Jonathan E. Shipman

Bio: Jonathan E. Shipman is an academic researcher from University of Arkansas. The author has contributed to research in topics: Audit & Goodwill. The author has an hindex of 9, co-authored 21 publications receiving 1081 citations.

Propensity Score Matching in Accounting Research

Abstract: Propensity score matching (PSM) has become a popular technique for estimating average treatment effects (ATEs) in accounting research. In this study, we discuss the usefulness and limitations of PSM relative to more traditional multiple regression (MR) analysis. We discuss several PSM design choices and review the use of PSM in 86 articles in leading accounting journals from 2008–2014. We document a significant increase in the use of PSM from zero studies in 2008 to 26 studies in 2014. However, studies often oversell the capabilities of PSM, fail to disclose important design choices, and/or implement PSM in a theoretically inconsistent manner. We then empirically illustrate complications associated with PSM in three accounting research settings. We first demonstrate that when the treatment is not binary, PSM tends to confine analyses to a subsample of observations where the effect size is likely to be smallest. We also show that seemingly innocuous design choices greatly influence sample composi...

Propensity Score Matching in Accounting Research

Abstract: Propensity score matching (PSM) has become a popular technique for estimating average treatment effects (ATEs) in accounting research. In this study, we discuss the usefulness and limitations of PSM relative to more traditional multiple regression (MR) analysis. We discuss several PSM design choices and review the use of PSM in 86 articles in leading accounting journals from 2008-2014. We document a significant increase in the use of PSM from 0 studies in 2008 to 26 studies in 2014. However, studies often oversell the capabilities of PSM, fail to disclose important design choices, and/or implement PSM in a theoretically inconsistent manner. We then empirically illustrate complications associated with PSM in three accounting research settings. We first demonstrate that when the treatment is not binary, PSM tends to confine analyses to a subsample of observations where the effect size is likely to be smallest. We also show that seemingly innocuous design choices greatly influence sample composition and estimates of the ATE. We conclude with suggestions for future research considering the use of matching methods.

Auditing Goodwill in the Post‐Amortization Era: Challenges for Auditors

Abstract: The elimination of goodwill amortization in 2001 brought about significant change in how companies are required to account for goodwill. This change in accounting also brought with it new challenges for auditors, namely evaluating the reasonableness of management's assumptions related to goodwill valuation. In addition to introducing technical challenges, this task is particularly difficult given the misalignment in incentives it creates between managers who likely prefer to avoid recording an impairment and auditors who seek to minimize the bias in management's impairment testing. This study focuses on the consequences of the misaligned incentives that auditors face under the current goodwill assessment process. We find that the decision to record a goodwill impairment is associated with an increase in the probability of auditor dismissal. Consistent with the presence of significant friction with clients, our results also indicate that the likelihood of auditor dismissals is negatively related to the favorability of the impairment decision. Furthermore, we find that companies impairing goodwill prior to dismissing auditors subsequently employ auditors that are, on average, more favorable to clients in their impairment decisions. Audit de l'ecart d'acquisition apres suppression de l'amortissement : un defi pour les auditeurs La suppression de l'amortissement de l'ecart d'acquisition (goodwill) en 2001 a engendre une importante modification dans la facon dont les societes doivent comptabiliser l'ecart d'acquisition. Cette modification s'assortit de nouveaux defis pour les auditeurs, appeles a juger du caractere raisonnable des hypotheses de la direction en ce qui a trait a l'evaluation de l'ecart d'acquisition. En plus de poser des difficultes techniques, cette tâche est particulierement ardue en raison de la divergence des objectifs vises par les gestionnaires, enclins a eviter la comptabilisation d'une perte de valeur, et par les auditeurs, desireux de reduire le plus possible la subjectivite des tests de depreciation effectues par la direction. Les auteurs s'interessent plus particulierement aux consequences de cette problematique de divergence a laquelle sont exposes les auditeurs dans le processus actuel d'evaluation de l'ecart d'acquisition. Ils constatent que la decision de comptabiliser une perte de valeur de l'ecart d'acquisition est associee a une hausse de la probabilite de non‐reconduction du mandat de l'auditeur. Les resultats de l'etude, en concordance avec la presence de frictions importantes avec les clients, revelent egalement que la probabilite de non‐reconduction du mandat de l'auditeur est en relation negative avec le caractere favorable de la decision de l'auditeur quant a la perte de valeur. Les auteurs observent en outre que les societes qui reduisent la valeur de l'ecart d'acquisition avant de revoquer le mandat des auditeurs retiennent les services d'auditeurs qui sont, en moyenne, plus favorables aux clients dans leurs decisions quant a la perte de valeur.

