about this item

    Keywords

    Condición de actividad; regresión logística; vecino más cercano; árboles de decisión; encuesta permanente de hogares;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search

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