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Identification, data combination and the risk of disclosure

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  • Komarova, Tatiana
  • Nekipelov, Denis
  • Yakovlev, Evgeny

Abstract

It is commonplace that the data needed for econometric inference are not contained in a single source. In this paper we analyze the problem of parametric inference from combined individual-level data when data combination is based on personal and demographic identifiers such as name, age, or address. Our main question is the identification of the econometric model based on the combined data when the data do not contain exact individual identifiers and no parametric assumptions are imposed on the joint distribution of information that is common across the combined dataset. We demonstrate the conditions on the observable marginal distributions of data in individual datasets that can and cannot guarantee identification of the parameters of interest. We also note that the data combination procedure is essential in the semiparametric setting such as ours. Provided that the (non-parametric) data combination procedure can only be defined in finite samples, we introduce a new notion of identification based on the concept of limits of statistical experiments. Our results apply to the setting where the individual data used for inferences are sensitive and their combination may lead to a substantial increase in the data sensitivity or lead to a de-anonymization of the previously anonymized information. We demonstrate that the point identification of an econometric model from combined data is incompatible with restrictions on the risk of individual disclosure. If the data combination procedure guarantees a bound on the risk of individual disclosure, then the information available from the combined dataset allows one to identify the parameter of interest only partially, and the size of the identification region is inversely related to the upper bound guarantee for the disclosure risk. This result is new in the context of data combination as we notice that the quality of links that need to be used in the combined data to assure point identification may be much higher than the average link quality in the entire dataset, and thus point inference requires the use of the most sensitive subset of the data. Our results provide important insights into the ongoing discourse on the empirical analysis of merged administrative records as well as discussions on the disclosive nature of policies implemented by the data-driven companies (such as Internet services companies and medical companies using individual patient records for policy decisions)

Suggested Citation

  • Komarova, Tatiana & Nekipelov, Denis & Yakovlev, Evgeny, 2018. "Identification, data combination and the risk of disclosure," LSE Research Online Documents on Economics 79384, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:79384
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    Cited by:

    1. Komarova, Tatiana & Nekipelov, Denis & Al Rafi , Ahnaf & Yakovlev, Evgeny, 2017. "K-anonymity: A note on the trade-off between data utility and data security," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 48, pages 44-62.
    2. Tatiana Komarova & Denis Nekipelov, 2020. "Identification and Formal Privacy Guarantees," Papers 2006.14732, arXiv.org, revised May 2021.
    3. David Pacini, 2012. "Least Square Linear Prediction with Two-Sample Data," Bristol Economics Discussion Papers 12/631, School of Economics, University of Bristol, UK.

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    More about this item

    Keywords

    Data protection; model identification; data combination.;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • 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
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

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