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2017 | OriginalPaper | Chapter

7. Causality: Endogeneity Biases and Possible Remedies

Authors : Willem Mertens, Amedeo Pugliese, Jan Recker

Published in: Quantitative Data Analysis

Publisher: Springer International Publishing

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Abstract

Many, if not all, studies in accounting and information systems address causal research questions. A key feature of such questions is that they seek to establish whether a variation in X (the treatment) leads to a state change in Y (the effect). These studies go beyond an association between two phenomena (i.e., a correlation between variables in the empirical model) to find a true cause-effect relationship. Moving from a simple association to a causal claims requires meeting a number of conditions.

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Footnotes
1
Editors and reviewers are increasingly aware of the issues with observational data and causal claims. Some of the leading journals in business and management fields suggest that authors deal with endogeneity issues in the manuscript prior to the first submission of the study for consideration: http://​strategicmanagem​ent.​net/​pdfs/​smj-guidelines-regarding-empirical-research.​pdf
 
2
If a researcher could measure managerial skill with any confidence, the OCV would become observable. By adding it as a covariate to the OLS model specification, the estimation would be freed from the biasing effect of managerial skill.
 
3
Chapter 6 on approaches to longitudinal and panel data illustrates some additional remedies.
 
4
A note of caution is warranted here. Even though OLS regression using cross-sectional data is not the best tool and setting in which to rule out endogeneity concerns, in certain circumstances researchers can still minimize endogeneity issues and rule out sources of concerns [19]. A note of thanks goes to Stefano Cascino for highlighting this sometimes hidden truth.
 
5
Data and codes are available upon request from authors and will be available on the book’s companion website.
 
6
IVE also helps in solving measurement error problems that are due to the inability to observing and measure the best proxy for the underlying concept.
 
7
The portion of X (Inst) that is non-overlapping with Inst (X) is likely to be endogenous (uncorrelated with Y), so both must not be employed in estimating the outcome model.
 
8
The Hausman test is employed in many contexts to compare the magnitude and significance of a series of coefficients. The Hausman test is discussed in Chap. 6 to compare the results of fixed-effects and random-effects model estimation.
 
9
The econometrics literature offers rich guidance in terms of the F-test values that should be used as a benchmark: If the number of instruments is 1, 2, or more than 5, the corresponding lower threshold of F-values are 8.96, 11.59, or higher than 15 to rule out the risk of a weak instrument.
 
10
Statistical software (e.g. STATA, R, SAS) offers convenient routines with which to estimate 2SLS models.
 
11
This example is reported in Murnane and Willet [3] in much more detail. We refer the reader directly to this valuable source for an in-depth assessment and understanding of the example.
 
12
All of the issues raised in this chapter follow in the realm of a frequentist parametric framework. Other methods, including non-parametric methods, have been employed to minimize the potential issues with self-selection that are due to unobservables. For instance, Bayesian approaches have been suggested to evaluate treatment effects when selections are based on unobservables. A note of caution is required, given the limited applications of such an approach in the accounting (and finance) literature and because of the lack of evidence on the advantages of this approach over a frequentist approach.
 
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Metadata
Title
Causality: Endogeneity Biases and Possible Remedies
Authors
Willem Mertens
Amedeo Pugliese
Jan Recker
Copyright Year
2017
Book
Quantitative Data Analysis

Print ISBN: 978-3-319-42699-0

Electronic ISBN: 978-3-319-42700-3

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-42700-3_7

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