An Empirical Study of Piercing the Corporate Veil - Berkeley Law

Why Courts Pierce:An Empirical Study of Piercing the Corporate Veil

John H. Mathesont

"The whole problem of the relation between [owners and their]corporations is one that is still enveloped in the mists of metaphor. Metaphorsin law are to be narrowly watched, for starting as devices to liberate thought,they end often by enslaving it. "

"Do you notice anything intellectually disturbing about this [standardpiercing-the-corporate-veil] formulation? That's right; it's vague. It hardlygives you any concrete idea about which conduct does or does not trigger thedoctrine - not enough of an idea, at least, to give you the ability to counselclients in a meaningful way."2

TABLE OF CONTENTS

Introduction.............................3..... ............... 3I. Piercing Doctrine and the Importance of Statistical Analysis...................5II. Methodology and Hypotheses.................................9

A. Case Selection ............................ ............... 10B. Variables .......... l................................1C. Overview of Statistical Methods.........................13

1. Descriptive Statistics..............................132. Logistic Regression..........................13

III. Descriptive Statistics ........................................... 14A. Piercing the Corporate Veil Generally ................. 14B. Court Information ....................................... 17

1. Jurisdiction............... ..................... 172. Court Level .......................... ........... 18

C. Party Information: Type of Plaintiff ........................ 19D. Type of Case: Underlying Cause of Action ...... ........... 20E. Piercing Factors ............................. 29

1. Fraud/Misrepresentation .......... ................. 32

t Law Alumni Distinguished Professor of Law, University of Minnesota Law School; Of Counsel,Kaplan, Strangis and Kaplan, P.A., Minneapolis, Minnesota. I want to thank my excellent researchassistants, Maria de Lourdes B. Dooner, Laurie E. Kellogg, Eric S. Taubel and Emily M. Van Vliet.Any errors or omissions, however, are mine.

1. Berkey v. Third Ave. Ry. Co., 155 N.E. 58, 61 (N.Y. 1926) (Cardozo, J.).2. ROBERT CHARLES CLARK, CORPORATE LAW 38 (1986).

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2. Owner Control/Dominance..........................323. Commingling of Funds.............................334. Undercapitalization...............................335. Non-Functioning.................................346. Overlap........................................357. Unfairness/Injustice...............................358. Non-Existent....................................359. Assumption of Risk...............................36

IV. Logistic Regression Analysis.................................36A. Overall Model..................... ................. 48B. Court Information...................................50C. Party Information....................................50D. Type of Claim......................................51E. Piercing Factors.....................................51

1. Fraud/Misrepresentation............................522. Owner Control/Dominance..........................533. Commingling of Funds.............................544. Undercapitalization...............................555. Non-Functioning................. ................ 556. Overlap........................................567. Fairness........................................568. Non-Existent....................................579. Assumption of the Risk............. .................... 57

Conclusion................................................. 58Appendix. .................................................. 61

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Why Courts Pierce: An Empirical Study of Piercing the Corporate Veil

Why Courts Pierce:An Empirical Study of Piercing the Corporate Veil

INTRODUCTION

Limited liability of business owners for the contracts, torts and otherliabilities of their companies has been commonplace for over one hundred andfifty years. This concept of limited liability means that a business owner'spotential personal loss is a fixed amount, namely, the amount invested in thebusiness, usually in the form of stock ownership. Consequently, if the businesssucceeds, the owner obtains the profits, but if the business fails, all of the lossesbeyond the owner's fixed investment are absorbed by others, that is, voluntaryor involuntary creditors,4 or society at large. Although initially applicableprimarily to corporations, new forms of business organizations have appeared,such as limited liability partnerships and limited liability companies, which alsooffer limited liability to their owners.

Although limited liability for business owners is common, it is notuncontroverted. From the very beginning, relieving business owners of liabilityfor the operations of the business has had proponents and detractors. In the1800s, Thomas Cooper described corporate limited liability as a "mode ofswindling, quite common and honourable in these United States" and "a fraudon the honest and confiding part of the public." 6 In rhetorical counterpoint,President Nicholas Butler of Columbia University proclaimed limited liabilityas "the greatest single discovery of modem times," and that "[e]ven steam andelectricity are far less important than the limited liability corporation, and theywould be reduced to comparative impotence without it."7 The academic debateover the propriety of limited liability continues unabated.

3. For a discussion of the evolution of business limited liability see John H. Matheson & Brent A.Olson, A Callfor a Unified Business Organization Law, 65 GEO. WASH. L. REV. 1, 5-9 (1996).

4. The classic "voluntary creditor" is a party to a contract with the business. "Involuntary creditor"will be used throughout this article to mean those creditors who did not enter into a debtor-creditorrelationship of their own free will. The clearest example of this category would be a person againstwhom the business committed some tortious act, such as negligent injury of a pedestrian by a companydelivery-truck driver.

5. See John H. Matheson & Raymond B. Eby, The Doctrine of Piercing the Veil in an Era ofMultiple Limited Liability Entities: An Opportunity to Codify the Test for Waiving Owners' Limited-Liability Protection, 75 WASH. L. REV. 147, 157-72 (2000) (discussing the evolution of modem businessforms).

6. THOMAS COOPER, LECTURES ON THE ELEMENTS OF POLITICAL ECONOMY 247 (2nd ed., A.M.Kelley 1971) (1829).

7. NICHOLAS MURRAY BUTLER, WHY SHOULD WE CHANGE OUR FORM OF GOVERNMENT?STUDIES IN PRACTICAL POLITICS 82 (1912).

8. Compare, e.g., Daniel J. Morrissey, Piercing All the Veils: Applying an Established Doctrine toa New Business Order, 32 IOWA I. CORP. L. 529, 533 (2007) (arguing that, because "LLCs and LLPsoffer their members and partners more direct management power than usually afforded shareholders,"

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This ambivalence over the propriety of limited business liability is reflectedin the courts in the form of veil piercing. Piercing the corporate veil is a

common law legal doctrine used to break rules of traditional limited liability forowners, and to hold shareholders accountable as though the corporation'saction was the shareholders' own. In deciding whether to pierce the veil, courtslook to sometimes disparate factors and often use unhelpful, conclusorycharacterizations such as "alter ego" and "instrumentality" to describe therelationship between the shareholders and the corporation. 9 While "[p]iercingthe corporate veil is the most litigated issue in corporate law,"' 0 common lawpiercing is complex, inconsistently applied and often poorly understood.

The empirical project presented by this Article is unique. This study is thefirst to empirically examine the distinct question of substantive common lawpiercing of the corporate veil. As a matter of pure hypothesis, one wouldexpect that any common law doctrine should be applied by the courts in aneutral manner, that is, evenhandedly except for variations in factors explicitlyand specifically identified as part of the applicable test. Given thatpresumption, the empirical results of this study, even on a descriptive level, arestartling. Among the statistically significant findings are:

* Courts pierce twice as often to hold individual persons liable thanthey do to hold entities, such as corporations and limited liabilitycompanies (parent-subsidiary piercing), liable.

* Entity plaintiffs are almost twice as likely as individual plaintiffs tosuccessfully pierce the corporate veil.

* Courts are more likely to pierce to enforce a contract claim than toaward recovery to a tort claimant

* The 'kitchen-sink' approach to piercing litigation (adding as manypossible substantive claims as possible) is not as effective asbringing a single claim.

More fundamentally, this Article is the first to apply to substantive piercingthe advanced statistical techniques of quantitative analysis." This study has

there is "greater justification to hold them personally accountable for the obligations of theirbusinesses") and Nina A. Mendelson, A Control-Based Approach to Shareholder Liability for CorporateTorts, 102 COLUM. L. REV. 1203, 1271-79 (2002) (espousing unlimited liability for torts in the singleshareholder and parent-subsidiary context in regard to capacity to control), with Stephen M. Bainbridge,Abolishing LLC Veil Piercing, 2005 U. ILL. L. REv. 77, 79 (2005) ("the case against veil piercing...applies with equal force to LLCs as to corporations.").

9. The failure of courts' attempts to articulate a single test for disregarding the corporate form andholding the owners of a corporation responsible for the business's financial obligations has resulted in anumber of overlapping lists of factors that are passed off as tests. See, e.g., Richard v. Bell Ati. Corp.,946 F. Supp. 54, 61 (D.C. 1996) (setting out four different tests); Laya v. Erin Homes, Inc., 352 S.E.2d93, 98-99 (W. Va. 1986) (listing nineteen factors); Victoria Elevator Co. v. Meriden Grain Co., 283N.W.2d 509, 512 (Minn. 1979) (listing eight factors).

10. Robert B. Thompson, Piercing the Corporate Veil: An Empirical Study, 76 CORNELL L. REV.1036,1036 (1991).

I1. The current database consists of all substantive piercing cases for the relevant period. A

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produced a number of key findings brought to light only through theimplementation of logistic regression methodology. Among these findings are:

* Pure descriptive statistics indicate that the relationship betweenplaintiff type (i.e. individual or entity) and claim type (tort,contract, etc.) is statistically significant, as are the relationshipsseparately between plaintiff type and piercing and between claimtype and piercing. This would suggest that either claim type orplaintiff type, or both, would have a statistically significant effecton piercing. However, this does not prove to be true when thesehypotheses are tested in the regression models. That is, eventhough these descriptive statistics tell us when courts pierce, theydo not explain why courts pierce.

* Fraud, owner control, and commingling of funds have the strongestand most predictive relationship with piercing the corporate veil.Indeed, the presence or absence of these factors alone is usuallydispositive of the piercing decision.

* Conversely, factors reflecting the lack of operational formalities,such as non-existence or non-functioning of corporate directors orofficers, are not significantly related to piercing in the regressionmodels.

* While only discussed in 3% of cases, assumption of the risk has alarge impact on incidence of piercing the corporate veil. It is theonly factor that applies to plaintiffs, and when a court finds itpresent the likelihood of a pierce is drastically reduced.

Part I presents a brief discussion of the problems with doctrinal piercingjurisprudence and an explanation of the need for and methods of logisticregression analysis. Part II presents the research design and explains themethodology employed in this study for capturing the relevant data. Part IIIpresents the empirical results of this study in descriptive form. Part IV presentsthe results and analysis of the logistic regression process.

I. PIERCING DOCTRINE AND THE IMPORTANCE OF STATISTICAL ANALYSIS

Commentators have frequently criticized the courts' reliance on conclusoryterms as producing results-oriented decisions when applying the piercingdoctrine.12 From an academic perspective, the courts are vague and

previous study split off a subset of parent-subsidiary cases for separate analysis based on the uniqueissues raised in that context. See John Matheson, The Modern Law of Corporate Groups: An EmpiricalStudy of Piercing the Corporate Veil in the Parent-Subsidiary Context, 87 N.C. L. REV. 1091 (2009).

12. See, e.g., Stephen M. Bainbridge, Abolishing Veil Piercing, 26 IOWA J. CORP. L. 479, 513(2001) ("Judicial opinions in this area tend to open with vague generalities and close with conclusorystatements with little or no concrete analysis in between. There simply are no [sic] bright-line rules fordeciding when courts will pierce the corporate veil.").

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inconsistent, providing little analysis of underlying facts and failing to discussthe policy rationales.' 3 Veil piercing "seems to happen freakishly. Likelightning, it is rare, severe, and unprincipled."1 4

This ad hoc judicial approach to piercing does not diminish the desire ofpractitioners and business owners to understand and predict how courts willrespond to piercing claims. Research that improves understanding of thejudicial application of piercing doctrine offers large practical value because ofthe frequency of piercing litigation and the lack of an easily understood bright-line rule. More fundamentally, disparate judicial results and apparentinconsistencies demonstrate the need for rigorous empirical and statisticalanalysis of actual court results.

A limited amount of previous piercing research has been done, principallyin the form of an article by Professor Robert Thompson in 1990, counting andcategorizing piercing cases up to that time. I5 Thompson categorized casesbased on the absence or presence of certain individual piercing factors. TheThompson study included jurisdictional cases as well as substantive piercingcases, statutory cases as well as common law ones, and reverse piercing casesas well.

13. Stephen Bainbridge has complained that use of veil piercing is "rare, unprincipled, andarbitrary." Id. at 535. See also Davis Millon, Piercing the Corporate Veil, Financial Responsibility,and the Limits of Limited Liability, 56 EMORY L.J. 1305, 1327 (2007) (describing veil-piercing factorsas an "unweighted laundry list"). At times, however, the academics are no clearer. See, e.g., FREDERICKJ. POWELL, PARENT AND SUBSIDIARY CORPORATIONS 9 (1931) (listing 11 factors for application ofinstrumentality rule); Cathy S. Krendl & James R. Krendl, Piercing the Corporate Veil: Focusing theInquiry, 55 DEN. L.J. 1, 52-55 (1978) (a 31 point checklist).

14. Frank H. Easterbrook & Daniel R. Fischel, Limited Liability and the Corporation, 52 U. CHI. L.REV. 89, 89 (1985).

15. The prior empirical work on substantive piercing involved simple descriptive statistics - that is,a counting and categorization of cases, with no quantitative statistical analysis. See, e.g., Thompson,supra note 10, at 1044-47. Professor Thompson subsequently purported to extend his original databasethrough 1996 but once again simply counted and categorized cases. Robert B. Thompson, Piercing theVeil Within Corporate Groups: Corporate Shareholders as Mere Investors, 13 CONN. J. INT'L L. 379,385 (1999). In 2008, two authors, apparently then students, attempted to update Thompson's originalstudy by considering a "random sampling of cases reported in Westlaw from January 1, 1986 throughDecember 31, 1995." Lee C. Hodge & Andrew B. Sachs, Piercing the Mist: Bringing the ThompsonStudy into the 1990s, 43 WAKE FOREST L. REV. 341, 347 (2008). See also Geoffrey Christopher Rapp,Preserving LLC Veil Piercing: A Response to Bainbridge, 31 IOWA J. CORP. L. 1063, 1068-69 (2006)(addressing sixty-one LLC piercing cases from 1997 to 2005).

16. Thompson, supra note 10, at 1044-45. Inclusion of reverse piercing cases is particularlyproblematic, since these cases seek to benefit individual owners by ignoring the corporate veil to providebenefits, not to impose liability. To include them in a database attempting to describe when courtspierce to hold owners liable results in an exaggerated piercing count. Jurisdictional cases should also beexcluded. Piercing in these cases is not a question of the ultimate liability of the owner but ratherwhether the owner can be made a part of the current lawsuit - that is, whether jurisdiction can beexercised over the owner as a matter preliminary to any adjudication of piercing or ultimate liability. Itis generally acknowledged that the courts tend to apply a different and more expansive approach forthose jurisdictional situations. See, e.g., D. Klein & Son, Inc. v. Good Decision, Inc., 147 Fed. App'x.195, 196 (2d Cir. 2005) ("the exercise of personal jurisdiction over an alleged alter ego ... requiresapplication of a 'less onerous standard' than that necessary for equity to pierce the corporate veil forliability purposes under New York law"). These cases were the focus of a separate study. John A.Swain & Edwin E. Aguilar, Piercing the Veil to Assert Personal Jurisdiction Over Corporate Affiliates:

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Although Professor Thompson's study was an important beginning, it wasaimed primarily at simple case counting and categorization. Thus, it sufferedfrom significant statistical limitations. As Professor Fred McChesney noted inhis landmark article identifying the limitations of case counting alone, "thedifficulty of identifying standards in any line of cases . . . may lie as much in

the deficiencies of legal research techniques as in any judicial 'fuzziness."" 7

In that vein, he found prior analytical and empirical work on piercing wanting:Though clearly an advance in the level of corporate law discourse, the rethinking ...carried forward by Thompson is not wholly satisfactory methodologically. Merelycounting cases and sorting them into various pigeonholes according to expressedjudicial rationales (the process used by Thompson for veil-piercing cases and by Frey

18in studying defective incorporation) suffers from . . . deficiencies ... .

One major deficiency of simple case counting and resultant descriptivestatistics is the inability to adequately account for multi-factor tests, such asthose used in veil-piercing cases.19 Several factors or combinations of factorsmay explain case results rather than individual factors. Moreover, casecounting often fails to account for the possible affects of other variables,whereas regression analysis allows for other variables of interest to becontrolled (or held constant). Additionally, any statistical review of a multi-factor judicial analysis may run into the problems of assigning weights to thevarious factors and isolating the separate affects of factors that may operatesimultaneously. While case counting based on individual factors providessome data, it is simply unable to measure the relative strength or interplay ofthose associations. 20

An Empirical Study of the Cannon Doctrine, 84 B.U. L. REV. 445, 446 (2004). Thompson found that, incases involving piercing the veil for jurisdictional purposes, the plaintiffs' success rate was 36.8%.Thompson, supra note 10, at 1060. For venue purposes, however, Thompson found that the plaintiffs'success rate was 58.3%, a higher piercing rate than in the substantive veil piercing cases. See id.