Out of Control: The (Over)Use of Controls in Accounting Research

Abstract: In the absence of random treatment assignment, the selection of appropriate control variables is essential to designing well-specified empirical tests of causal effects. However, the importance of control variables seems underappreciated in accounting research relative to other methodological issues. Despite the frequent reliance on control variables, the accounting literature has limited guidance on how to select them. We evaluate the evolution in use of control variables in accounting research and discuss some of the issues that researchers should consider when choosing control variables. Using simulations, we illustrate that more control is not always better and that some control variables can introduce bias into an otherwise well-specified model. We also demonstrate other issues with control variables including the effects of measurement error and complications associated with fixed effects. Lastly, we provide practical suggestions for future accounting research.

Auditor Independence and Fair Value Accounting: An Examination of Nonaudit Fees and Goodwill Impairments

Abstract: Inadequate testing of fair value accounting estimates, including goodwill, is often cited as an audit deficiency in PCAOB inspection reports, and, in some cases, these deficiencies have led to enforcement actions against the auditor. As a result of these issues, the PCAOB recently proposed a new auditing standard for fair value accounting. While these regulatory actions suggest that auditors are challenged by the fair value regime of accounting for goodwill, they also highlight an area where the auditor could be influenced by their financial ties to a client. In this study, we test whether nonaudit fees are associated with goodwill impairment decision outcomes. Our results indicate that the nonaudit fees a client pays are inversely related to the likelihood of impairment in settings where goodwill is likely to be impaired. Additional examinations suggest that the negative relation between nonaudit fees and auditor independence is driven by clients who are most incentivized to exert their influence over the auditor. Independance de l'auditeur et comptabilite a la juste valeur : une etude des honoraires pour services non lies a l'audit et de la depreciation du goodwill Les tests inadequats des estimations comptables de la juste valeur, notamment celle du goodwill, sont souvent evoques comme deficience de l'audit dans les rapports d'inspection du PCAOB, deficience qui, dans certains cas, a mene a l'application de sanctions aux auditeurs. C'est pourquoi le PCAOB a recemment propose une nouvelle norme d'audit pour la comptabilisation a la juste valeur. Bien que ces mesures reglementaires donnent a penser que les auditeurs sont aux prises avec les difficultes que souleve le regime de comptabilisation du goodwill a la juste valeur, elles mettent aussi en lumiere un aspect du travail dans lequel l'auditeur pourrait etre influence par ses liens financiers avec le client. Les auteurs verifient dans leur etude si les honoraires pour services non lies a l'audit sont associes aux consequences des decisions relatives a la depreciation du goodwill. Les resultats de leur analyse indiquent que les honoraires pour services non lies a l'audit que verse un client sont en relation inverse avec la probabilite de depreciation dans les situations ou la depreciation du goodwill est probable. Des analyses supplementaires semblent indiquer que la relation negative entre les honoraires pour services non lies a l'audit et l'independance de l'auditeur est tributaire des clients qui sont le plus motives a exercer leur influence sur l'auditeur.

《The influence of institutional investors on myopic R＆D investment behavior》述评

Abstract: 机构投资者作为证券市场中的重要力量,越来越受到理论界和实务界的关注。论文对宾夕法尼亚大学沃顿商学院会计学教授布赖恩-布希（Brian Bushee）的论文＂The influence of institutional investors on myopic R＆D investment behavior＂（机构投资者对企业短视研发投资行为的影响,以下简称Bushee（1998））进行评价并提出相关的建议和研究方向。

Do Client Characteristics Really Drive the Big N Audit Quality Effect? New Evidence from Propensity Score Matching

Abstract: A large auditing literature concludes that Big N auditors provide higher audit quality than non-Big N auditors. Recently, however, a high-profile study suggests that propensity score matching (PSM) on client characteristics eliminates the Big N effect [Lawrence A, Minutti-Meza M, Zhang P (2011) Can Big 4 versus non-Big 4 differences in audit-quality proxies be attributed to client characteristics? Accounting Rev. 86(1):259–286]. We conjecture that this finding may be affected by PSM’s sensitivity to its design choices and/or by the validity of the audit quality measures used in the analysis. To investigate, we examine random combinations of PSM design choices that achieve covariate balance, and four commonly used audit quality measures. We find that the majority of these design choices support a Big N effect for most of the audit quality measures. Overall, our findings show that it is premature to suggest that PSM eliminates the Big N effect. This paper was accepted by Suraj Srinivasan, accounting.

