Microeconometrics Methods and Applications

Introduction

Problems of measurement error pervade all econometrics. In microeconometrics, a common source of the measurement error problem comes from incorrect response to a survey question, incorrect coding of a correct response, and the use of a correctly measured variable as a proxy for another theoretically valid but unobserved variable (e.g., using observed income as a proxy for “normal income”). Questions that seek sensitive information may elicit partial or incorrect responses. That is, a measurement error is triggered by unobservables (or latent variables) when such variables are replaced by proxy variables.

Here are some examples. Consider the problem of testing for the presence of gender bias in a study of earnings. The obvious approach is to regress a measure of earnings on a categorical gender variable while controlling for qualifications, age, experience, and so forth. However, the most relevant variable may be an individual's on-the-job productivity, which may not be directly observed and a proxy may be used instead. Therefore, the impact of measurement error on inferences about the gender discrimination is an important issue. Studies of individual demand for goods and services feature concepts such as “economic cost” or “full price of a service.” However, these are rarely directly measured in published data and must be constructed by the econometrician prior to model estimation. Inevitably their measurement is subject to error.

There are virtually no models discussed in this book that are protected from the problem of measurement errors.