17. Fred S. McChesney, Doctrinal Analysis and Statistical Modeling in Law: The Case ofDefectiveIncorporation, 71 WASH. U.L.Q. 493, 495 (1993). Professor McChesney's article was "the first to usemultiple regression to discern the separate legal reasons for judicial decisions in a purely common-lawdomain." Id. at 519. The statistical techniques McChesney used have been employed subsequently byother authors. See, e.g., Larry A. DiMattco & Bruce Louis Rich, A Consent Theory ofUnconscionability: An Empirical Study ofLaw in Action, 33 FLA. ST. U. L. REV. 1067, 1093-94 (2006).

18. McChesney, supra note 17, at 515.19. Id.20. The Thompson study and those similar to it include only descriptive statistics with no tests for

statistical significance. While Thompson's study shows the distribution of cases and is suggestive ofhow factors effect piercing, his study is not able to compare those findings to determine which has thelarger effect. Indeed, Professor Thompson recognized the limitations of such simple case-countingdescriptive statistics at the time of his study. McChesney, supra note 17, at 515 n.82 ("Thompson isaware of the methodological shortcomings of merely sorting cases, and reports that he is at work on amultiple regression model for the veil-piercing cases."). McChesney's citation is to footnote 62 ofThompson's original article, where Thompson states:

In an additional article in progress, I use this data and a logit analysis, a form of statistical

regression analysis, to test the relationship between a dependent variable, here the court's

decision to pierce the veil, and independent variables here the various factors recorded in the

data set. Not surprisingly, the "conclusory" indicators of alter ego and instrumentality are the

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Multiple regression analysis, however, is "a statistical technique that cansolve the problems of calculating the influence of individual case factors,identifying their relative weights, and accounting for the simultaneous presenceof different factors."21 Simply put, case-counting research at best attempts todescribe when, or under what circumstances, a court will pierce the corporateveil. This Article, in contrast, focuses on the question of why courts eitherpierce or do not pierce the corporate veil. 22

In order to do so, this study employs multiple regression analysis,specifically a logistic model.23 The data generated by a logistic regressionmodel can be used to assess the impact of the independent (predictor) variableson the likelihood of the dependent (outcome) variable occurring, that is,whether a court will pierce. This heightened level of analysis is critical tounderstanding the courts' piercing decisions. Consider one exampledemonstrated in this Article: case counting alone indicates that tort (vs.contract) claims are significantly less successful in achieving a piercing, butthis study empirically demonstrates that, while claim type and piercing areassociated, claim type does not statistically significantly affect the likelihoodthat a court will pierce the corporate veil.24

Given the zero-sum nature of legal disputes, logistic regression can be apowerful tool for practitioner as well as academics. Logistic models generateequations based on known data such that future outcomes can be predicted byapplying the regression equation to new cases. In the models presented belowthe equations generated accurately predicted the court's decision withapproximately 90% accuracy.25 The models presented can be used to make

factors most closely associated with a piercing result. The explanation of that model and theresults are left for another day.

Thompson, supra note 10, at 1046 n.62. Although Thompson recognized the need for a moresophisticated "logit analysis, a form of statistical regression analysis," the supposed "model and theresults" have never been reported. Id.

21. McChesney, supra note 17, at 519.22. "Indeed, as Franklin Fisher has observed, it is difficult to see how anyone could reach

conclusions in legal proceedings involving large-sample, multivariable situations without resort tomultiple regression." Id. at 519-20 (citing Franklin M. Fisher, Multiple Regression in LegalProceedings, 80 COLUM. L. REv. 702, 730 (1980)). See generally Michael 0. Finklestein, RegressionModels in Administrative Proceedings, 86 HARV. L. REV. 1442 (1973) (addressing the use of multipleregression analysis in administrative proceedings); Daniel M. Rubinfeld, Econometrics in theCourtroom, 85 COLUM. L. REV. 1048 (1985) (addressing the use of multiple regression analysis in courtproceedings).

23. Logistic regression is the preferable regression method when the dependent (outcome) variablehas only two response categories. ALAN AGRESTI, AN INTRODUCTION TO CATEGORICAL DATAANALYSIS 103 (1996). For an extended explanation of the technique and benefits of logistic regressionanalysis in a categorical data context see Matheson, supra note I1, at 1133-37.

24. Compare Tables 7.1A and 7.1B with the effect of claim type in Models IA-4A and Models IB-4B in the Appendix.

25. Alternative measures of the effectiveness of the model suggests that the model accounts for 50-75% of the variance in the outcome variable (piercing), or that using the model generated increasespredictive accuracy by 50-75% (over a model with no independent variables). See Model Summary for

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strategic litigation decisions, as well as provide valuable insight for attorneysadvising business owners. Findings that show which factors have the largestimpact on the odds of a piercing result can be used to determine which factorsshould be emphasized at trial. Conversely, lawyers will be better able to advisedefendants on whether they should settle by determining which factors are mostlikely to be found at trial and making a more informed risk assessment. Thefindings presented below suggest that when owners are establishing newentities, they need not emphasize operational or corporate formalities in orderto maintain limited liability. 2 6

II. METHODOLOGY AND HYPOTHESES

This project has two primary objectives. The initial goal is to statisticallydescribe the propensities of modem courts for substantive piercing of thecorporate veil. This is the first study to document these judicial proclivities. Inconnection with these descriptive statistics, it is important to explore whetherdifferences in piercing results, such as the level of piercing in cases ofindividual liability as compared to all piercing cases, are statistically significantaccording to accepted quantitative measures of statistical analysis. The secondaim of this study is to move beyond descriptive statistics and explore potentialcausal relationships regarding piercing the veil. These causal relationshipswould inform academic debate and help legal counsel to advise clients based onthe facts in a particular dispute. To that end, the study is designed to test thefollowing major hypotheses involving substantive piercing, among others:

1. Piercing occurs with the same frequency when an entity is theowner (i.e. parent-subsidiary) as when the owners are individuals.

2. Trial courts pierce at the same frequency as appellate courts.3. Courts pierce with the same frequency whether the plaintiff is a

corporate entity or an individual.4. Courts pierce with the same frequency irrespective of the

underlying cause of action, that is, for example, whether a contractclaim is brought as opposed to a tort claim.

5. Each piercing factor identified by the courts is independentlysignificant. That is, the presence of any one factor should beenough to cause a court to pierce.

6. The absence of any single factor will not be enough to prevent acourt from piercing the corporate veil. 27

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Tables 9A, 9B, 10A, and 10B.26. See discussion infra pp. 55-56 and 59-60.27. The crux of the final two hypotheses is that any factor is sufficient to trigger a pierce, and yet

none is necessary.


A. Case Selection

This study employs a database of every case involving piercing in theUnited States from January 1, 1990 to April 1, 2008.28 Cases were selectedusing targeted electronic searches.29 The overall database includes reportedand unreported cases in state and federal courts and every effort was made toinclude only a final decision for a particular case. However, it is important tonote that this database does not account for unreported opinions not availablefrom Westlaw or Lexis, or for cases settled outside of court.

This Article examines substantive piercing for two datasets constructedfrom the database. The first includes all substantive piercing cases.30 Thisdataset was constructed to include all attempts to hold individual owners andparent entities liable, but excludes cases of statutory, reverse, horizontal, andjurisdictional piercing. 31 The second dataset is a sub-dataset that excludesparent-subsidiary piercing. 32 This sub-dataset only includes cases where courts

28. This is the same database from which a subset of parent-subsidiary piercing cases was used inMatheson, supra note 11.

29. The following searches were run for the relevant period:Westlaw searches in ALL STATE and ALL FEDERAL databases. Westlaw Home Page,http://www.westlaw.com.

* SY,DI(pierc! /5 "corporate veil") & da(after 1/l/1990)-yielded 1759 cases* topic(101) /p pierc! /5 "corporate veil" yielded 2275 cases* 101kl.3-yielded 758 cases* 101kl.4-yielded 2519 cases* 101kl.5-yielded 1091 cases* 1OlkI.6-yieldcd 3078 cases* 101kl.7-yielded 1624* Corporate Is "alter ego" (disregard! pierc! Is veil entity form) (single Is business enterprise) &

DA(AFT 1989)-yielded 8739 casesLexis Nexis In-Summary Searches. Lexis Nexis Home Page, http://www.lexisnexis.com.

* (pierc! /5 veil! parent! subsid!)* pierce and corporate veil and (alter ego or instrumentality)

30. Searches constructed for both Lexis and Westlaw resulted in 3,129 piercing cases. However,2,099 of these cases were excluded from the examined database for at least one of the following reasons:the case 1) failed to reach the merits of the piercing issue; 2) had a statutory basis for piercing; 3)pierced to gain jurisdiction over a party; 4) constituted horizontal piercing; or 5) constituted reversepiercing. The remaining 929 cases constitute the overall dataset.

31. This is a significant point of contrast to the Thomspon research, supra note 10, which includesall forms of piercing. Studies involving databases containing all piercing cases are a jumble of variousunrelated situations. They include cases where the legislature has provided a specific statutory testoutside of the common law, such as in environmental litigation. These studies also include bothtraditional single entity piercing as well as corporate group horizontal piercing, where sibling entitiesmay be combined even though the ultimate owners are not held liable. They also capture all forms ofpiercing, including more lenient jurisdictional cases as well as cases that do not truly involve piercing,such as reverse piercing cases that provide benefits to corporate owners, instead of holding them liable.The decision to include only substantive piercing cases in the current project is based in part on thepremise that courts apply different standards for piercing in different piercing contexts. In contrast,examining all groups as one would result in meaningless statistical outcomes.

32. The excluded subset of cases is the subject of a separately published set of statistical findingsfor a parent-subsidiary piercing dataset. See Matheson, supra note I1.

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sought to hold individual owners, not parent corporations, liable.33 In bothdatasets, the relationship between the outcome variable (piercing the corporateveil) and theoretically important predictor variables was analyzed usingdescriptive statistics and logistic regression.

B. Variables

The cases were coded for 25 categorical variables. These variables fit intoeach of four different types of information: 1) court information; 2) partyinformation; 3) type of case; and 4) piercing factors. The variable of primaryinterest (the dependent variable) throughout the statistical analysis was ultimateliability, that is, whether or not the court pierced the corporate veil to holdowners liable. 34 The variables are described in Table 1.

Table 1: Variable InformationVariable Description

Pierce Did the court pierce the veil to hold thecompany's owner(s) liable?

O)

Jurisdiction Identifies whether the court hearing thecase was a Federal or State court.

Court Level Identifies whether the court hearing thecase was a district/trial,appellate/intermediate, supreme/highestcourt, or a US Bankruptcy Court.

33. This sub-datasct was created by excluding the 364 cases involving parent-subsidiary piercing,leaving 565 cases for the sub-dataset in which only individuals were potentially personally liable.

34. A "1" was used when the court decided to pierce; a "0" was used when the court did not pierce.Cases were coded by multiple research assistants, who were current law students familiar with theprocess of reading judicial decisions. Complete instructions for student coders are on file with theBerkeley Business Law Journal and available from the author. While using formal statistical measuresfor coder interreliability was considered, they were not utilized. Reliability measures consideredincluded: Cohen's kappa, Fleiss' kappa, Scott's pi, and Krippendorf's alpha. Because of the number ofcoders involved in the project and natural turnover due to the time period required to code such a largenumber of cases, there were no reliability measures available that would provide meaningful andaccurate measures of reliability.

35. The description of the variable uses the generic term "Company" because cases involvingalternative forms of limited liability organizations, such as limited liability companies and limitedliability partnerships, were also included in the research and the datasets, in addition to traditionalcorporations.

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State Identifies when a case was heard in statecourt, and if so in which state was it heard.

Plaintiff Identifies whether the plaintiff was anindividual or an entity.

0

Cause of Action Identifies whether the underlying cause ofaction is a contract, tort, statutory,criminal, or bankruptcy claim.36

Piercing Factors For each of the piercing factors below, thisvariable assesses whether the courtdiscussed the factor, and if so whether thecourt found the factor to be absent orpresent.

Fraud/Misrepresentation Did the owner(s) engage in fraud ormisrepresentation?

Owner Did the owners(s) to be held liable exertControl/Dominance direct control over the company or

dominate the company?Commingling of Funds Did the owner(s) comingle funds with the

company?'2 Undercapitalization Did the owner(s) properly capitalize the

(Sum)37 entity from the outset, drain the funds ofthe entity, and/or fail to keep the entityproperly capitalized in order to functioneffectively?

Undercapitalization Did the owner(s) properly capitalize the(start-up) entity from the outset?

'2 Undercapitalization Did the owner(s) drain the funds of theLV (ongoing) entity and fail to keep it properly

capitalized such that it could not functioneffectively?

Nonexistent Were some or all directors/officers/

36. In some cases the plaintiff(s) brought multiple claims. See tables 7.2A and 7.2B.37. Undercapitalization (Sum) is computed based on the premise that any positive finding of

undercapitalization is most controlling, and a finding that there is no undercapitalization will bear moreweight than non-discussion of another form of undercapitalization. Using these assumptions, whereboth undercapitalization variables are 0, the sum is 0. Where both are 1, the sum is 1. Where there isone 0 and one 1, the sum is 1. Where there is a 0 and a 99, the sum is 0. Where there is a I and a 99, thesum is 1. Finally, where there are two 99's, the sum is 99.

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managers or other functionaries neverappointed and/or were there no companyfinancial or other records?

Nonfunctioning When directors/officers/managers or otherfunctionaries were appointed, did theyfunction normally?

Overlap Did the owner(s) and the entity sharecommon activities/persons/places, such ascommon offices, common businessactivity, common employees, includingdirectors/officers/managers?

Assumption of Risk Did the plaintiff assume a risk such that thecourt should not pierce the corporate veil?

Fairness Fairness is a conclusory determination thatevaluates the findings with respect tofairness and justice?

C. Overview of Statistical Methods

1. Descriptive Statistics

For descriptive statistics, data is presented in percentages comparingpierced versus not pierced overall as well as among the many different factors.This provides a snapshot of potential trends in piercing cases. But a snapshotonly allows for conjecture as to the relationships that may exist among thediffering factors such as entity type (corporation, individual). Quantitativestatistical analysis is required to determine whether relationships exist amongthe variables.

2. Logistic Regression

Regression analysis is used to determine correlations between and amongdifferent variables. The analysis consists of a dependent or response variableand one or more independent or explanatory variables. In its simplest form, theregression output measures the correlation strength between an independent

38. Perceived differences based on percentages are misleading. What may seem different at firstglance may actually have a high amount of variability within and among the data. Therefore, adifference seen between two different factors may not actually be a real statistical difference. This is themain difference between descriptive statistics and inferential or inductive statistics. Descriptivestatistics allow for an overall quantitative picture of the data. Inferential statistics allow testing ofinferential relationships among data. JERROLD H. ZAR, BIOSTATISTICAL ANALYSIS 22-35 (5th ed.

2009).

13


variable (e.g. court type) and the dependent variable (e.g. whether a courtpierces). When more than one independent variable exists that can influencethe dependent variable, the tool of choice is multiple regression.

Choosing a statistical method requires an understanding of the limitationsof each method. Our data consist of a dichotomous categorical dependentvariable, meaning that the data fall into one of two qualitative categories-either the court pierces or does not pierce. These types of data are notappropriate for traditional regression analysis as the outcome would lead tononsensical results confounded by weight exerted by only two points.Therefore, to overcome the problems associated with categorical data, the toolof choice is logistic regression. This statistical test determines the amount ofvariance in the dependent variable that can be explained by the independentvariables. Ultimately, the test predicts the odds of an event such as the rate ofpiercing occurring given a set of independent variables.

III. DESCRIPTIVE STATISTICS

A. Piercing the Corporate Veil Generally

Throughout the descriptive statistics section, data is presented in pairedtables. The "A" tables contain data for the overall dataset. The "B" tablescontain the data for the sub-dataset which excludes parent-subsidiary cases andthus involves only cases where individual owners were sought to be heldpersonally liable.

