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The Consequences of Mismeasuring Economic Activity

Project description

A better measure of economic activity

Measuring economic activity is a fundamental challenge. For instance, consider production problems such as how a firm produces goods from intermediate goods or how income of an individual is produced from the individual's cognitive and non-cognitive skills. Such inputs (intermediate goods or skills) are inherently difficult to measure and researchers often wonder whether their data actually measure what they are supposed to measure. The EU-funded MEImpact project will develop a new methodology for formally assessing the potential impact of measurement error (ME), which is the difference between a measured quantity and its true value. The project aims to change how empirical researchers approach measurement issues and prevent misleading conclusions and policy decisions.

Objective

Measuring economic activity is a fundamental challenge for empirical work in economics. Most empirical projects raise concerns about whether the data do in fact measure what they purport to measure. Mismeasurement may lead to severe model misspecification, biased estimates, and misled conclusions and policy decisions. Unfortunately, formally accounting for the possibility of mismeasurement in the econometric model is complicated and possible only under strong assumptions that limit the credibility of resulting conclusions. Therefore, the most common approaches to measurement issues are to ignore them, to informally argue why they may not be of first-order importance, to abandon the project, or to search for better data.

The objective of the research described in this proposal is to develop new methodologies for formally assessing the potential impact of measurement error (ME) on all aspects of an empirical project: on model-building, on estimation and inference, and on decision-making. For instance, the new inference procedures allow the researcher to test whether ME is a statistically significant feature that should be modeled, whether ME distorts objects of interest (e.g. a production or utility function), whether ME distorts conclusions from hypothesis tests, and whether ME affects subsequent decision-making.
I show that answering such questions is possible under much weaker assumptions than identification and estimation of a ME model and thus leads to more credible and robust conclusions. In addition, the implementation of the new procedures can be based on standard nonparametric estimation techniques that are part of many applied researchers’ toolkits.

In consequence, the research has the potential to fundamentally transform the way empirical researchers approach measurement issues, to significantly impact empirical practice, and ultimately to avoid misled conclusions and policy decisions.

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Coordinator

LUDWIG-MAXIMILIANS-UNIVERSITAET MUENCHEN
Net EU contribution
€ 680 682,25
Address
Geschwister scholl platz 1
80539 Muenchen
Germany

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Region
Bayern Oberbayern München, Kreisfreie Stadt
Activity type
Higher or Secondary Education Establishments
Links
Other funding
€ 0,00

Beneficiaries (2)