Linear Models in Statistics

Abstract: the book provides a brief introduction to SAS, SPSS, and BMDP, along with their use in performing ANOVA. The book also has a chapter devoted to experimental designs and the corresponding ANOVA. In terms of coverage, a nice feature of the book is the inclusion of a chapter on Ž nite population models—typically not found in books on experimental designs and ANOVA. Several appendixes are given at the end of the book discussing some of the standard distributions, the Satterthwaite approximation, rules for computing the sums of squares, degrees of freedom, expected mean squares, and so forth. The exercises at the end of each chapter contain a number of numerical problems. Some of my quibbles about the book are the following. At times, it simply gives expressions without adequate motivation or examples. A reader who is not already familiar with ANOVA techniques will wonder as to the relevance of some of the expressions. Just to give an example, the quantity “sum of squares due to a contrast” is deŽ ned on page 65. The algebraic property that the sums of squares due to a set of a ƒ 1 orthogonal contrasts will add up to the sum of squares due to an effect having a ƒ 1 df is then stated. Given the level of the book, discussion of such a property appears to be irrelevant. I did not see this property used anywhere in the book; neither did I see the sum of squares due to a contrast explicitly used or mentioned later in the book. Examples in which the one-way model is adequate are mentioned only after introducing the model and the assumptions, and the examples are buried inside the remarks (in small print) following the model. This is also the case with the two-way model with interaction (Chap. 4). The authors indicate in the preface that the remarks are mostly meant to include results to be kept out of the main body of the text. I believe that good examples should be the starting point for introducing ANOVA models. The authors present the analysis of Ž xed, random, and mixed models simultaneously. Motivating examples that distinguish between these scenarios should have been made the highlight of the presentation in each chapter rather than deferred to the later part of the chapter under “worked out examples” or buried within the remarks. The authors discuss transformations to correct lack of normality and lack of homoscedasticity (Sec. 2.22). However, these are not illustrated with any real examples. Regarding tests concerning the departure from the model assumptions, formal tests are presented in some detail; however, graphical procedures are only very brie y mentioned under a remark. I consider this to be a glaring omission. Consequently, I would be somewhat hesitant to recommend this book to anyone interested in actual data analysis using ANOVA unless the application is such that one of the standard models (along with the standard assumptions) is known to be adequate and diagnostic checks are not called for. Obviously, this is an unlikely scenario in most applications. The preceding criticisms aside, I can see myself consulting this book to refer to an ANOVA table, to look up an expected value or test statistic under a random or mixed-effects model, or to refer to the use of SAS, SPSS, or BMDP for performing ANOVA. The book is indeed an excellent source of reference for the ANOVA based on Ž xed, random, and mixed-effects models.

Proxies and Databases in Financial Misconduct Research

Abstract: An extensive accounting and finance literature examines the causes and effects of financial misreporting or misconduct based on samples drawn from four popular databases that identify restatements, securities class action lawsuits, and Securities and Exchange Commission (SEC) Accounting and Auditing Enforcement Releases (AAERs). We show, however, that the results from empirical tests can depend on which database is accessed. To examine the causes of such discrepancies, we compare the information in each database to a detailed sample of 1,243 case histories in which the SEC brought enforcement action for financial misrepresentation. These comparisons allow us to identify, measure, and estimate the economic importance of four characteristics of each database that affect inferences from empirical tests. First, these databases contain information on only the event that is used to proxy for misconduct (e.g., restatements), so they omit other relevant announcements that affect a researcher’s interpretation and use of the events. Second, the initial public revelation of financial misconduct occurs, on average, months before the initial coverage in these databases, leading to discrepancies in event study measures and pre/post comparison tests. Third, most of the events captured by these databases are unrelated to financial fraud, and efforts to cull out non-fraud events yield heterogeneous results. Fourth, the databases omit large numbers of events they were designed to capture. We show the extent to which each database is subject to these concerns and offer suggestions for researchers seeking to use these databases.

Influential Observations and Inference in Accounting Research

Abstract: Capital market accounting research (CMAR) studies routinely encounter observations taking on extreme values that are likely to affect statistical inferences. Ex-ante univariate approaches such as truncation and winsorization are the most common methods used in CMAR to mitigate the effect of extreme data values. While expedient, each relies on researcher-selected cut-offs which possibly alter legitimate observations in the process. More importantly, there is no empirical evidence in CMAR that either approach is effective at identifying and dealing with the effect of influential observations. We document the efficacy and trade-offs associated with using winsorization, truncation, and robust regression to address the effects of influential observations in CMAR. We first replicate three published CMAR studies to show how the approaches can yield different estimates and statistical inferences. We then use simulations to compare the approaches in controlled settings where we hold the data-generating process constant. The results indicate that robust regression generally outperforms winsorization and truncation, especially in the presence of unusual (or infrequent) economic events that are correlated with the dependent and independent variables of interest. The findings lead us to recommend that future CMAR studies consider using robust regression, or at least report sensitivity/robustness tests using robust regression, especially because robust regression focuses on overall model fit to deal with influential observations, in addition to being relatively straight-forward to implement in typical CMAR settings.