Table 2A: Overall Piercing Results in Overall DatasetTotal Cases Pierce No Pierce % Pierce929 296 633 31.86%

Table 2B: Overall Piercing Results for Individual Liability CasesTotal Cases Pierce No Pierce % Pierce565 222 343 39.29%

As displayed above in Table 2A, the courts pierced the corporate veil in31.86% of the cases in the entire piercing dataset. This value is lower than thegeneral figure of 40.18% in Thompson's study.39 However, looking solely atcases of potential individual liability (when excluding the parent-subsidiary

39. Thompson, supra note 10, at 1048. Remember that Thompson's database contained all piercingcases involving unrelated situations. It included both traditional single entity piercing as well ascorporate group piercing. It also captured all forms of piercing, including both more lenientjurisdictional cases as well as cases that do not truly involve piercing, such as reverse piercing cases thatprovide benefits to corporate owners, instead of holding them liable. Id. at 1044-47.

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cases from the dataset), the courts pierced the corporate veil in 39.29% ofcases. And when solely looking at piercing in the parent-subsidiary context,courts pierced the corporate veil in 20.56% of the cases. 40 This indicates thatpiercing to hold individuals personally liable occurs approximately twice asoften as piercing in the parent-subsidiary context, where the potentially liableparty is a second legal entity such as a corporation or limited liability company.

Several factors may account for this marked difference. The most obviousexplanation is that parent-subsidiary cases present factual situations that thecourts find generally less favorable to piercing. The factors that often supportholding individual owners of a small business liable, such as commingling ofassets and failure to follow corporate formalities, may simply appear less oftenin entity cases. Thus, it may be that individual owners are sloppier or followthe rules less frequently than do corporate owners.

Cases involving the typical small business that has only one or just a fewindividual owners may also present a fundamentally different analyticalchallenge for the courts than do parent-subsidiary situations. Parent-subsidiarypiercing claims may involve relatively large entities at both parent andsubsidiary levels, each of which has a substantial economic reality. This factor,combined with the concept of the corporation as a separate legal persongenerally, may create a form of reification for the entities in this context.Moreover, as with other business litigation, the general intractability of dealingwith complex business organizations may present its own juridical obstacles.For many judges, the attempt to sort out these corporate relationships andrelated equities may simply fall into the "too hard" pile. Nevertheless, thebottom line is clear: courts are significantly more likely to hold individualowners liable than they are to pierce to hold parent business entities liable.

Thle tA- Piercing Rates over Time

199U '4/ 14 ii -1____ 2 V. / 9"/

1991 55 17 38 30.91% _

1992 36 17 19 47.22% 34.78%1993 66 29 37 43.94% 40.13%1994 76 23 53 30.26% 38.76%

40. Matheson, supra note 11, at 1114.41. The weighted average was calculated by dividing the sum of the number of cases pierced for

the previous three years by the sum of the total number of cases for the previous three years.

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1995 73 26 47 35.62% 36.28%1996 18 4 14 22.22% 31.74%1997 29 9 20 31.03% 32.50%1998 52 15 37 28.85% 28.28%1999 53 14 39 26.42% 28.36%2000 54 15 39 27.78% 27.67%2001 65 21 44 32.31% 29.07%2002 91 26 65 28.57% 29.52%2003 66 16 50 24.24% 28.38%2004 56 22 34 39.29% 30.05%2005 28 7 21 25% 30.00%2006 21 9 12 42.86% 36.19%2007 31 10 21 32.26% 32.50%2008 12 2 10 16.67% 32.81%Total 929 296 633 31.86%

Table 3A shows the rate of piercing from 1990 to 2008. During this timeperiod, the average piercing rate is 31.86%. However, these rates fluctuatedrastically throughout this period and range from 47.22% in 1992 to 16.67% in2008. Based on these results, there is no strong indication of an increase ordecrease towards veil piercing over time.

Table 3B: Piercing Rates over Time for Individual Liability CasesYears Total Pierce No Pierce % Pierce Average

Cases Piercing %forPreviousThreeYears 42

1990 27 9 18 33.33%1991 39 15 24 38.46%1992 17 10 7 58.82% 40.96%1993 45 23 22 51.11% 47.52%1994 45 16 29 35.56% 45.79%1995 51 23 28 45.1% 43.97%1996 12 4 8 33.33% 39.81%1997 12 4 8 33.33% 41.33%1998 28 12 16 42.86% 38.46%1999 30 8 22 26.67% 34.29%

42. The weighted average was calculated by dividing the sum of the number of cases pierced forthe previous three years by the sum of the total number of cases for the previous three years.

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2000 26 12 14 46.15% 38.10%2001 40 16 24 40.00% 37.50%2002 62 19 43 30.65% 36.72%2003 43 13 30 30.23% 33.10%2004 36 17 19 47.22% 34.75%2005 19 5 14 26.32% 35.71%2006 5 4 1 80.00% 43.33%

2007 18 10 8 55.56% 45.24%

2008 10 2 8 20.00% 48.48%Total 565 222 343 39.29%

Similar to Table 3A, Table 3B does not show any indication of a trendtowards increased or decreased piercing over time when including onlyindividual liability cases. However, as compared to the overall dataset, thepiercing rates are slightly higher and vary more drastically, ranging from80.00% in 2006 to 20% in 2008.43

B. Court Information

1. Jurisdiction

As shown in Tables 4A and 4B, there appear to be no differences between therates of piercing by jurisdiction. State courts have a slightly higher percentage ofpiercing in the overall dataset; however, in individual liability cases, piercingappears to occur at the same rates in both federal and state courts.

Table 4A: Piercing by Jurisdiction Level

Total Cases % Total Cases Pierce No Pierce % Pierce

Federal 464 49.95% 137 327 29.53%Courts

State Courts 465 50.05% 159 306 34.19%Total 929 100.00% 296 633 31.86%

Table 4B: Piercing by Jurisdiction for Individual Liability Cases

Total Cases % Total Cases Pierce No Pierce % PierceFederal 233 41.24% 93 140 39.91%Courts

State Courts 332 58.76% 129 203 38.86%Total 565 100.00% 222 343 39.29%

43. It is important to note that while the piercing rate in 2006 was 80%, the sample size for thatyear was also very small: 5.

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2. Court Level

Table 5A shows that bankruptcy, appellate/intermediate and supreme courtshave similar rates of piercing ranging between 36% and 37%. However,district/trial courts tend to pierce at a noticeably lower rate; approximately 25%of the cases before a district/trial courts resulted in a pierce.

Table 5A: Piercing by Court LevelCategory Total % of Pierce No % Pierce

Cases Total PierceCases

District/Trial 380 40.90% 95 285 25%CourtAppellate Court/ 454 48.87% 166 288 36.56%IntermediateCourtSupreme Court 68 7.32% 25 43 36.76%(Highest Court)US Bankruptcy 27 2.91% 10 17 37.04%Court I I

Total 929 100.00% 296 633 31.86%

When excluding the parent-subsidiary cases (Table 5B), the disparitybetween the rates at the district/trial court and other courts is not aspronounced, and there does not appear to be much difference in the rate ofpiercing by court level.

Table 5B: Piercing by Court Level for Individual Liability CasesCategory Total % of Pierce No %

Cases Total Pierce PiercedCases

District/Trial 172 30.44% 64 108 37.21%Court

Appellate Court/ 326 57.70% 131 195 40.18%IntermediateCourtSupreme Court 48 8.50% 19 29 39.58%(Highest Court)

US Bankruptcy 19 3.36% 8 11 42.11%Court I I I I I

Total 565 100.00% 222 343 39.30%

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C. Party Information: Type of Plaintiff

Table 6A examines the rate of piercing by type of plaintiff. This tableshows that entities who were plaintiffs were nearly two times more likely towin a pierce than plaintiffs who were individuals. This difference isstatistically significant.44 Entities are also more likely to be plaintiffs thanindividuals.

Table 6A: Type of Plaintiff

Type of Total Cases Pierce No Pierce % PiercePlaintiff

Individual 398 87 311 21.86%Entity 531 209 322 39.36%Total 929 296 633 31.9%

When including only individual liability cases (Table 6B), the rate ofpiercing by type of plaintiff is slightly higher for both plaintiffs who areindividuals and entities. However, the difference between individuals andentities is still very large, and entities are substantially more successful inpiercing claims than individuals. 45

Table 6B: Type of Plaintiff for Individual Liability CasesType of Total Cases Pierce No Pierce % PiercePlaintiff

Individual 230 67 163 29.13%Entity 335 155 180 46.27%Total 565 222 343 39.29%

44. A Chi Square (X2) test or a Pearson's chi square test determines whether the distribution of thesamples, in this case pierce rates, are equal or weighted toward a specific variable. For example, if therate of piercing was equal across plaintiff types, the chi square test would show non-significantdifferences. The level of significance is a measure of the probability that the null hypothesis (i.e. thatthe relationship between the independent and dependant variable is non-existent) was rejected when itwas actually true. A p value of 0.050 (which is the general level used to show statistical significance inthe social sciences) means that the null hypothesis can be rejected with 95% confidence. That is, there isonly a 5% chance that we would have arrived at the coefficient that we did if the true relationship werenon-existent. Generally the null hypothesis is said to be rejected when the probability of rejecting thenull hypothesis when it is in fact correct is less than 0.050 (or 5%). To put it another way, there is lessthan a 5% chance that the results are based on random chance.Pierson's chi squared for plaintiff type and pierce (x2) = 32.092, degrees of freedom (dj) = 1, p<0.001.The relationship between these two variables is statistically significant, because chances that therelationship between them is non-existent (the null hypothesis) can be rejected with a less than .001probability that the null hypothesis was actually correct. There is less than a 1% chance that there is norelationship between these two variables.It is important to note that X2 is only a measure of association. It reveals the extent to which variablesco-vary, that is the extent to which the increase or decrease in one variable is associated with theincrease or decrease in the other.

45. Pierson's chi squared (x) 16.792, degrees of freedom (d) = 1, p<0.001. Thus, therelationship between type of plaintiff and piercing the corporate veil is significant.

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D. Type of Case: Underlying Cause ofAction

Tables 7.1A and 7.1B analyze cases by underlying cause of action.46 Inboth tables, the rate of piercing is much higher in contract cases than in tortcases. This is consistent with Professor Thompson's finding that courts piercemore often in contract cases than in tort cases.47 It is also interesting to notethat the gap between the rate of piercing in contract versus tort cases appears todecrease when looking only at cases of potential individual liability. In theoverall dataset both tort claims and contract claims have a statisticallysignificant relationship with piercing the corporate veil, however, therelationships between piercing and bankruptcy, criminal, and statutory claimsare not statistically significant.48 In the subset of data that involves onlypotential individual liability cases; only tort claims are statisticallysignificant. 49

Table 7.1A: Underlying Causes of ActionType of Claim Total ON o f Pierce No Pierce % Pierce

Cases 0 Total Cases CasesCases

Contract 598 64.4% 213 385 35.6%Tort 289 31.1% 63 226 21.8%Statutory 105 11.3% 32 73 30.5%Bankruptcy 63 6.8% 25 38 39.7%Criminal 26 2.8% 12 14 46.2%Total 929 100% 296 633 31.86%

46. While there is some overlap between causes of action, the number of cases with multiple causesof action is relatively insubstantial compared to the total number of cases. There are 152 more causes ofaction than cases for Table 7.1 A. Recognizing that some cases may have more than two causes ofaction, there is overlap in a maximum of 16% of the cases. In Table 7.IB, there are 101 more causes ofaction than cases. Therefore there is overlap between causes of action in a maximum of 18% of thecases.

47. Thompson, supra note 10, at 1058.48. Pierson's chi squared (X2) for contract claims in table 7.1A= 10.909, degrees of freedom (d)=

1, p<0.01 0 . Pierson's chi squared (?) for tort claims in table 7.1A = 19.567, degrees of freedom (dj)=1, p<0.001.

49. Pierson's chi squared (X2) for tort claims in table 7.2A = 4.134, degrees of freedom (d) = 1,p< 0 .0 5 0 .

50. Some cases have multiple causes of action, see table 7.2A, this is why the sum of the five casetypes does not equal the total number of cases in the dataset.

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Table 7.1B: Underlying Causes of Action for Individual Liability CasesType of Claim Total % of Pierce No Pierce % Pierce

Cases5' Total Cases CasesCases

Contract 382 67.6% 153 229 40.1%Tort 154 27.3% 50 104 32.5%Statutory 58 10.3% 26 32 44.8%Bankruptcy 50 8.8% 21 29 42.0%Criminal 22 3.9% 11 11 50.0%Total 565 100% 222 343 39.3%

Plaintiffs often bring more than a single cause of action in the underlyingsuit. In the overall dataset there were 137 suits which contained more than asingle cause of action, and 92 such cases in the sub-dataset. Tables 7.2A and7.2B present the rates of piercing for the various combinations of claims. 52

These tables show the ways in which different combinations of claims affectpiercing rates. While the small numbers within each subgroup make moresophisticated statistical analysis problematic, these tables do evidence thatsimply adding additional claims, that is, a 'kitchen-sink' approach to litigation,may be unwise. Compare in Table 7.2A, for instance, the pierce rates forstatutory claims (43.24%) and contract claims (34.62%) with the rate ofpiercing in contract/statutory claims (23.08%).53 There may however be atipping point at which the number of claims becomes not an indication ofweakness in the case of plaintiff, but indicative of a situation of completemisconduct by the defendant. The high rate of piercing in cases whichinvolved contract, statutory, and bankruptcy claims (55.56% in Table 7.2A)would suggest as much, however the relatively low rate in cases with contract,tort, and statutory claims (28.57% in Table 7.2A) would provide a counterpoint. 54

Moreover, the rates of piercing in Tables 7.1 might also be skewed by theeffects of other causes of action. Most notably the rate for all statutory claims

51. Some cases have multiple causes of action, see table 7.2B, this is why the sum of the five casetypes does not equal the total number of cases in the dataset.

52. The numbers and rates for the individual claims are lower than those presented in Tables 7.1,because cases are only reported in a single category in Tables 7.2. Thus a case reported as Contract/Tortin 7.2 would be coded as both a contract and a tort case in tables 7.1.

53. The same basic trend holds for Contract/Bankruptcy, Tort/Statutory, and Bankruptcy/Statutory,see Table 7.2A.

54. The small samples in the other two triple-causes-of-action categories make a conclusorystatement difficult. Moreover, the relatively small portion of the overall dataset that these types of casesconstitute may render any analysis of these ratios meaningless, since the cases included may not be trueindicators (i.e. outliers). To the extent that this dataset essentially constitutes the known universe ofpiercing cases these concerns are mitigated.

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as represented in Table 7.1 is 30.5%, however, when those claims that include astatutory claim and an additional claim(s) are removed the rate jumps to46.16%. This effect is most exaggerated in the incidents of statutory/tortclaims, where only 13.33% of cases result in a pierce. The lowered rate ofpiercing in statutory cases in Table 7.1A then may be a result of the lesssuccessful tort claims. That is, it seems plausible that the tort claimpredominates over the statutory claim. The difference in rates may also beexplainable in terms of the type of plaintiff (see Tables 7.3A-B, 7.4A-B and7.5A-B). The ratio of entity-to-individual plaintiffs for statutory claims is 2-1(25-12, see Tables 7.5A and 7.4A respectively) while the ratio of entity-to-individual plaintiffs for tort/statutory claims is 2-3 (6-9, see Tables 7.5A and7.4A respectively). The decrease in success from statutory claims tostatutory/tort claims may be caused by the increased likelihood of an individualplaintiff bringing a statutory/tort claim.55

Due to the correlation between type of claim and plaintiff type the twovariables were tested for potential multicollinearity. 56 Type of plaintiff wassignificantly correlated with tort claims (Pearson's correlation of -.278 with apvalue of 0.000), contract (Pearson's correlation of .237 with ap value of 0.000),and bankruptcy claims (Pearson's correlation of .078 with ap value of 0.018).Diagnostic statistics suggested that while the variables were highly correlatedthey were within acceptable tolerance levels for collinearity.57

55. For a discussion of why this theory, casting plaintiff type as driving the effects of claim type, isproblematic see infra pp. 27-29.

56. Multicollinearity is a data problem that arises when independent variables are highly correlated.This is a problem for regression analysis because the model attempts to discern the effects of eachindependent variable while controlling for the effects of the other variables. When two, or more,variables are highly correlated the model will not be able to accurately predict how a specific variablewill impact the outcome variable because the model won't be able to disentangle the effects of thecollinear variables. While multicollincarity does not affect the magnitude and direction of therelationship it does inflate the standard errors making it more difficult to achieve statistical significance.PAUL D. ALLISON, LOGISTICAL REGRESSION USING SAS@: THEORY AND APPLICATION § 3.5 (1999),available at http://proquest.safaribooksonline.com/9781580253529.

