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Measuring wage inequality under right censoring

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  • Paulo M.M. Rodrigues
  • João Nicolau
  • Pedro Raposo

Abstract

In this paper we investigate potential changes which may have occurred over the last two decades in the probability mass of the right tail of the wage distribution, through the analysis of the corresponding tail index. In specific, a conditional tail index estimator is introduced which explicitly allows for right tail censoring (top-coding), which is a feature of the widely used current population survey (CPS), as well as of other surveys. Ignoring the top-coding may lead to inconsistent estimates of the tail index and to under or over statements of inequality and of its evolution over time. Thus, having a tail index estimator that explicitly accounts for this sample characteristic is of importance to better understand and compute the tail index dynamics in the censored right tail of the wage distribution. The contribution of this paper is threefold: i) we introduce a conditional tail index estimator that explicitly handles the top-coding problem, and evaluate its finite sample performance and compare it with competing methods; ii) we highlight that the factor values used to adjust the top-coded wage have changed over time and depend on the characteristics of individuals, occupations and industries, and propose suitable values; and iii) we provide an in-depth empirical analysis of the dynamics of the US wage distribution’s right tail using the public-use CPS database from 1992 to 2017.

Suggested Citation

  • Paulo M.M. Rodrigues & João Nicolau & Pedro Raposo, 2020. "Measuring wage inequality under right censoring," Working Papers w202008, Banco de Portugal, Economics and Research Department.
  • Handle: RePEc:ptu:wpaper:w202008
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    References listed on IDEAS

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    More about this item

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • J11 - Labor and Demographic Economics - - Demographic Economics - - - Demographic Trends, Macroeconomic Effects, and Forecasts
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

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