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<p>The paper deals with models in which the dependent variable, some explanatory variables, or both represent unobserved sensitive data. We introduce a novel discretization method that reveals sufficient information from the sensitive variable to approximate the parameter(s) of interest. Multiple discretization schemes are employed, and we show convergence in distribution for the unobserved variable. The asymptotic properties of the OLS estimator for linear models are derived and discussed. Monte Carlo simulations support our theoretical findings and demonstrate finite-sample properties. Finally, we contrast our method with other alternative methods for estimating the Australian gender wage gap.</p>

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Modeling with sensitive variables

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月13日

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  • Felix Chan,
  • László Mátyás,
  • Ágoston Reguly

摘要

The paper deals with models in which the dependent variable, some explanatory variables, or both represent unobserved sensitive data. We introduce a novel discretization method that reveals sufficient information from the sensitive variable to approximate the parameter(s) of interest. Multiple discretization schemes are employed, and we show convergence in distribution for the unobserved variable. The asymptotic properties of the OLS estimator for linear models are derived and discussed. Monte Carlo simulations support our theoretical findings and demonstrate finite-sample properties. Finally, we contrast our method with other alternative methods for estimating the Australian gender wage gap.