57. Tolerance for contract is 0.437 with a variance inflation factor (VIF) of 2.399, tolerance for tortis 0.449 with VIF of 2.225, and tolerance for bankruptcy is 0.852 with VIF of 1.173. Multicollinearityin logistic models can be diagnosed by running the model as a linear regression, even though the binaryoutcome variable violates an assumption of linear regression. This is not a problem, because the twokey diagnostic measurements (tolerance and variance inflation factor) are constructed by regressing theindependent variables on each other, and the outcome variable is not included. The first measure foreach variable, tolerance, is constructed by taking the R2 of the regression in which that variable was theoutcome variable and subtracting one. A low tolerance is associated with the presence ofmulticollincarity, because this would mean that the other variables were able to predict the outcome ofthe tested variable. The implication of this finding is that the test variable does not need to be in themodel because the remaining independent variables account for its affects. Like all statistics, there isnot a hard and fast number at which it can be said that the tolerance is so low that multicollinearity ispresent. A tolerance below .40, however, should raise concerns. Id. The second statistic, VIF, is ameasure of "how inflated the variance of the coefficient is, compared to what it would be if the variablewere uncorrelated with any other variable in the model." Id. (internal quotations omitted).

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Table 7.2A: Underlying Cause of Action with Multiple ClaimsType of Claim Total % of Pierce No % Pierce

Cases Total Cases PierceCases Cases

Contract 491 52.9% 170 321 34.62%Tort 210 22.6% 48 162 22.86%Statutory 37 4.0% 16 21 43.24%Bankruptcy 25 2.7% 9 16 36.00%Criminal 26 2.8% 12 14 46.16%Contract/Tort 51 5.5% 16 35 31.37%Contract/Statutory 26 2.8% 6 20 23.08%Contract/Bankruptcy 13 1.4% 4 9 30.77%Tort/Statutory 15 1.6% 2 13 13.33%Tort/Bankruptcy 4 0.4% 1 3 25.00%Statutory/Bankruptcy 10 1.1% 3 7 30.00%Contract/Tort/Statutory 7 0.8% 2 5 28.57%Contract/Tort/Bankruptcy 1 0.1% 1 0 100.00%Contract/Statutory/Bankruptcy 9 1.0% 5 4 55.56%Tort/Statutory/Bankruptcy 1 0.1% 0 1 0.00%Other 3 0.3% 1 2 33.33%Total 929 100.0% 296 633 31.86%

Table 7.2B: Underlying Cause of Action with Multiple Claims forIndividual Liability Cases

Type of Claim Total % of Pierce No %Cases Total Cases Pierce Pierce

Cases CasesContract 302 53.5% 122 180 40.40%Tort 103 18.2% 33 70 32.04%Statutory 20 3.5% 13 7 65.00%Bankruptcy 23 4.1% 9 14 39.13%Criminal 22 3.9% 11 11 50.0%Contract/Tort 38 6.7% 15 23 39.47%Contract/Statutory 15 2.7% 5 10 33.33%Contract/Bankruptcy 11 1.9% 4 7 36.36%Tort/Statutory 7 1.2% 0 7 0.00%Tort/Bankruptcy 3 0.5% 1 2 33.33%Statutory/Bankruptcy 6 1.1% 2 4 33.33%Contract/Tort/Statutory 5 0.9% 2 3 40.00%Contract/Statutory/Bankruptcy 7 1.2% 4 3 57.14%Other 3 0.5% 1 2 33.33%

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Total 565 100.0% 222 343 39.29%Table 7.3A: Type of Plaintiff and Underlying Cause of Action

Type of Claim Total # Ind. P % Ind. # Entity P % EntityCases58 Cases P Cases P

Contract 598 204 34.1% 394 65.9%Tort 289 182 63.2% 106 36.8%Statute 105 48 45.7% 57 54.3%Bankruptcy 63 18 28.6% 45 71.4%Criminal 26 10 38.5% 16 61.5%Total 929 398 42.8% 531 57.2%

Table 7.3B: Type of Plaintiff and Underlying Cause of ActionType of Claim Total # Md. P % Id. Entity P % Entity

Cases59 Cases P Cases PContract 382 144 37.7% 238 62.3%Tort 154 89 57.8% 65 42.2%Statute 58 16 27.6% 42 72.4%Bankruptcy 50 15 30% 35 70%Criminal 22 8 36.4% 14 63.6%Total 565 230 40.7% 335 59.3%

Tables 7.3A and 7.3B show the interaction between the type of plaintiff andthe underlying cause of action in piercing cases. In both Tables 7.3A and 7.3B,individuals brought more tort claims (63.2% and 57.8%) while entities broughtmore contract claims (65.9% and 62.3%). Type of plaintiff and underlyingcause of action are significantly associated in both the overall dataset and thesubset database for both tort and contract claims.60

While the piercing percentages for the types of plaintiff for each individualcause of action do not vary significantly between tables 7.3A and 7.3B, whentable 7.3B is compared with data solely related to parent-subsidiary piercing,

58. Some cases have multiple causes of action: 59 cases have both contract and tort claims, 23cases have both contract and bankruptcy claims, 42 cases have both contract and statutory claims, 6cases have both tort and bankruptcy claims, 23 cases have both tort and statutory claims, and 20 caseshave both bankruptcy and statutory claims.

59. Some cases have multiple causes of action: 44 cases have both tort and contract claims, 16cases have both bankruptcy and contract claims, 28 cases have both contract and statutory claims, 3cases have both tort and bankruptcy claims, 12 cases have both tort and statutory claims, and 12 caseshave both statutory and bankruptcy claims.

60. Pierson's chi squared (X) for tort claims in table 7.3A = 71.854, degrees of freedom (dj) = 1,p<0.001. Pierson's chi squared (X2) for contract claims in table 7.3A = 52.213, degrees of freedom (dj)= 1, p<0.001. Pierson's chi squared (X2)for tort claims in table 7.3B = 25.600, degrees of freedom (d)=1, p<0.001. Pierson's chi squared (X) for contract claims in table 7.3B = 4.432, degrees of freedom (dj)= 1, p<0.050.

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there are larger differences. For instance, in the piercing dataset excludingparent-subsidiary piercing, the individuals brought 57.8% of tort cases while inparent-subsidiary cases they brought 70% of tort cases.6

Tables 7.4 and 7.5 compare the relationship between cause of action,piercing rate, and plaintiff type, by creating separate tables for individualplaintiffs (7.4 A and 7.4B) and entity plaintiffs (7.5A and 7.5B). In both theoverall dataset and the sub-dataset excluding parent-subsidiary cases entityplaintiffs are more successful than individual plaintiffs (the only exceptionbeing contract/statutory claims in the dataset which excludes parent-subsidiarycases).

Table 7.4A: Underlying Cause of Action with Multiple Claims Brought byIndividual Plaintiffs

Type of Claim Total % of Pierce No % PierceCases Total Cases Pierce

Cases Cases

Contract 174 43.7% 46 128 26.44%Tort 133 33.4% 24 109 18.05%Statutory 12 3.0% 4 8 33.33%Bankruptcy 9 2.3% 2 7 22.22%Criminal 10 2.5% 2 8 20.00%

Contract/Tort 30 7.5% 6 24 20.00%Contract/Statutory 11 2.7% 2 9 18.18%Contract/Bankruptcy 5 1.3% 0 5 0.00%Tort/Statutory 9 2.3% 1 8 11.11%Tort/Bankruptcy 1 0.2% 0 1 0.00%Statutory/Bankruptcy 3 0.8% 0 3 0.00%Contract/Tort/Statutory 1 0.2% 0 1 0.00%Total 398 100.0% 87 311 21.86%

Table 7.5A: Underlying Cause of Action with Multiple ClaimsBrought by Entities


Cases CasesContract 317 59.7% 124 193 39.12%Tort 77 14.5% 24 53 31.17%Statutory 25 4.7% 12 13 48.00%Bankruptcy 16 3.0% 7 9 43.75%Criminal 16 3.0% 10 6 62.50%

61. See Matheson, supra note 11, at 1122.

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Contract/Tort 21 4.0% 10 11 47.62%Contract/Statutory 15 2.8% 4 11 26.67%Contract/Bankruptcy 8 1.5% 4 4 50.00%Tort/Statutory 6 1.1% 1 5 16.67%Tort/Bankruptcy 3 0.6% 1 2 33.33%Statutory/Bankruptcy 7 1.3% 3 4 42.86%Contract/Tort/Statutory 6 1.1% 2 4 33.33%Contract/Tort/Bankruptcy 1 0.2% 1 0 100.00%Contract/Statutory/Bankruptcy 9 1.7% 5 4 55.56%Tort/Statutory/Bankruptcy 1 0.2% 0 1 0.00%Other 3 0.6% 1 2 33.33%Total 531 100.0% 209 322 39.36%

Table 7.4B: Underlying Cause of Action with Multiple Claims forIndividual Liability Cases Brought by Individual Plaintiffs


SCases CasesContract 108 47.0% 37 71 34.26%Tort 61 26.5% 15 46 24.59%Statutory 5 2.2% 3 2 60.00%Bankruptcy 8 3.5% 2 6 25.00%Criminal 8 3.5% 2 6 25.00%Contract/Tort 24 10.4% 6 18 25.00%Contract/Statutory 5 2.2% 2 3 40.00%Contract/Bankruptcy 4 1.7% 0 4 0.00%Tort/Statutory 3 1.3% 0 3 0.00%Tort/Bankruptcy 1 0.4% 0 1 0.00%Statutory/Bankruptcy 2 0.9% 0 2 0.00%Contract/Tort/Statutory 1 0.4% 0 1 0.00%Total 230 100.0% 67 163 29.13%

Table 7.5B: Underlying Cause of Action with Multiple Claims forIndividual Liability Cases Brought by Entities

Type of Claim Total % of Pierce No %Cases Total Cases Pierce Pierce

Cases CasesContract 194 57.9% 85 109 43.81%Tort 42 12.5% 18 24 42.86%Statutory 15 4.5% 10 5 66.67%Bankruptcy 15 4.5% 7 8 46.67%

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Criminal 14 4.2% 9 5 64.29%Contract/Tort 14 4.2% 9 5 64.29%Contract/Statutory 10 3.0% 3 7 30.00%Contract/Bankruptcy 7 2.1% 4 3 57.14%Tort/Statutory 4 1.2% 0 4 0.00%Tort/Bankruptcy 2 0.6% 1 1 50.00%Statutory/Bankruptcy 4 1.2% 2 2 50.00%Contract/Tort/Statutory 4 1.2% 2 2 50.00%Contract/Statutory/Bankruptcy 7 1.2% 4 3 57.14%Other 3 0.9% 1 2 33.33%Total 335 100.0% 155 180 46.27%

The relationship between plaintiff and claim can be conceptualized inseveral ways. It is possible that both type of plaintiff and type of claim have anindependent affect on the rates of piercing the corporate veil (Figure 1).

Figure 1: Independent Effects of Type of Plaintiff and Type of Claim

Type ofPlaintiff

Type of Claim

In this scenario the difference in rates of piercing between tort and contractcases would be persistent whether a plaintiff was an entity or individual. Thusthe rate of piercing in Tables 7.1A-B and 7.2A-B should be unchanged when

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plaintiff type is introduced. The rates of piercing reported in Tables 7.4A-Band 7.5A-B however show that there is some interaction between the type ofclaim brought and the type of plaintiff bringing the claim.

Conversely the relationship might be theorized as intervening relationship,whereby the effects of claim type are driven by plaintiff type (Figure 2).

Figure 2: Type of Claim as an Intervening Variable between Plaintiff Typeand Piercing the Veil

Type ofPlaintiff

Piercing theVeil

Type of Claim

Under this model, the gap between tort and contract claims is a reflection ofthe different plaintiff types not the different claim types, so when plaintiff typeis held constant the gap should be eliminated. This is true for Table 7.5Bwhere the gap in piercing rates is eliminated, however the gap in piercing ratesbetween tort and contract claims persists in tables 7.4A, 7.5A, and 7.5B, whichseparate claims made by individuals from those made by entities.

It is more likely that the relationship is captured by Figure 3. Type ofplaintiff and type of claim affect one another and yet each has an independenteffect on the likelihood of piercing. This conceptualization captures the factthat on the whole different types of plaintiffs bring different types of claims,and those differences are patterned in consistent ways. Individuals tend tobring more tort claims, and entities tend to bring more contract claims. InFigure 3 (unlike Figure 1) this statistical finding is captured by the two sidedarrow between plaintiff type and claim type, indicating that these patterns arenot random, but that the situation in which an individual needs to pierce thecorporate veil likely arose in a much different manner than the situation inwhich an entity would attempt to pierce the corporate veil. Additionally Figure3 (unlike Figure 2), conceptualizes the impact of claim and plaintiff as partiallydistinct from one another. That is, in Figure 2 the only reason plaintiff typeeffects piercing is because it determines the type of claim that will be brought,

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which in turn effects piercing.

Figure 3: Independent Effects of Type of Plaintiff and Type of Claimand Relationship between Claim and Plaintiff type

Type of

Plaintiff

Piercing theI VeilType of Claim

E. Piercing Factors

Tables 8A and 8B present the rates at which courts discuss specific factors,as well as ratio between whether a court finds a factor present or not present,and the effect that decision has on piercing the corporate veil. The rate atwhich courts discuss factors ranges from approximately 50% (fraud and ownercontrol/dominance) to barely 3% (assumption of the risk).

Table 8A: Presence of Specific Factors in Decisions to Pierce

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Fraud/ 49.19% Not 330 35.5% 304 26 92.1% 7.9%Misrep- Present 127 13.7% 15 112 11.8% 88.2%resenta- Presenttion

Owner 48.55% Not 235 25.3% 224 11 95.3% 4.7%Control/ Present 216 23.3% 51 165 23.6% 76.4%Domin- Presentance

Comm- 38.11% Not 172 18.5% 161 11 93.6% 6.4%ingling Present 182 19.6% 35 147 19.2% 80.8%of PresentFunds

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Under- 32.62% Not 147 15.8% 136 11 92.5% 7.5%capital- Present 156 16.8% 36 120 23.1% 76.9%ization Present(Sum)

Under- 24.76% Not 126 13.6% 115 11 91.3% 8.7%capital- Present 104 11.2% 22 82 21.2% 78.8%ization Present(drain-ing offunds)Under- 23.36% Not 120 12.9% 111 9 92.5% 7.5%capital- Present 97 10.4% 22 75 22.7% 77.3%ization Present(notenoughto startbusin-ess)

Non- 30.25% Not 158 17.0% 149 9 87.4% 12.6%funct- Present 107 11.5% 7 100 20.8% 79.2%ioning Present

Overlap 28.74% Not 97 10.4% 86 11 88.7% 11.3%Present 170 18.3% 74 96 43.5% 56.5%Present

Unfair- 28.53% Not 158 17.0% 149 9 94.3% 5.7%ness/ Present 107 11.5% 7 100 6.5% 93.5%Injust- PresenticeNonex- 22.07% Not 132 14.2% 114 18 86.4% 13.6%istent Present 73 7.9% 15 58 20.5% 79.5%

PresentAssum- 3.88% Not 27 2.9% 21 6 77.8% 22.2%ption of Present 9 1.0% 5 4 55.6% 44.4%Risk Present

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Table 8B: Presence of Specific Factors in Decisions to Pierce for IndividualLiability Cases

Fraud/ 50.09% Not 183 32.4% 169 14 92.3% 7.7%Misre- Present 100 17.7% 12 88 12.0% 88.0%presen Present-tation

Owner 43.19% Not 89 15.8% 81 8 91.0% 9.0%Contr- Present 155 27.4% 40 115 25.8% 74.2%ol/ PresentDomi-nance

Com- 39.12% Not 95 16.8% 87 8 91.6% 8.4%ming- Present 126 22.3% 21 105 16.7% 83.3%ling of PresentFunds

Unde- 32.74% Not 73 12.9% 65 8 89.0% 11.0%reapit- Present 112 19.8% 23 89 20.5% 79.5%aliza- Presenttion(Sum)Unde- 23.89% Not 60 10.6% 52 8 86.7% 13.3%reapit- Present 75 13.3% 12 63 16.0% 84.0%aliza- Presenttion(drain-ing offunds)

Unde- 21.42% Not 53 9.4% 47 6 88.7% 11.3%rcapit- Present 68 12.0% 16 52 23.5% 76.5%aliza- Presenttion(notenou-gh tostartbusin-ess)

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Non- 31.15% Not 93 16.5% 80 13 86.0% 14.0%funct- Present 83 14.7% 17 66 20.5% 79.5%ioning PresentOver- 19.29% Not 38 6.7% 31 7 81.6% 18.4%lap Present 71 12.6% 22 49 31.0% 69.0%

PresentUnfai- 26.55% Not 70 12.4% 65 5 92.9% 7.1%mess/ Present 80 14.2% 5 75 6.3% 93.8%Injust- PresenticeNone- 20.71% Not 61 10.8% 51 10 83.6% 16.4%xistent Present 56 9.9% 10 46 17.9% 82.1%

Present

Assu- 3.89% Not 15 2.7% 12 3 80.0% 20.0%mption Present 7 1.2% 5 2 71.4% 28.6%of PresentRisk

1. Fraud/Misrepresentation

Fraud/misrepresentation was the most frequently addressed factor in thepiercing cases studied. In the overall dataset, it was discussed (whether foundpresent or not present) in 49.2% of cases and in the individual liability sub-dataset, it was discussed in 50.1% of cases. In the overall dataset, where fraudwas present, courts pierced 88.2% of the time and refused to pierce 11.8% ofthe time. Where fraud was not present, courts pierced only 7.9% of the timeand refused to pierce 92.1% of the time. The dataset for individual liabilitycases shown in Table 8B presents similar piercing rates. When a court foundthat fraud was present, it pierced 88.0% of the time and refused to pierce 12.0%of the time. When a court found that fraud was not present, it refused to pierce92.3% of the time and pierced 7.7% of the time.

