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.

中文翻译：

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IT外包与企业生产率：消除因变量中的选择性缺失造成的偏差

在所有基于调查数据的计量经济学应用中，缺失值是一个主要问题。一种标准的方法假设数据是随机丢失的，并使用插补方法甚至逐列表删除。如果项目无响应不依赖于可能丢失的变量的实现，则此方法是合理的。但是，如果无响应实际上是选择性的，则假定随机丢失会引入偏差。相关应用范围从财务或战略性公司级数据到有关收入或隐私敏感行为的个人级数据。在本文中，我们提出了一种新颖的方法来处理模型因变量中的选择性项目无响应。我们的方法基于仅通过部分观察到的结果变量影响选择的工具变量。此外，我们允许内生回归变量。我们建立结构参数的识别，并提出一个简单的两步估算程序。我们的估计是一致的，并且对于随机假设缺失的情况下普遍存在的偏差具有鲁棒性。我们使用公司级别的调查数据和二进制工具变量来实施估算程序，以估算外包对生产率的影响。