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Multidimensional inequality for current status of Japanese private companies’ employees

Author

Listed:
  • Hikaru Hasegawa

    (Hokkaido University)

  • Kazuhiro Ueda

    (Nihon Fukushi University)

Abstract

In this paper, we show a method for measuring multidimensional inequality. We use a Bayesian method for a multivariate ordered probit model to obtain a single index, which we refer to as a probability associated with an individual’s overall status, from a multivariate distribution representing a number of attributes. We evaluate multidimensional inequality by the Gini coefficient and the generalized entropy measure of inequality using this index. This method is used for the employee data of Japanese private companies, which include income and attributes related to subjective perception such as job satisfaction.

Suggested Citation

  • Hikaru Hasegawa & Kazuhiro Ueda, 2016. "Multidimensional inequality for current status of Japanese private companies’ employees," METRON, Springer;Sapienza Università di Roma, vol. 74(3), pages 357-373, December.
    • Handle: RePEc:spr:metron:v:74:y:2016:i:3:d:10.1007_s40300-016-0094-7
    DOI: 10.1007/s40300-016-0094-7
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    References listed on IDEAS

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    Cited by:

    1. Lei Shi, 2020. "Bayesian analysis of multivariate ordered probit model with individual heterogeneity," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(4), pages 649-665, December.

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    More about this item

    Keywords

    Generalized entropy measure; Gini coefficient; Markov chain Monte Carlo (MCMC); Multivariate ordered probit model; Polychoric and polyserial correlation coefficients;
    All these keywords.

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement

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