2. Owner Control/Dominance

Owner control/dominance was the second-most commonly discussedvariable. It was discussed in 48.6% of cases in the overall dataset and in 43.2%of cases in the dataset for individual liability. Where courts found ownercontrol/dominance in the overall dataset (23.3% of cases), they pierced 76.4%of the time and refused to pierce in the remaining 23.6% of cases. Wherecourts found owner control/dominance not present (25.3% of cases), theypierced in only 4.7% of cases and refused to pierce in 95.3% of cases.

In the dataset shown in Table 8B, courts found owner control/dominance

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present in 27.4% of all cases in the sub-dataset and not present in 15.8% ofcases. Where courts found it to be present, they pierced 74.2% of the time,refusing to pierce the remaining 25.8% of the time. Where courts found ownercontrol/dominance not present, they refused to pierce in 91.0% of cases andpierced in only the remaining 9.0% of cases.

3. Commingling of Funds

In the overall dataset, courts discussed commingling of funds in over a thirdof the cases (38.1%). Courts discussed commingling in a similar proportion inthe individual liability dataset (39.1%). In the overall dataset in Table 8A,commingling was found to be present in 19.6% of the cases, and when it waspresent, the court pierced 80.8% of the time and refused to pierce only 19.2%of the time. When commingling was found not to be present in 18.5% of thecases, courts refused to pierce in 93.6% of the cases and pierced in 6.4% of thecases.

In the dataset shown in Table 8B, courts found commingling in 22.3% ofcases. In 83.3% of those cases courts pierced whereas in 16.7% of those casesthey declined to hold owners liable. In the 16.8% of cases where a court foundthat there was no commingling, it refused to pierce 91.6% of the time andpierced only 8.4% of the time.

4. Undercapitalization

Undercapitalization is discussed in three variables below: (1)Undercapitalization (not enough to start a business); (2) Undercapitalization(draining of funds); and (3) Undercapitalization (Sum). 62 Undercapitalization(Sum) was created during the analysis process. Because a court does notalways distinguish which type of undercapitalization is being discussed,Undercapitalization (Sum) eliminates difficulties posed by this lack of clarityby combining the two variables. 63

In the overall dataset shown in Table 8A, insufficient funds to start abusiness are discussed in 24.8% of the cases. Where it is discussed, the courtfound that it was present in 13.6% of cases, but not present in 11.2% of cases.Of the cases where the court found insufficient funds to start a business, itpierced in 77.3% of cases and refused to pierce in 22.7% of cases. Where thecourt found that there were not insufficient funds to start the business, it refusedto pierce in 92.5% of those cases and pierced in 7.5% of those cases.

In the dataset where liability was sought to be imposed on individualowners, shown in Table 8B, the court discussed insufficiency of funds in 21.4%

62. Table 1.63. See supra note 38.

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of cases. Where the court found that the funds were insufficient (12% ofcases), it pierced 76.5% of the time and refused to pierce 23.5% of the time.Where the court found that the funds were not insufficient (9.4% of cases), itpierced only 11.3% of the time and refused to pierce in 88.7% of cases.

In the overall dataset, the court discussed draining of funds in 24.8% ofcases, of which it found that there was draining of funds in 11.2% of cases andwas not draining of funds in 13.6% of cases. Where there was no draining offunds, the courts refused to pierce 91.3% of the time and chose to pierce only8.7% of the time. Where there was draining of funds, the courts pierced 78.8%of the time and declined to pierce 21.2% of the time.

In the individual liability sub-dataset, the court discussed draining of fundsin 23.9% of cases. Of those cases, courts found that draining was present in13.3% and was not present in 10.6% of cases. Where courts found draining offunds, they pierced 84.0% of the time and refused to pierce 16.0% of the time.Where the courts discussed draining but found that it was not present, theypierced only 11.3% of the time and declined to pierce 88.7% of the time.

When the two undercapitalization variables are combined, in the overalldataset, the court looked at undercapitalization in nearly a third (32.6%) ofcases. Of those cases, there was a finding of undercapitalization in 16.8% andno undercapitalization in 15.8% of the cases. Where there was only a findingof no undercapitalization, the courts pierced only 7.5% of the time and refusedto pierce 92.5% of the time. Where the courts found at least one type ofundercapitalization, they pierced 76.9% of the time and declined to pierce23.1% of the time.

Courts discussed undercapitalization at nearly the same rate when theparent subsidiary cases were removed; they discussed it in 32.7% of cases. Ofthose cases, the court found undercapitalization slightly more frequently than inthe overall dataset: in 19.8% of cases. The courts declined to find it in 12.9%of cases. When the courts found undercapitalization, they held owners liable79.5% of the time and refused to pierce 20.5% of the time. Where the courtsfound no undercapitalization, they declined to pierce 89.0% of the time andpierced 11.0% of the time.

5. Non-Functioning

In the overall dataset as shown in Table 8A, the courts discussed non-functioning leadership in 30.3% of cases. Where courts found that leadershipwas non-functioning (11.5% of cases), they pierced in 79.2% of cases andrefused to pierce in 20.8% of cases. Where courts found that leadership wasnot non-functioning (17.0%), they declined to hold owners liable in 87.4% ofcases and pierced in 12.6% of cases.

In Table 8B, where parent-subsidiary cases were excluded, courts discussed

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non-functioning leadership in 31.2% of cases. Where courts found leadershipwas non-functioning (14.7% of cases), they pierced in 79.5% of those cases andrefused to pierce in 20.5% of those cases. Where courts found that leadershipwas not non-functioning (16.5%), they pierced in only 14.0% of cases andrefused to pierce in the remaining 86.0% of cases.

6. Overlap

Overlap was discussed in slightly over one-fourth of the cases (28.7%) inthe overall dataset and in only 19.3% of the cases in the dataset excludingparent-subsidiary cases. In the overall dataset, courts found overlap in 170(18.3%) cases and found that there was no overlap in 97 (10%) cases. When acourt found overlap, it pierced 56.5% of the time and declined to pierce 43.5%

of the time. Where a court found no overlap, it refused to pierce 88.7% of thetime, and pierced only 11.3% of the time.

In the sub-dataset shown in Table 8B, courts found overlap in 71 cases(12.6%) and found no overlap in 38 cases (6.7%). When a court found overlap,it pierced 69% of the time and refused to pierce 31% of the time. When a courtfound no overlap, it refused to pierce 81.6% of the time and pierced 18.4% ofthe time.

7. Unfairness/Injustice

Unfaimess/injustice was discussed in 28.5% of cases in the overall datasetas shown in Table 8A and in 26.6% of cases in the dataset shown in Table 8B.Courts in the overall dataset found unfairness/injustice in 11.5% of casesoverall and found no unfairness/injustice in 17.0% of cases. When courtsfound unfaimess/injustice, they pierced 93.5% of the time and refused to piercea mere 6.5% of the time. When courts found no unfaimess/injustice, theyrefused to pierce 94.3% of the time and only pierced 5.7% of the time.

In the dataset involving individual liability, courts foundunfaimess/injustice present 14.2% of the time and found it not present 12.4% ofthe time. When the court found that unfairness/injustice was not present, theyrefused to pierce 92.9% of the time and pierced only 7.1% of the time. Wherecourts found injustice/unfairness to be present, they pierced 93.8% of the timeand refused to pierce only 6.2% of the time.

These consistent results in both datasets suggest that there may be a strongrelationship between a positive or negative finding of unfaimess/injustice and adecision to pierce. Such a relationship will be further explored in the context ofregression analysis.

8. Non-Existent

In the overall dataset as shown in Table 8A, the courts discussed

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nonexistent leadership in 22.1% of cases. Where courts found that leadershipwas non-existent (7.9% of cases), they pierced in 79.5% of cases and refused topierce in 20.5% of cases. Where courts found that leadership was notnonexistent (14.2% of cases), they pierced in 86.4% of cases and refused topierce in 13.6% of cases.

In Table 8B, where parent-subsidiary cases were excluded, courts discussednon-existent leadership in 20.7% of cases. Where courts found that the non-existent variable to be present (9.9% of cases), they pierced in 82.1% of thosecases and refused to pierce in 17.9% of those cases. Where courts found thatthe non-existent variable was not present (10.8%), they refused to pierce in83.6% of cases and pierced in 16.4% of cases.

9. Assumption ofRisk

Assumption of risk was the least frequently discussed factor. Courtsdiscussed assumption of risk in only 3.9% of cases in both the overall datasetand the dataset excluding parent-subsidiary cases. In the overall dataset, asshown in Table 8A, courts found assumption of risk to be present in 1.0% ofcases and not present in only 2.9% of cases. Where a court found it to bepresent, it refused to pierce 55.6% of the time and pierced 44.4% of the time.Where assumption of risk was not present, a court pierced only 22.2% of thetime and refused to pierce 77.8% of the time.

In the dataset involving personal liability claims only, court foundassumption of risk to be present in 1.2 % of cases and found it not present in2.7% of cases. When it was present, courts refused to pierce 71.4% of the timeand pierced 28.6% percent of the time. Where it was not present, courtspierced in 20.0% of those cases and refused to pierce in 80.0% of cases.

IV. LOGISTIC REGRESSION ANALYSIS

The foregoing descriptive statistics and analysis suggest that there aremeaningful associations between the dependent variable (piercing the corporateveil) and certain independent variables (e.g., fraud, domination, and overlap).This kind of data analysis, known as bivariate analysis, explores the associationbetween two variables, and without providing evidence as to the relativestrength of those associations. Bivariate analysis also fails to control for otherindependent variables, thereby allowing statistical confounding. 64

64. See DAVID COPE, FUNDAMENTALS OF STATISTICAL ANALYSIS 67 (2005) ("When a correlationcoefficient misleads as to the strength of a causal connection between two correlated variables because itreflects not only the relationship between those variables but also the influence of one or more othervariables whose individual effects can't easily be isolated and assessed, there is said to beconfounding."). While all statistical models are susceptible to confounding variables, certain models,such as the logistic regression analysis employed herein, are able to control for the independent variablesincluded in the model.

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Regression analysis is the generally recognized statistical technique foranalysis of numerical data consisting of values of a dependent variable(response variable) and of one or more independent variables (explanatoryvariables). Multiple regression analysis is the technique used when the studyhas more than two variables, as does the study of piercing cases.66

Logistic regression analysis67 allows for the assessment of the impact ofspecific factors and case demographics on the outcome variable (piercing). Theregression analysis provides data to determine which factors have the strongesteffect on a court's decision to pierce the corporate veil. A peculiar andimportant benefit of multiple regression analysis is that it

65. Thirty years ago, Michael Finklestein wrote:The term "regression," as it is used here, refers to a certain technique for estimating amathematical relationship between factors on the basis of numerical data. The use ofregression has become firmly established as the standard method of analysis in econometric(mathematical economic) models used by both public and private decision makers informulating and examining policies. The same techniques are now beginning to be used indealing with important issues in sharply contested regulatory proceedings; in these contextsthe precision, reliability, and usefulness of regression methodology have become a specialprovince and concern of lawyers.

MICHAEL 0. FINKELSTEIN, QUANTITATIVE METHODS IN LAW 211 (1978). See generally Lee Epstein &Gary King, Building an Infrastructure for Empirical Research in the Law, 53 J. LEGAL EDUC. 311(2003) (discussing the use of empirical research methodologies in legal scholarship); Howell E. Jackson,Analytical Methods for Lawyers, 53 J. LEGAL EDUC. 321 (2003) (explaining the importance ofregression and other analytical methods in the legal field).

66. See G. David Garson, Multiple Regression, in Statnotes: Topics in Multivariate Analysis,http://www2.chass.ncsu.edu/garson/PA765/regress.htm ("Multiple regression, a time-honored techniquegoing back to Pearson's 1908 use of it, is employed to account for (predict) the variance in an intervaldependent, based on linear combinations of interval, dichotomous, or dummy independent variables.Multiple regression can establish that a set of independent variables explains a proportion of thevariance in a dependent variable at a significant level (through a significance test of R2), and canestablish the relative predictive importance of the independent variables (by comparing beta weights).").

67. As an initial matter, classical linear regression coefficients would seem, on the surface, to be anadequate means of analyzing a dichotomous variable with values of 0 and 1. The regression coefficientswill show an increase or decrease in the predicted probability of piercing the corporate veil due to a one-unit change of the independent variables. When working with dichotomous categorical values, however,linear regression models produce a line that can extend upward toward positive infinity and downwardtoward negative infinity. This produces nonsensical results because the dependent variable (pierce or nopierce) is bounded at 0 and 1. FRED C. PAMPEL, LOGISTIC REGRESSION: A PRIMER 3-10 (2000).

The favored analytical tool for analysis involving dichotomous dependent variables such as the currentstudy is called logistic regression analysis. See G. David Garson, Logistic Regression, in Statnotes:Topics in Multivariate Analysis, available at http://faculty.chass.ncsu.edu/garson/PA765/logistics.htm("Binomial (or binary) logistic regression is a form of regression which is used when the dependent is adichotomy and the independents are of any type. . . . Logistic regression can be used to predict adependent variable on the basis of continuous and/or categorical independents and to determine thepercent of variance in the dependent variable explained by the independents; to rank the relativeimportance of independents; to assess interaction effects; and to understand the impact of covariatecontrol variables. The impact of predictor variables is usually explained in terms of odds ratios.").This form of logistic regression is the means of analysis used by Swain & Aguilar. See Swain, supranote 16, at 446. For other studies using this technique, see generally Kevin M. Clermont & TheodoreEisenberg, Xenophilia in American Courts, 109 HARV. L. REV. 1120 (1996) (using logistic regression tostudy the effect of foreignness on favorable verdicts); Deborah Jones Merritt & Barbara F. Reskin, Sex,Race, And Credentials: The Truth about Affirmative Action in Law Faculty Hiring, 97 COLUM. L. REV.199 (1997) (using logistic regression to study the effects of sex and race on tenure-track hiring at lawschools).

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allows us to take account of all additional relevant factors for which we have data.We develop a multiple regression equation that includes all relevant explanatoryvariables (or at least as many as practicable) and then focus on the coefficient of thevariable of interest . .. This coefficient . .. reflects the magnitude of the relationshipbetween [piercing results and the specific factor identified] when the influence ofthe other predictor variables is accounted for. 68

In the current study a logistic regression model was constructed factoring inall of the coded basic informational variables and also for the piercing factorsto determine the effects of each on a court's likelihood to pierce the corporateveil. By taking into account the court, party, and claim information, thecoefficients for the piercing factors are more robust because the coefficientscontrol for those variables. 69

Four models were constructed for both the overall dataset and sub-dataset.70 The models differ from one another only in the combination ofreference variables.7 1 Reference categories were used for court type(trial/district court), plaintiff type (entity), claim type (either contract or tort)and piercing factors (either not present or not discussed). Reference categoriesare the group against which the group associated with the statistic in the table iscompared. For example, taking the first line of information in Table 9A, theeffect of a case being heard before an appellate court is reported in terms of itsaffect relative to the effect a case being heard before a trial/district court has onpiercing. Thus, the odds ratio of 2.290 means that a given case is 2.290 timesmore likely to result in a pierce if it is heard in an appellate court rather than atrial/district court. That is, what is the increase or decrease in odds when thecase is before an appellate court and not a trial/district. 72 Because 'present',

68. COPE, supra note 64, at 84-85.69. In a regression analysis the coefficients produced for each variable represent the relationship

between that variable and the outcome (dependent) variable net of the other variables. That is, thecoefficient reports the relationship when all other variables are static, i.e. how does fraud affect piercingwhen court, party and claim type are all the same.

