Income adjustment in Mexico with a Bayesian approach

  • Fredy Yair Montes Rivera Colegio de Postgraduados
  • Paulino Pérez Rodríguez Colegio de Postgraduados
  • Sergio Pérez Elizalde Colegio de Postgraduados
Keywords: Pareto, Lognormal, Dagum distributions, algorithm, Gini’s coefficient, Lorenz’s curve
JEL Classification: C02, C11, C12, C13, C15

Abstract

In this work, three distributions are proposed (Pareto, Lognormal and Dagum) to model the income of mexican population, by using the Bayesian approach. It was found that the Dagum model was the one that best describes the data. The posterior distributions of the quantities of interest were obtained by using Markov Chain Monte Carlo methods. The analysis was done by using data from the years 1998, 2002 and 2008. The results shown that the distribution income is quite unequal, which is not in agreement with the results reported by Centro de Estudios de las Finanzas Públicas, H. Cámara de Diputados.

References

Akaike, H. 1974. A New Look at the Statistical Model Identification, IEEE Transactions on Automatic Control, 19(6): 716-723.

Arnold, B.C., E. Castillo y J.M. Sarabia. 1998. Bayesian Analysis for Classical Distributions Using Conditionally Specified Priors, The Indian Journal of Statistics, 60: 228-245.

Casella, G. y E.I. George. 1992. Explaining the Gibbs Sampler, American Statistical Association, 46: 167-174.

Centro de Estudios de las Finanzas Públicas, CEFP. 2008. Distribución del ingreso y desigualdad en México; un análisis sobre la ENIGH 2000 - 2006, México, Cámara de Diputados, CEFP/9/2008.

Christen, J. y C. Fox. 2010. A General Purpose Sampling Algorithm for Continuous Distributions (the t-walk), Bayesian Analysis, 5: 263-282.

Dagum, C. 1977. A New Model for Personal Income Distributions: Specification and Estimation, Ecomomic Appliqueé, 30: 413-437.

Gelman, A. y D. Rubin. 1992. Inference from Iterative Simulation Using Multiple Sequences, Statistical Science, 7: 457-511.

Gilks, W. y P. Wild. 1992. Adaptive Rejection Sampling for Gibbs Sampling, Applied Statistical, 41: 337-348.

INEGI. 1998, 2002, 2008. Encuesta nacional de ingresos y gastos de los hogares, México.

Metropolis, N., A. Rosenbluth, M. Rosenluth y E. Teller. 1953. Equations of State Calculations by Fast Computing Machines, Journal of the Chemical Physics, 21: 1087-1091.

Pareto, V. 1895. La legge della domanda, Giornale degli Economisti, 10: 59-68.

R Development Core Team. 2011. R: A Language and Environment for Statistical Computing, R. Foundation for Statistical Computing, http://www.R-project.org.

Rtwalk. 2010. Rtwalk: The R implementation of the t-walk, R package versión 1.5.1.

Upadhyay, S. K., N. Vasishta y A. Smith. 2000. Bayes Inference in Life Testing and Reliability via Markov Chain Monte Carlo Simulation, Sankhya, 62(2): 203-222.

Published
01-07-2012
How to Cite
Montes RiveraF., Pérez RodríguezP., & Pérez ElizaldeS. (2012). Income adjustment in Mexico with a Bayesian approach. Estudios Económicos, 27(2), 273-293. https://doi.org/10.24201/ee.v27i2.90
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