summary and PowerPoint

Make a summary and PowerPoint (Please use easy words and easy sentences)

prepare a summary of each paper (including the Libby box summary)

Focus on summarizing the most salient points of the article.

. After that make sure the summaries address the following questions (I will send them).

B .and make PowerPoint slides for each study.

• Three Articles in the attachments

Gow, I., Larcker, D., and Reiss, P. (2016). Causal inference in accounting research. Journal of Accounting Research54 (2): 477–523.

Larcker, D., and Rusticus, T. (2010). 2010-On the use of instrumental variables in accounting research. Journal of Accounting and Economics49: 186–205.

Lennox, C., Francis, J., Wang, Z. (2012). Selection models in accounting research. The Accounting Review87 (2): 589–616.

Selection Models in Accounting Research
Author(s): Clive S. Lennox, Jere R. Francis and Zitian Wang
Source: The Accounting Review, Vol. 87, No. 2 (MARCH 2012), pp. 589-616
Published by: American Accounting Association
Stable URL: https://www.jstor.org/stable/23245616
Accessed: 21-04-2020 01:42 UTC
JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide
range of content in a trusted digital archive. We use information technology and tools to increase productivity and
facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.
Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at

Terms and Conditions of Use

American Accounting Association is collaborating with JSTOR to digitize, preserve and
extend access to The Accounting Review
This content downloaded from 129.2.19.102 on Tue, 21 Apr 2020 01:42:59 UTC
All use subject to https://about.jstor.org/terms
THE ACCOUNTING REVIEW
American Accounting Association
Vol. 87, No. 2
DOI: 10.2308/accr-10195
2012
pp. 589-616
Selection Models in Accounting Research
Clive S. Lennox
Nanyang Technological University
Jere R. Francis
University of Missouri-Columbia
Zitian Wang
Nanyang Technological University
ABSTRACT: This study explains the challenges associated with the Heckman (1979)
procedure to control for selection bias, assesses the quality of its application in
accounting research, and offers guidance for better implementation of selection models.
A survey of 75 recent accounting articles in leading journals reveals that many
researchers implement the technique in a mechanical way with relatively little
appreciation of important econometric issues and problems surrounding its use. Using
empirical examples motivated by prior research, we illustrate that selection models are
fragile and can yield quite literally any possible outcome in response to fairly minor
changes in model specification. We conclude with guidance on how researchers can
better implement selection models that will provide more convincing evidence on
potential selection bias, including the need to justify model specifications and careful
sensitivity analyses with respect to robustness and multicollinearity.
Keywords: selection model; Heckman; selection bias; endogeneity; treatment effect
model.
Data Availability: Data used are available from public sources identified in the study.
I. INTRODUCTION
provides guidance to accounting researchers on potential problems with selection models,
This study
evaluates
thethatimplementation
selection
and recommends
some steps
can be taken to improve theirof
implementation.
Such models in the accounting literature,
guidance is especially important given the increased use of selection models and the frequent
comments by editors and reviewers of the need to control for endogeneity and selection bias. Over
We thank Steven Kachelmeier (editor) and the two reviewers for their helpful comments throughout the review process.
We also appreciate the comments of Mark Clatworthy, Bill Griffiths, Gilles Hilary, David Larcker, Christian Leuz, Siu
Fai Leung, Ping-Sheng Koh, David Maber, Chul Park, Mike Peel, Jeff Pittman, and Terry Shevlin, and the research
assistance of Rui Ge and Scott Seavey.
Editor’s note: Accepted by Steven Kachelmeier.
Submitted: April 2010
Accepted: July 2011
Published Online: November 2011
589
This content downloaded from 129.2.19.102 on Tue, 21 Apr 2020 01:42:59 UTC
All use subject to https://about.jstor.org/terms
590
Lennox, Francis, and Wang
the period 2000 through 2009, we identify 75 articles from The Accounting Review, Journal of
Accounting and Economics, Journal of Accounting Research, Contemporary Accounting Research,
and Review of Accounting Studies that use selection models out of 1,016 empirical articles
published in these journals over the same period. The recent trend is even stronger with 11 percent
of empirical articles employing a selection model during 2008 to 2009.
Selection occurs when observations are non-randomly sorted into discrete groups, resulting in
the potential for coefficient bias in estimation procedures such as ordinary least squares (OLS)
(Maddala 1991). The standard approach to controlling for selection bias is the procedure developed
by Heckman (1979), hereafter referred to as the selection model. A convincing implementation