70. These Models, 1A4A and 1B-4B, can be found in the Appendix.71. When using categorical data (data which cannot be numerically measured in a meaningful way)

in regression it is necessary to construct 'dummy variables' which measure only the presence or absenceof a category. Failing to do so would yield illogical results. For example, the model constructed has avariable labeled 'federal' which records whether the case was heard in state or federal court. Theregression coefficient represents the effect of a one unit increase in 'federal' on the outcome variable'pierce'. Since categorical data is by definition non-numeric this analysis makes little sense. However,because the one unit increase in the variable (from 0 to 1) marks the switch from state to federal it ispossible to compare the effect of being in federal court vis-A-vis the effect in state court. When acategorical variable is marked by more than two categories each response must be turned into a separatedummy variable. Thus, the variable 'claim type' which could be coded contract, tort, statutory,bankruptcy or criminal results in five separate variables, each of which measures the number of cases inwhich that particular claim was present. In order for these variables to be interpreted one of the newlycreated variables must be omitted to serve as a reference group (technically the same is true of a tworesponse variable; in the federal/state variable the dummy variable for state was omitted). Thus thecoefficients for each claim type represent the effect of the particular claim type vis-a-vis the omittedcategory.

72. This is a key interpretive limitation for logistic regression. The data can only speak to what it iscompared to, and nothing beyond that. Thus, the reported increase in odds is not an absolute increase,

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'not present' and 'not discussed' are zero-sum (each factor fits one and onlyone of these categories) the absolute value of 'not present vs. not discussed'will be the same as 'not discussed vs. not present'. For claim type, however,the claims are not zero-sum, as many cases involved multiple claims. 73 Assuch, the relationship between 'tort vs. contract' is different than therelationship between 'contract vs. tort'. For this reason the data is present intwo sets of tables. In Tables 9A and 9B contract serves as the reference group,while in Tables 1 OA and 1 OB tort is the reference group.

In the first model (Models 1A and IB) contract was used as a referencegroup for claim type and 'not discussed' served as a reference group for thepiercing factors. In the second model (Models 2A and 2B) tort and 'notdiscussed' serve as the reference groups. In the third model (Models 3A and3B) contract and 'not present' are the reference categories, and in the fourthmodel (Models 4A and 4B) tort and 'not present' are the reference groups. Inall models trial courts are used as the reference group for court type.74

All models consist of four accretive "blocks."7 5 The blocks correspond tothe types of variables constructed out of the data collected. Thus, the firstblock contains variables about the presiding courts. The second block addsinformation about the plaintiff, and the third block adds information relating tothe type of claim brought.76 The final block contains the factors employed bycourts to determine whether or not to pierce the corporate veil.

For each model the regression coefficients (P) presented are the effects onthe log odds of the outcome variable (pierce) given the presence of thepredictor variable. 77 The log odds coefficients are however valuable in that

but a relative one.73. See Table 7.2A and 7.2B.74. This is done for both pragmatic and theoretical reasons. Pragmatically, the use of appellate as a

reference group offered no statistically noteworthy findings. Theoretically the strongest comparison isbetween trial courts and appellate courts, as well as, the difference between a court of generaljurisdiction (trial courts) and a court of specialized jurisdiction (bankruptcy courts).

75. Each block was added sequentially, with all variables in each block being added at the sametime. The independent effects of each set of variables can be ascertained by comparing the full modelevaluations after the addition of each block. The full models can be found in the Appendix.

76. The order in which the blocks were arranged is largely cosmetic; the order could easily havebeen reversed. Given the relationship between plaintiff type and claim brought the models were all runwith blocks 2 and 3 inverted to see what, if any, effect the addition of party type after claim type wouldhave. None of the models run with blocks two and three reversed yield any statistically noteworthyresults. If for instance reversing the blocks cause claim types to be (more) significant before theaddition of party type, and subsequently lose significance upon the addition of the entity variable, thenthe relationship between claim and pierce may have been operating through party type (see abovediscussion).

77. The logistic regression model is a non-linear model that uses the log function to create an S-curve, and as such the coefficients created by the model are in terms of that non-linear equation. Inorder to make these values have interpretive meaning they must be transformed, such that they representthe relative increase or decrease in the odds of the outcome variable occurring (which are reported inTables 9A/B and 10BA/). See ALLISON, supra note 56, at § 2.9.

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they are used to calculate the Wald statistic78 which measures the statisticalsignificance of the individual relationship between a particular independentvariable and the dependent variable. 79 In order to test the overall effectivenessof the model four additional statistics are provided: first, the model chi -square

(X2) which measures whether the model significantly improves prediction overan intercept (or constant) only modelo ; second, the Cox and Snell R 2; third, theNagelkerke R2 ;81 and fourth, the percentage of cases classified correctly. 82

Additionally, the coefficient and standard error for the constant are provided. 83

The tables reported in this section below are based on the models reportedin the Appendix.84 These tables include the logit coefficients, p values,86

Odds Ratios, and the 95% confidence intervals for the odds ratio.

78. The Wald statistic is calculated by dividing the coefficient by the standard error and squaringthe result. See AGRESTI, supra note 23, at 88.

79. While neither Tables 9A/B - 10A/B, nor Models IA/B - 4A/B present the Wald statistic, the pvalues reported in Tables 9A/B-I0A/B are associated with the Wald statistic.

80. In essence this statistic captures how much better the model is at predicting the outcome whenthe independent variables are added to the equation. This value is associated with p value and providesa measure for statistical significance based on a chi-squared distribution.

81. Both the Cox and Snell R2 and the Nagelkerke R2 are considered pseudo-R2 statistics. Theyboth attempt to create a statistic that approximates the analytical value of an R2 statistic in ordinary leastsquares (OLS) linear regression. R2 is a measure of the amount variance in the outcome variableexplained by the model. For instance, statisticians interpret an R2 of 0.53 in linear regression as theindependent variable(s) explaining approximately 53% of the variation seen in the dependent variable.While these variables make sense and give some interpretive weight, they do not capture in logisticregression what an R2 would in linear regression. In linear regression the outcome variable is bothcontinuous and normally distributed. In logistic regression however, the outcome variable isdichotomous and as such the variance is conditioned on the distribution rate between the two possibleoutcomes. Therefore, the pseudo-R 2s approximate what the linear R2 measures but it is not actually ameasure of the percentage of variance explained. However, a high R2, though not an actual measure ofthe percentage of variance explained, may indicate a strong relationship.

82. The equation generated by the regression model is used to predict whether a court will pierce,and this prediction is compared with the actual decision made by the court. The full model (with allvariables included) accurately predicts 90% of cases.

83. This coefficient is the Y-intercept and is calculated by making all predictor variables equal tozero. Generally this is meaningless for interpretation, especially when models contain variables where azero value would be nonsensical. For example if we wanted to construct a model that predictedsocioeconomic status very little would be achieved by knowing what the socioeconomic status is for aperson who is 0 years old with 0 years of education.

84. The footnotes to the tables provide a reference to which Model in the footnote the data is drawnfrom.

85. Logit coefficients show the effect of a one unit increase in the independent variable on thedependent. The unit of the coefficient is log odds, such that a coefficient of 1.335 for Fraud NotDiscussed v. Not Present would mean that the Fraud not being discussed increases the log odds of apierce by a 1.335. Log odds have little if any intuitive meaning. The coefficients are most helpful forshowing the magnitude (the larger the value in either direction the larger the effect of the predictor onthe outcome variable) and direction (either positive or negative) of a relationship.

86. The p values are associated with the Wald statistic (see supra text accompanying note 78), andrepresent the level at which null hypothesis can be rejected. The null hypothesis for the Wald statistic isthat the 'true' relationship between the independent and dependant variable is zero. A p value of 0.050(which is the general level used to show statistical significance in the social sciences) means that the nullhypothesis can be rejected with 95% confidence. That is, there is only a 5% chance that if the truerelationship were nonexistent that we would have arrived at the coefficient that we did.

87. In order to provide analytic meaning to the model, the coefficients, which are reported in terms

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Additionally the models report the four measures of overall modelfit/goodness.89 The only variables reported in the tables, however, are thosethat were significant in the fourth block of the respective models.

The four tables below represent all of the findings reported by the models inthe Appendix. Tables 9A and 9B report data from Models 1A/B and 3A/B,while Tables 10A and 10B report data from Models 2A/B and 4A/B. Inexamining the tables the logit provides the first interpretive data. The larger theabsolute value the larger the magnitude of the relationship. The p value reportsthe level at which the finding is statistically significant.90 The odds ratioprovides a measure of how much a variable either increases or decreases theodds of the outcome variable occurring. Finally, the 95% confidence intervalsrepresent the range of potential values that the odds might increase or decrease.

Table 9A: Overall Dataset with Contract as Reference Categories9 1

1,0(-t 13 Ratio f'orCoefticien Valuie

Court

Appellate vs. Trial .829 .015 2.290 1.177 4.456Court

Plaintiff

Entity vs.Individual .557 .035 1.745 1.041 2.923Plaintiff

Fraud/Misrepresent-ation

of log odds, are converted to Odds Ratios by taking the antilog of the slope coefficient. The Odds Ratioreports the increase in the odds of the outcome variable occurring for a one unit increase in predictorvariable. In the case of binary categorical variables, this translates to the increase in odds relative to thereference group.

88. See Matheson, supra note 11, at 1138 n.129.89. Model Chi-Square (Z), Cox & Snell R2, Nagelkerke R 2, and Percentage of Cases Classified

Correctly.90. Generally speaking, statisticians consider ap value below 0.050 significant. See supra note 86.91. This Table reports only those variables that were statistically significant in the fourth block of

the regression models labeled Model IA and 3A in the Appendix. Because both Models IA and 3A useContract as a reference category, the coefficients for the variables in the court, plaintiff, and claim typesections remain the same, as do the statistics which assess overall model fit. The only variance betweenthe models occurs in the estimated effects of factors being present (because they are being comparedagainst different reference groups (either not present or not discussed). The relationship between notpresent and not discussed stays the same in magnitude, but works in opposite directions. For example,the effect of fraud being not present (vs. not discussed) in Model IA is -1.557, compared with the effectof fraud not being discussed (vs. not being present) in Model 3A, which is 1.557. The relationshipmeasured is the same relationship, but changes directions based on which group is the referencecategory. As such only the value associated with not present vs. not discussed is reported.

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Present vs.Not 3.936 .000 51.202 10.353 149.960Present

Present vs. Not 2.378 .000 10.788 4.972 23.408Discussed

NotPresentvs. -1.557 .000 .211 .112 .397Not Discussed

OwnerControl/Dominance

Presentvs.Not 3.011 .000 20.310 8.188 50.378Present

Present vs. Not 1.766 .000 5.845 3.205 10.660Discussed

Not Present vs. -1.245 .002 .288 .130 .636Not Discussed

Commingling ofFunds

Presentvs.Not 2.550 .000 12.808 4.321 37.966Present

Present vs. Not 1.476 .000 4.376 2.244 8.534DiscussedNotPresentvs. -1.074 .026 .342 .132 .882Not Discussed

Undercapitalization

(sum)Present vs. NotPresent

Present vs. Not .802 .031 2.231 1.076 4.625DiscussedNot Present vs.Not Discussed

OverlapPresent vs. NotPresentPresentvs.Not 1.011 .002 2.748 1.437 5.258DiscussedNotPresentvs. 1.330 .006 3.783 1.458 9.814Not Discussed

Fairness

Present vs. Not 3.647 .000 39.403 10.353 149.960Present

Present vs. Not 2.375 .000 10.750 4.229 27.324

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Discussed

NotPresentvs. -1.299 .010 .273 .101 .737Not Discussed

Assumption of RiskPresent vs. Not -4.797 .001 .008 .000 .139Present

Present vs. NotDiscussed

NotPresentvs. 2.500 .001 12.183 2.684 55.299Not Discussed

Model Summary

X2 (degrees of 703.242 .000freedom = 27)

Cox & Snell R 2 .531 -Nagelkerke R2 .744Percentage ofCases Classified 91.6Correctly I II

Table 9B: Sub-Dataset with Contract as Reference Categories 92

. t G95! C.L for ORVaniable oit P Ratio

Fraud/Misrepresentation

Present vs. Not 4.075 .000 58.858 19.867 174.380PresentPresent vs. Not 2.035 .000 7.653 3.318 17.652DiscussedNot Present vs. -2.040 .000 .130 .057 .298Not Discussed


Present vs. Not 2.416 .000 11.196 3.526 35.557PresentPresent vs. Not 1.495 .000 4.461 2.206 9.019

92. This Table reports only those variables that were significant in the fourth block of theregression model labeled Model IB and Model 3B in the Appendix, which includes only cases whereindividual liability was at stake.

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DiscussedNot Present vs.Not Discussed

Commingling ofFunds

Present vs. Not 2.252 .001 9.505 2.444 36.966PresentPresent vs. Not 1.217 .003 3.378 1.528 7.470DiscussedNot Present vs.Not Discussed

Undercapitalization(sum)

Present vs. NotPresentPresent vs. Not .859 .044 2.360 1.022 5.452DiscussedNot Present vs.Not Discussed

Non-FunctioningPresent vs. NotPresent v1.817 .027 6.156 1.229 30.826PresentPresent vs. NotDiscussedNot Present vs.Not Discussed

OverlapPresent vs. NotPresentPresent vs. NotDiscussedNot Present vs. 1.647 .018 5.189 1.321 20.376Not Discussed

FairnessPresentvs.Not 3.852 .000 35.928 6.711 192.358PresentPresent vs. Not 2.364 .000 10.639 3.520 32.154DiscussedNot Present vs.Not Discussed

Assumption of RiskPresent vs. Not -5.350 .003 .005 .000 .160PresentPresent vs. NotDiscussed

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Not Present vs. 2.744 .018 15.542 1.602 150.751Not Discussed

Model Summary

x(degrees ofx (eges f 429.711 .000 - - -freedom = 27)

Cox & Snell R2 .533 -

Nagelkerke R2 .721 -

Percentage ofCases Classified 89.4Correctly I ___1-___t_

Table 1 OA: Overall Dataset with Tort Reference Categories 93

Vairiable- L-ogit P) RaItio ORCodic 1:ient ValIue for Ioe pe

Loe Upper

CourtAppellate vs. .880 .010 2.410 1.233 4.713Trial Court

PlaintiffEntity v.Endiv. .517 .049 1.677 1.002 2.808Individual

CaseContract v. Tort .647 .033 1.910 1.055 3.459

Criminal v. Tort 1.634 .032 5.122 1.147 22.883Fraud/Misrepresentation

Present vs. Not 3.998 .000 54.480 21.709 136.720PresentPresentvs.Not 2.424 .000 11.288 5.177 24.613DiscussedNotPresentvs. -1.574 .000 .207 .110 .390Not Discussed


Present vs. Not 3.029 .000 20.673 8.291 51.547PresentPresentvs.Not 1.734 .000 5.663 3.112 10.305DiscussedNot Present vs. -1.295 .002 .274 .123 .609

93. This Table reports only those variables that were significant in the fourth block of theregression model labeled Model 2A and Model 4A in the Appendix.

