Information technology outsourcing and firm productivity: eliminating bias from selective missingness in the dependent variable

Christoph Breunig, Michael Kummer, Joerg Ohnemus, Steffen Viete

Research output: Contribution to journalArticlepeer-review


Missing values are a major problem in all econometric applications based on survey data. A standard approach assumes data are missing at random and uses imputation methods or even listwise deletion. This approach is justified if item nonresponse does not depend on the potentially missing variables’ realization. However, assuming missingness at random may introduce bias if nonresponse is, in fact, selective. Relevant applications range from financial or strategic firm-level data to individual-level data on income or privacy-sensitive behaviors. In this paper, we propose a novel approach to deal with selective item nonresponse in the model’s dependent variable. Our approach is based on instrumental variables that affect selection only through a partially observed outcome variable. In addition, we allow for endogenous regressors. We establish identification of the structural parameter and propose a simple two-step estimation procedure for it. Our estimator is consistent and robust against biases that would prevail when assuming missingness at random. We implement the estimation procedure using firm-level survey data and a binary instrumental variable to estimate the effect of outsourcing on productivity.
Original languageEnglish
Pages (from-to)88-114
Number of pages27
JournalThe Econometrics Journal
Issue number1
Early online date20 Sep 2019
Publication statusPublished - Jan 2020


  • C14 - Semiparametric and Nonparametric Methods: General
  • C36 - Instrumental Variables (IV) Estimation
  • D24 - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
  • L24 - Contracting Out; Joint Ventures; Technology Licensing

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