requires the researcher to identify exogenous independent variables from the first stage choice
model that can be validly excluded from the set of independent variables in the second stage
regression (Little 1985). However, the importance of exclusion restrictions appears to have fallen
under the radar of the accounting literature. A surprising number of studies (14 of 75) fail to have
any exclusions, and other studies (7 out of 75) do not report the first stage model, making it
impossible to determine if they imposed exclusion restrictions. Moreover, very few studies provide
any theoretical or economic justification for their chosen restrictions.
We demonstrate empirically that the selection model is fragile and that results can be
non-robust and therefore unreliable when researchers choose exclusion restrictions in an ad hoc
fashion or choose none at all. To improve the implementation of selection models in accounting
research, we recommend careful reporting of sensitivity analyses and robustness tests, which,
surprisingly, are uncommon in accounting studies that use selection models. The majority of the 75
studies in our review do not report whether their inferences are sensitive to alternative exclusion
restrictions, nor do they discuss the problems that can arise when using the selection model, such as
high multicollinearity. Our central conclusion is that, as accounting researchers, we need to be more
careful and rigorous in our implementation of selection models, particularly in the choice of
exclusion restrictions. Further, because of the inherent limitations and fragility of selection models,
we should also be much more circumspect with respect to claims about “controlling for selection
bias.” Last, it may not be feasible to implement a convincing selection model in some research
settings and, in this case, our advice is that studies acknowledge this limitation and provide a caveat
that the reported results could be affected by selection bias.
The remainder of our article proceeds as follows. The next section discusses the selection
model and implementation issues. Section III reviews how selection models have been used in the
accounting literature and compares this with best practice. We also highlight the differences
between our critique of selection models and those of Larcker and Rusticus (2010) and Tucker
(2010), who survey the accounting literature’s implementation of regular instrumental variable (IV)
estimation and Heckman models. Section IV provides three empirical examples based on past
accounting studies and shows that inferences are extremely sensitive to fairly minor changes in the
selection model’s specification.1 Section V replicates and extends a study that was recently
published in one of the top-tier accounting journals, demonstrating that its inferences are sensitive
to minor changes in model specification. Section VI offers guidance on improving the
implementation of selection models. These recommendations have important implications for
editors and reviewers, as well as authors. Section VII concludes.
1 By “fairly minor” we mean the chosen research design would not necessarily arouse the suspicions of an editor
or reviewer.
Accounting
The
Accounting
Assoc’3,ion
March
Review
2012
This content downloaded from 129.2.19.102 on Tue, 21 Apr 2020 01:42:59 UTC
All use subject to https://about.jstor.org/terms
591
Selection Models in Accounting Research
II. CORRECTING FOR SELECTIVITY BIAS
The Selection Model
There are two distinct applications of the selection model. The first—commonly kno
treatment effect model—is where an endogenous indicator variable (D) is include
independent regressor. For example, a researcher might be interested in testing w
management earnings forecasts affect the cost of capital. In this case, the endogenous i
variable (D) indicates whether the company issues an earnings forecast and the dependent
is the cost of capital. The second application—sometimes known as a sample selection mo
occurs when a regression is estimated on a subsample of observations. For example, a re
might be interested in testing the determinants of management forecast accuracy. In this
dependent variable measures forecast accuracy and the regression is estimated on a subs
companies that issue earnings forecasts. In both applications D is endogenous, raising po
concerns about bias.
The treatment effect model can be written as follows:
Y
=
P’X
where X
variable,
D*
=
where
QD
+
u,
(1)
is a vector of exogenous
Y. The choice of D is est
a’QZ
+
a\X
+
u,
(2)
D
= 1 if D* > 0 and D = 0 if D* < normally distributed error ter error terms in Equations (1) and assumes The + a distribution with mean zero and covariance matrix: (T2 per pa 1 If the error terms u and v are correlated (i.e.: p / 0), then E(u\D) ^ 0 and the OLS est Equation (1) will be biased. The intuition underlying the Heckman procedure is to con bias by estimating the inverse Mills' ratio (MILLS) from Equation (2): (p(a0Z + ol[X)/d>(ocoZ + ot[X) if D = 1
–