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Not DiscussedCommingling ofFunds



Present vs. NotPresentPresent vs. Not.ssed .825 .026 2.281 1.102 4.724

DiscussedNot Present vs.Not Discussed

OverlapPresent vs. NotPresentPresent vs. Not 1.060 .002 2.886 1.497 5.563DiscussedNot Present vs. 1.452 .003 4.272 1.629 11.208Not Discussed

FairnessPresent vs. Not 3.668 .000 39.164 10.220 150.081PresentPresentvs.Not 2.364 .000 10.638 4.173 27.118DiscussedNotPresentvs. -1.303 .010 .272 .100 .737Not Discussed

Assumption of RiskPresent vs. NotPresent v-4.921 .001 .007 .000 .123PresentPresent vs. Not -2.356 .049 .095 .009 .992DiscussedNot Present vs. 2.564 .001 12.993 2.893 58.342Not Discussed

Model SummaryZ (degrees of 706.150 .000 --freedom = 27)Cox & Snell R2 .532 -Nagelkerke R2 .746 - -Percentage of 91.5Cases Classified I

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Correctly I

Table lOB: Sub-Dataset with Tort as Reference Categories94

Odds 91%C.I. forLogit P R~atio ORZ

. aiil L ovificient Value for LAc pe

Piercing-Fraud/Misrepresentation



Presentvs. Not 2.413 .000 11.163 3.512 35.490PresentPresent vs. Not 1.460 .000 4.305 2.129 8.706DiscussedNot Present vs.Not Discussed

Commingling ofFunds

Present vs. Not 2.241 .001 9.402 2.417 36.580PresentPresent vs. Not 1.216 .003 3.375 1.530 7.446DiscussedNot Present vs.Not Discussed


Present vs. NotPresentPresent vs. Not .882 .039 2.415 1.047 5.571DiscussedNot Present vs.Not Discussed

94. This Table reports only those variables that were significant in the fourth block of theregression model labeled Model 2B and Model 4B in the Appendix, which includes only cases whereindividual liability was at stake.

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Non-FunctioningPresent vs. NotPresent v1.824 .026 6.199 1.240 30.987PresentPresent vs. NotDiscussedNot Discussedvs. Not Present

OverlapPresent vs. NotPresentPresent vs. NotDiscussedNot Present vs. 1.733 .014 5.655 1.424 22.460Not Discussed

FairnessPresent vs. Not 3.5' .000 34.695 6.478 185.832PresentPresent vs. Not 2.358 .000 10.567 3.498 31.926DiscussedNot Present vs.Not Discussed

Assumption of RiskPresent vs. Not -5.371 .003 .005 .000 .156PresentPresent vs. NotDiscussedNot Present vs. 2.721 .018 15.193 1.594 144.774Not Discussed

Model SummaryX2 (degrees of 430.265 .000freedom= 27)Cox & Snell R2 .533 -

Nagelkerke R2 .722 -

Percentage ofCases Classified 89.6Correctly

A. Overall Model

The complete models (block 4) all produced highly accurate predictiveformulas (89.4% -91.6% correctly classified cases). That is, the regressionformula's predicted outcome ( pierce or no pierce) based on the recorded valuesof the independent variables matched the actual outcome of the case

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approximately 9 out of 10 times. 95 These percentages represent a 25-30%increase from the constant only model.96 The large jump in predictiveaccuracy from blocks 1-3 to block 4 (as evidenced by the increase inpercentage of cases correctly classified, as well as the increase in both pseudoR2 statistics) indicates that the factors courts identify as influencing thedecision to pierce the corporate veil are in fact determinative. 97 Moreover theinclusion of the factors in the fourth block either renders the variables enteredin the first three blocks insignificant,98 or greatly reduces the statisticalsignificance of the factors which are significant. This should be expected, asthese are the factors which courts have consistently turned to in cases involvingpiercing the corporate veil. 99

An interesting distinction between the overall and the sub datasets emerged.In block 4 of Models 1-4B (Tables 9B-10B) no variable in Court Information,Party Information, or Type of Case emerged as statistically significant,however, in Models 1-4A (Tables 9A-1OA) Appellate Court and Entity weresignificant and in Models 2A and 4A (Tables 1 OA) both Contract and Criminalwere significant. This suggests that when Parent-Subsidiary cases areexcluded, and the factors of the piercing test are controlled for, plaintiff typeand claim type, which appeared so strong in the descriptive statistics, have noeffect on the likelihood of piercing. The significant effects of plaintiff type andclaim type in the overall model are being driven by the power of those variablesin the parent subsidiary context. This suggests that case demographics (e.g.jurisdiction, court level, plaintiff type, and claim type) have less relevance inthe situation where a court is being asked to hold an individual liable, than

95. If it were possible to know ex ante which factors a court would find present, not present, or notdiscuss, a practitioner could with 90% accuracy determine whether a case would win or lose. Since suchknowledge is impossible, the practical use of the full model formula is minimal. More likely thepractical importance of the findings is the way in which particular factors can dramatically increase thepossibility of a pierce.

96. Models IA and 3A (Table 9A) produced a 23.5% increase over the constant only model,Models 2A and 4A (Table IOA) produced a 23.4% increase. Models I B and 3B (Table 9B) yielded a28.7% increase, and Models 2B and 4B (Tables 1013) yielded a 28.9% increase over the constant onlymodel.

97. This finding seems to state the obvious, but it is worth noting that while the percentage of casesclassified correctly in blocks 1-3 of all of the models is relatively high (over 50% in all cases), it is not asubstantial increase over the constant only model, which includes no independent or predictor variables.The factors themselves alone then make up for any increase in predictive power above the constant onlymodel.

98. In the dataset, which excludes the parent-subsidiary, the variable for plaintiff was highlysignificant in blocks 2 and 3 (p < .000), but the inclusion of the piercing factors rendered the variablestatistically nonsignificant. This suggests that the effects that party type captured in blocks 2 and 3 wereactually being driven by the piercing factors, and once those factors were included the significance ofplaintiff type was eliminated.

99. A note of caution: because the data is gathered across jurisdictions the notion that courts followthe test for piercing the corporate veil is a general conclusion, but not necessarily true of all courts. Thewide distribution of cases, with respect to jurisdiction (see tables 5A and 5B) makes evaluation ofiridividual states/districts difficult.

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when a court is being asked to hold a parent company (or entity) liable.

B. Court Information

Whether a case is before a federal or state court is not statisticallysignificant with respect to whether or not a court is likely to pierce thecorporate veil. 00 Moreover, whether a court is a trial court,appeals/intermediary court, highest/supreme court or a bankruptcy court haslittle effect on the likelihood that a court will pierce the corporate veil.' 0

In the overall dataset, when the case is before an appellate court (instead ofa trial court) the odds of a pierce increase by a factor of approximately 2.4 (seeTables 9A and 10A).102 In the data subset that excludes parent-subsidiarycases no court information variables were significant.

In the overall dataset the inclusion of the pierce factors weakened the effectof court type, which suggests that some of the significance reported in theearlier blocks was actually the influence of the factors operating through thecourt information variables, but once those factors were added to the equation,the true effect of court type was lessened.

C. Party Information

In the overall dataset a corporate plaintiff bringing the action increases theodds of piercing the corporate veil by a factor between 1.677 and 1.745 (Tables1 OA and 9A, respectfully). The strength of this finding is questionable becausethe lower bound confidence interval for entity in table 1 OA is 1.002.103 Thus,the type of plaintiff may not affect the likelihood of a court to pierce.

100. See coefficients for the variable 'Federal' in Block 4 of Models 1A/B-4A/B in the Appendix.101. See coefficients for the variables 'Appellate Court,' 'Bankruptcy Court' and 'Supreme Court'

in Block 4 of Models IA/B-4A/B in the Appendix.102. The odds ratio is calculated by taking the antilog of the coefficient. In this case the increase

by a factor of 2.4 is arrived at by comparing the odds from Tables 9A (2.290) and 1OA (2.410).103. The intervals represent, with 95% confidence, the range in which the 'true' estimate will fall.

That is, the value calculated by the regression model is an estimate of the true relationship between thevariables, and since it is only an estimate, the value is not necessarily the actual value of the relationship(interestingly, this is why the LSAT and other standardized tests often report a 'score band' because asingle test rarely captures the true score level of a test taker). In Table 9A, for example, the modelconstructed reports that the odds of a pierce arc increased by a factor of 1.745 when the plaintiff is acorporate entity, rather than an individual. The confidence intervals around that estimate are 1.041 and2.923, suggesting that the odds are actually increased by a factor somewhere between those two values.Confidence intervals provide for two statistical inferences to be drawn. First, the intervals suggest howaccurate the estimate reported is. The larger the spread suggests that the predicted odds are lessaccurate, and conversely the tighter the spread the more likely that the odds reported are accurate.Second, the odds can provide a measure of substantive (as opposed to statistical) significance. If aconfidence interval contains 1.000, then it can be said that the variable has no impact (vis-A-vis thereference category) on the outcome variable. That is, the relationship may be statistically significant,but it lacks substantive significance because the effect of the variable on the odds may be non-existent(because odds ratios represent likelihood of an increase in the outcome variable, an increase by a factoror one, is the same as no increase). See also Matheson, supra note 11, at 1138 n. 129.

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In the dataset which excludes parent-subsidiary cases the effect of plaintifftype was not statistically significant at the standard level;'0 however, the oddsratio for plaintiff type in Models 1-4B reveals a similar sized increase(1.811)'0 of the odds of piercing the corporate veil for a corporation (vis-A-vis)an individual.

D. Type of Claim

In the overall dataset, when controlling for all factors and with contract asthe reference group, there was no statistically significant effect of case type onthe likelihood of a court piercing the corporate veil. Similarly, in the subdataset, there was no statistical significance associated with the type of claimbrought by the plaintiff. The only Models in which the type of case wasstatistically significant were Models 2A and 4A (see Table 10A). In thosemodels there was an increase in the odds of piercing for both contract andcriminal cases compared to tort cases.106

While the tables in the descriptive section give the impression that thefactors related to type of claim are intimately tied to the courts' decisions topierce, it is apparent that these factors are not in fact statistically tied to theoutcome variable. That is, even though the distributions are patterned and thepattern can be explained in terms of theory, the relationship is not statisticallysignificant, which means that we cannot say for certain that a tort claim orcontract claim actually influences outcome. This highlights the distinctionmade in the introduction between the ability to answer the question when docourts pierce, and why do courts pierce. Thus, even though courts are morelikely to pierce when the claim is a contract claim, the regression analysisreveals that this factor is not statistically correlated enough to conclude that it iswhy the courts pierce.

E. Piercing Factors

Given the nature of the test for piercing the corporate veil it is expected for

104. The p value for plaintiff type in Models 1-4B was 0.053. The level of significance is ameasure of the probability that the null hypothesis (i.e. that the relationship between the independent anddependant variable is non-existent) was rejected when it was actually true. Generally the null hypothesisis said to be rejected when the probability of rejecting the null hypothesis, when it is in fact correct, isless than 0.050 (or 5%). To put it another way, when there is less than a 5% chance that the results arebased on random chance. See also Matheson, supra note 11, at 1121 n.86.

105. See Models IB-4B, where the coefficient of entity is 0.594, the antilog of which is 1.811. Theconfidence intervals run from 0.992 to 3.307, again indicating that the actual relationship may not havean effect on the courts likelihood to pierce.

106. Intuitively the coefficients associated with contract and tort should be of the same absolutevalue but opposite directions (compare the values and directions of any factors in Models IA and 3A forthe Not Present v. Not Discussed, and Not Discussed v. Not Present). This does not happen for the casevariables because of the presence of multiple claim types (see Tables 7.2A and 7.2B). While a factor isalways present, not present, or not discussed, the same cannot be said of case type.

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each factor that the comparison between present and not present would alwaysyield a statistically significant positive relationship. 0 7 That is, when a courtfinds a factor present (as opposed to finding it not present) it is expected thatthe odds of piercing will go up. Similarly, the odds of a pierce should risewhen a court explicitly finds a factor present as opposed to not discussing thefactor at all. It is expected that this too would be a statistically significantrelationship because the presence of the factor should trigger a pierce. Finally,it is expected that the relationship between not present and not discussed willbe negative, but not statistically significant. That is, when a court finds a factorto be not present it should decrease the odds of a pierce, if only because it is notfound present, but because the tests used by courts to pierce the corporate veilconsiders all factors the absence of a single factor should not be a reason to notpierce the corporate veil.

1. Fraud/Misrepresentation

Across all 8 models the affect of the presence of fraud and/ormisrepresentation had a significant, large, and positive impact on piercing thecorporate veil. A court finding fraud not present or not discussing fraud wasalso significant in every model. In the models run from the overall dataset thepresence of fraud (compared to fraud not being discussed) increased the oddsof a pierce by a factor of 10.788 when contract was the reference category forcase type (Table 9A) and 11.288 when tort was the reference category (TableI0A). When the comparison is between fraud being present and fraud notbeing present the effect of fraud is intensified. In Table 9A, where contract wasthe reference category for case type, a court finding fraud or misrepresentationto be present increased the odds of a pierce by a factor of 51.202. Similarly, inTable 1 OA, where tort was the reference category for case type, the presence offraud (vis-A-vis not present) increased the odds of a pierce by a factor of54.480.

When a court discussed fraud but did not find it present the likelihood of apierce decreased. In Table 9A, where contract was the reference category, acourt finding fraud not present (as opposed to not discussing fraud) decreasedthe odds of a pierce by a factor of .211. os In table 10A, where tort was thereference group, the odds of a pierce decreased by a factor of .207 when thecourts find fraud not present (as opposed to not discussing fraud).

107. The only exception is Assumption of the Risk, which should have the inverse relationships.108. Model 3A reports the inverse of this comparison, Not Discussed v. Not Present. The

corresponding coefficient is the same absolute value, but in the opposite direction (-1.557 in Model IAand 1.557 in Model 3A). Associated with this coefficient in Model 3A is an odds ratio of 4.746. Thisrelationship (equal in magnitude and opposite in direction) will hold for every factor discussed in boththe overall dataset and sub-dataset. For the case of analysis, however, only the Not Present v. NotDiscussed will be discussed.

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In the sub-dataset the effect of fraud was similarly pronounced. Thepresence of fraud (compared to fraud not being discussed) increased the odds ofa pierce by a factor of 7.653, when contract was the reference category for casetype (Table 9B), and 8.042 when tort was the reference category (Table 10B).When the comparison was between fraud being present and fraud not beingpresent the effect of fraud was enlarged. In Table 9B, where contract was thereference category for case type, a court finding fraud or misrepresentationtpresent increased the odds of a pierce by a factor of 58.858. Similarly, inTable 10B, where tort was the reference category for case type, the presence offraud (vis-a-vis not present) increased the odds of a pierce by a factor of61.622.

When a court discussed fraud but did not find it present the likelihood of apierce decreased. In Table 9B, where contract was the reference category, acourt finding fraud not present (as opposed to not discussing fraud) decreasedthe odds of a pierce by a factor of .130. In table 10B, where tort was thereference group, the odds of a pierce decreased by a factor of .131 when thecourts find fraud not present (as opposed to not discussing fraud).

2. Owner Control/Dominance

As with fraud, a court finding owner control and/or dominance wasassociated with a strong positive effect on the odds of piercing the corporateveil. A court finding that owner control/dominance was not present or notdiscussing fraud was significant only in the overall dataset. In the models runfrom the overall dataset the presence of owner control/dominance (compared tonot being discussed) increased the odds of a pierce by a factor of 5.854, whencontract was the reference category for case type (Table 9A), and 5.663 whentort was the reference category (Table 1 OA). As expected, when comparing afinding of owner control/dominance present as opposed to not present, theeffect on piercing is greater, than when comparing present to not discussed. InTable 9A, where contract was the reference category for case type, a courtfinding owner control/dominance present increased the odds of a pierce by afactor of 20.310. Similarly, in Table 10A, where tort was the referencecategory for case type, the presence of owner control/dominance (vis-a-vis notpresent) increased the odds of a pierce by a factor of 20.673.

When a court discussed owner control/dominance but did not find it presentthe likelihood of a pierce decreased. In Table 9A, where contract was thereference category, a court finding owner control/dominance not present (asopposed to not discussing owner control/dominance) decreased the odds of apierce by a factor of .288. In table 10A, where tort was the reference group, theodds of a pierce decreased by a factor of .274 when the courts find owner

control/dominance not present (as opposed to not discussing ownercontrol/dominance).

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In the sub-dataset the effect of owner control/dominance was statisticallysignificant, only when the court found owner control/dominance present. Thepresence of owner control/dominance (compared to not being discussed)increased the odds of a pierce by a factor of 4.461, when contract was thereference category for case type (Table 9B), and 4.305 when tort was thereference category (Table 10B). When the comparison is between present andnot present, the effect more than doubles. In Table 9B, where contract was thereference category for case type, a court finding owner control/dominancepresent increased the odds of a pierce by a factor of 11.916. Similarly, in Table1OB, where tort was the reference category for case type, the presence of ownercontrol/dominance (vis-A-vis not present) increased the odds of a pierce by afactor of 11.163.

3. Commingling of Funds

In the overall dataset commingling of funds had a consistently significanteffect on a court's decision to pierce. When commingling of funds waspresent, courts were more likely to pierce, and when the court foundcommingling not present it decreased the likelihood of a pierce at a statisticallysignificant level. In the models run from the overall dataset the presence ofcommingling of funds (compared to not being discussed) increased the odds ofa pierce by a factor of 4.376, when contract was the reference category for casetype (Table 9A), and 4.277 when tort was the reference category (Table 1 OA).As expected, when comparing a finding of commingling of funds present asopposed to not present, the effect on piercing is greater, than when comparingpresent to not discussed. In Table 9A, where contract was the referencecategory for case type, a court finding commingling of funds present increasedthe odds of a pierce by a factor of 12.808. Similarly, in Table 10A, where tortwas the reference category for case type, the presence of commingling of funds(vis-A-vis not present) increased the odds of a pierce by a factor of 12.656.

When a court discussed commingling of funds but did not find it present thelikelihood of a pierce decreased. In Table 9A, where contract was the referencecategory, a court finding commingling of funds not present (as opposed to notdiscussed) decreased the odds of a pierce by a factor of .342. In table 10A,where tort was the reference group, the odds of a pierce are decreased by afactor of .338 when the courts find commingling of funds not present (asopposed to not discussed).

In the sub-dataset the effect of commingling of funds was statisticallysignificant, only when the court found it to be present. The presence ofcommingling of funds (compared to not being discussed) increased the odds ofa pierce by a factor of 3.378, when contract was the reference category for casetype (Table 9B), and 3.375 when tort was the reference category (Table 10B).In Table 9B, where contract was the reference category for case type, a court

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finding commingling of funds to be present increased the odds of a pierce by afactor of 9.505. Similarly, in Table 10B, where tort was the reference categoryfor case type, the presence of commingling of funds (vis-A-vis not present)increased the odds of a pierce by a factor of 9.402.

4. Undercapitalization

In the overall dataset undercapitalization is only statistically significantwhen it is present and not discussed is the reference group. The implicationthat undercapitalization is statistically significant when it is present versus notdiscussed, but not significant when comparing cases where undercapitalizationwas present and cases where it was not present is that getting the court todiscuss undercapitalization is more important than having the court actuallyfind it present. In the models run from the overall dataset the presence ofundercapitalization (compared to not being discussed) increased the odds of apierce by a factor of 2.231, when contract was the reference category for casetype (Table 9A)' 09 , and 2.281 when tort was the reference category (Table10A).

The same pattern of significance emerged in the sub-dataset. The presenceof undercapitalization (compared to not discussed) increased the odds of apierce by a factor of 2.360110, when contract was the reference category forcase type (Table 9B), and 2.415 when tort was the reference category (Table1OB).

5. Non-Functioning

Non-Functioning was not statistically significant in the models for theoverall dataset. In the sub-dataset the effect of non-functioning was statisticallysignificant, only when non-functioning was present and not present was thereference group.III In Table 9B, where contract was the reference category forcase type, a court finding non-functioning present increased the odds of apierce by a factor of 6.156. Similarly, in Table 10B, where tort was thereference category for case type, the presence of non-functioning (vis-A-vis notpresent) increased the odds of a pierce by a factor of 6.199, which suggests thatcorporate formality was more important in the individual liability context thanthe parent-subsidiary context.

109. The 95% confidence interval's lower boundary is 1.076, which suggests that the effect ofundercapitalization may be minimal or non-existent. This makes sense given the lack of statisticalsignificance of undercapitalization in the other models.

110. Again, the lower boundary of the confidence interval (1.022) is dangerously close to 1.000.111. The fact that this was not true of the overall database is surprising, because, if the hypothesis

that each factor is capable of being sufficient grounds for a pierce is true, it would be expected that anyfactor present, vis-A-vis its absence, would significantly increase the likelihood of a pierce.

55


6. Overlap

Overlap proved to be the most interesting factor analyzed. Unexpectedly,the key statistical findings about overlap is that its impact turns not on presentor not present (like the other factors), but rather on discussed versus notdiscussed. In the overall dataset the odds of a pierce increased both whenoverlap was present and when it was not present. Most interesting, thecoefficients associated with overlap when it is present versus not present(Tables 9A and 10A) are statistically not significant, which supports the claimthat the key question for overlap is not so much present vs. absent, but ratherwhether a court will discuss the factor.

In the models run from the overall dataset the presence of overlap(compared to not being discussed) increased the odds of a pierce by a factor of2.748, when contract was the reference category for case type (Table 9A), and2.886 when tort was the reference category (Table 10A). When a courtdiscussed overlap but did not find it present the likelihood of a pierce increased.In Table 9A, where contract was the reference category, a court finding overlapnot present (as opposed to not discussed) increased the odds of a pierce by afactor of 3.783. In table 10A, where tort was the reference group, the odds of apierce increased by a factor of 4.272 when the courts find overlap not present(as opposed to not discussed).

In the sub-dataset the effect of overlap was statistically significant, onlywhen comparing not present to not discussed. When a court discussed overlapbut did not find it present the likelihood of a pierce increased. In Table 9B,where contract was the reference category, a court finding overlap not presentincreased the odds of a pierce by a factor of 5.189. In table 10B, where tortwas the reference group, the odds of a pierce increased by a factor of 5.655when the court found overlap not present (as opposed to not discussed).

7. Fairness

Fairness, like fraud and owner control/dominance, had a sizable effect onpiercing in the overall dataset when the court found it present, not present, ordid not discuss it. The presence of fairness (compared to not discussed)increased the odds of a pierce by a factor of 10.750 when contract was thereference category for case type (Table 9A) and 10.638 when tort was thereference category (Table 10A). When comparing a finding of fairness presentas opposed to not present, the effect on piercing nearly quadruples from the ratefound when comparing present to not discussed. In Table 9A, where contractwas the reference category for case type, a court finding fairness presentincreased the odds of a pierce by a factor of 39.403. Similarly, in Table 1OA,where tort was the reference category for case type, the presence of fairness(vis-A-vis not present) increased the odds of a pierce by a factor of 39.164.

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Vol. 7, 20 10


When a court discussed fairness but did not find it present the likelihood ofa pierce decreased. In Table 9A, where contract was the reference category, acourt finding fairness not present (as opposed to not discussed) decreased theodds of a pierce by a factor of .273. In table 10A, where tort was the referencegroup, the odds of a pierce decreased by a factor of .272 when the courts findfairness not present.

In the sub-dataset the effect of fairness was statistically significant onlywhen the court found fairness to be present. The presence of fairness(compared to not being discussed) increased the odds of a pierce by a factor of10.639, when contract was the reference category for case type (Table 9B), and10.567 when tort was the reference category (Table 10B). In Table 9B, wherecontract was the reference category for case type, a court finding fairnesspresent increased the odds of a pierce by a factor of 35.928. Similarly, in Table10B, where tort was the reference category for case type, the presence offairness (vis-A-vis not present) increased the odds of a pierce by a factor of34.695.

8. Non-Existent

Non-Existent was not statistically significant in any of the models foroverall dataset or the sub-dataset, which excluded parent-subsidiary cases.

9. Assumption of the Risk

Assumption of the risk is unlike the previous variables, insomuch as it isattributed to the plaintiff and not to the defendant. Additionally, assumption ofthe risk was an infrequently occurring factor (discussed in less than 4% ofcases). The combination of these two limitations makes assumption of the riska potentially problematic variable for the regression analysis. While thevariable was included in the overall model and is reported in Tables 9A, 9B,10A, and 10B, the result should be interpreted and employed with caution.Generally though, a finding of assumption of the risk present was associatedwith a decrease in the odds of a pierce. 112 Additionally, assumption of the riskbeing explicitly not present has a substantial and positive effect on the odds that

112. In the models run from the overall dataset, the presence of assumption of the risk (compared tonot being discussed) decreased the odds of a pierce by a factor of .095 when tort was the referencecategory (Table IOA). In Table 9A, where contract was the reference category for case type, a courtfinding assumption of the risk to be present decreased the odds of a pierce by a factor of .008. Similarly,in Table I OA, where tort was the reference category for case type, the presence of assumption of the risk(vis-A-vis not present) decreased the odds of a pierce by a factor of .007.In the sub-dataset, a finding of assumption of the risk present was statistically significant only when notpresent was the reference group. The presence of assumption of the risk (compared to not present)decreased the odds of a pierce by a factor of .005, both when contract was the reference category forcase type (Table 9B), and when tort was the reference category (Table I OB).

57


a court will pierce.113

CONCLUSION

The purpose of this Article was not to proselytize to a certain view of thepiercing doctrine or its appropriate ambit of applicability, although theempirical and analytical results do provide fodder for academic and policydebate on those topics. Nor was the purpose to prescribe litigation approachesor tactics, although practitioners can well be served by heeding the results ofthe study for their litigation strategy. Rather, this empirical study purportedprimarily to test certain major hypotheses involving substantive piercing in thecourts. Here are the results:

1. Piercing occurs with the same frequency when an entity is the owner(i.e. parent-subsidiary) as when the owners are individuals.

The descriptive statistics in this Article, as well as in prior research fromthis database suggest that hypothesis one is incorrect. The pierce rate for theoverall dataset is 31.86%, while courts pierce at a 20.56% rate when the owneris an entity and a 39.29% rate when the owner is an individual.

2. Trial courts pierce at the same frequency as appellate courts.

Hypothesis two is partially refuted by the descriptive statistics. In theoverall dataset (Table 5A) the piercing rate was lower for trial courts; however,in the individual liability sub-dataset, the piercing rate between trial andappellate courts was relatively similar. The regression analysis mirrors thesefinding. In the overall dataset the effect of court level was statisticallysignificant, but the magnitude of the effect was relatively small.114 In theindividual liability sub-dataset the level of court was not statistically associatedwith the likelihood of a pierce.

3. Courts pierce with the same frequency whether the plaintiff is acorporate entity or an individual.

This hypothesis is rejected by the descriptive statistics produced in Tables6A and 6B, but partially validated by the regression analysis. The descriptivestatistics show a marked difference in the rates of piercing for both the overall

113. When a court discussed assumption of the risk but did not find it present, the likelihood of apierce increases. In Table 9A, where contract was the reference category, a court finding assumption ofthe risk to be not present (as opposed to not discussing assumption of the risk) increases the odds of apierce by a factor of 12.183. In Table 10A, where tort was the reference group, the odds of a pierce areincrease by a factor of 12.993 when the courts find assumption of the risk not present (as opposed to notdiscussing assumption of the risk).In the sub-dataset, when a court discussed assumption of the risk but did not find it present, thelikelihood of a pierce increases. In Table 9B, where contract was the reference category, a court findingassumption of the risk to be not present increases the odds of a pierce by a factor of 15.542. In Table10B, where tort was the reference group, the odds of a pierce are decreased by a factor of 15.193.

114. Compare, in Table 10A, the magnitude of Fraud (54.480 for Present vs. Not Present), OwnerControl/Dominance (20.673 for Present vs. Not Present), Commingling (12.656 for Present vs. NotPresent), and Fairness (39.164 for Present vs. Not Present), with Appellate (2.410).

58

Vol. 7, 2010


dataset and the sub-dataset based on plaintiff type. As with hypothesis two, theregression analysis revealed that plaintiff type was significant, if only mildlyincreasing the odds of a pierce, in the overall dataset, while in the sub-datasetplaintiff was not significantly associated with likelihood of a court piercing thecorporate veil.

4. Courts pierce with the same frequency irrespective of the underlyingcause of action, that is, for example, whether a contract claim isbrought as opposed to a tort claim.

This hypothesis is not supported by the descriptive statistics; however theregression analysis indicates that the underlying cause of action does not affectthe likelihood of a pierce. The descriptive statistics in Tables 7.1A, 7.1B, 7.2A,and 7.2B suggest that the specific type of claim brought, and even thecombination of claims brought significantly impacts the likelihood that a courteither will or will not pierce the corporate veil. In the regression analysishowever, the underlying cause of action was only significant in two of the eightmodels. In those models (reported in Table 10A) the effect of a contract claim(compared to a tort claim) and a criminal claim (compared to a tort claim) wassignificant. In the other 6 models (reported in Tables 9A, 9B, and 10B theunderlying claim had no statistically significant effect on the likelihood that acourt would pierce the corporate veil.

5. Each piercing factor identified by the courts is independentlysignificant. That is, the presence of any one factor should be enoughto cause a court to pierce.

Hypothesis five was partially supported by the regression analysis. Thevariables Fraud/Misrepresentation, Owner Control/Dominance, Comminglingof Funds, and Fairness were always statistically significant when they werefound present irrespective of whether 'not present' or 'not discussed' was thereference group, and offer strong support for the hypothesis. For each of thesevariables the court finding the factor present increased the likelihood that thecourt would pierce the corporate veil.

Non-Functioning was only significant in the individual liability datasetwhen 'not present' was the reference group. This finding offers weak supportfor the claim that the presence of any factor should be enough to trigger apierce, since in the majority of models this factor was not significantly

associated with an increase in the odds of a pierce.

Undercapitalization (sum) and Overlap were not significant in any modelswhere 'not present' was the reference group; however, for both factors afinding of present was significant when compared with the court not discussing

115. The effect of Assumption of the Risk was not considered in assessing this hypothesis because(1) the factor is discussed so rarely; (2) unlike the other factors, Assumption of the Risk is attributed tothe plaintiff; and (3) the presence of Assumption of the Risk would (hypothetically) have the oppositepredicted effect.

59


the factor. That is, in the case of both Undercapitalization (sum) and Overlap,the key question for whether the factor would trigger a pierce was not presentvs. absent, but rather whether the court discussed the factor. This finding iscounter to what would be expected, that the largest effect on the odds ratiowould be when comparing present to not present, and as such it offers weakersupport for the hypothesis than the factors discussed above. While this doessupport the proposition that any finding of a factor present should increase theodds of a pierce, it is somewhat surprising that the effect came in comparison tothe 'not discussed' category rather than the 'not present' category.

Non-existent was not significant in any of the models. This finding, alongwith the findings relating to Non-functioning, Undercapitalization (sum), andOverlap, calls into question the validity of the hypothesis that any factor issufficient to cause a pierce. In fact, it appears that there is a clear breakbetween the predictive power of Fraud/Misrepresentation, OwnerControl/Dominance, and Commingling of Funds and the other factors.

6. The absence of any single factor will not be enough to prevent acourt from piercing the corporate veil.116

Hypothesis six finds greater support in the sub-dataset that looks only atcase where an individual would be held liable. In the overall dataset (Tables9A and 10A), Fraud/Misrepresentation, Owner Control/Dominance,Commingling of Funds, and Fairness all have a statistically significant negativeeffect on piercing the corporate veil.117 That is, when these factors are found'not present' the odds of a pierce are decreased. If hypothesis six were correctit would be expected that these relationships would not be statisticallysignificant, because the absence of one particular factor should not have apredictive relationship with the likelihood of a pierce. These findings suggestthat the absence of particular factors has a negative effect on piercing ratherthan the hypothesized non-effect. In the sub-dataset onlyFraud/Misrepresentation has a statistically significant negative relationshipwith the likelihood that a court will pierce the corporate veil." 8 The individualliability dataset provides great support for hypothesis six, but even still thestatistically significant relationship between a court finding fraud absent andthe likelihood of a pierce suggests that fraud, above all other factors, maybenecessary to prove in order to attain a pierce.

116. The effect of Assumption of the Risk was not considered in assessing this hypothesis because(1) the factor is discussed so rarely; (2) unlike the other factors, Assumption of the Risk is attributed tothe plaintiff; and (3) the presence of Assumption of the Risk would (hypothetically) have the oppositepredicted effect.

117. Overlap has a statistically significant effect on the likelihood of a pierce, but the finding ofOverlap 'Not Present' actually increases the odds that a court will pierce the corporate veil. See Tables9A and IOA.

118. As in the overall dataset, Overlap has a statistically significant positive relationship withpiercing the corporate veil.

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Vol. 7, 2010


APPENDIX

Model lA: Contract and Not Discussed as Reference Variables (n-929)Variable Block I Block 2 Block 3 Block 4

Court Information P (SE) P (SE) P (SE) P (SE)Federal .209(.191) .139(.194) .102(.199) .355(.331)Appellate Court .681(.196)** .712(.199)*** .686(.202)** .829(.340)*Bankruptcy Court .549(.416) .489(.423) .100(.504) -.485(1.509)Supreme Court .733(.322)* .761(.328)* .730(.330)* .785(.578)

Party InformationEntity .887(.153) .774(.159) .557(.263)

Type of CaseTort -.447(.175)* -.385(.290)Statutory .015(.242) -.075(.435)Criminal .486(.415) 1.005(.725)Bankruptcy .378(.339) -.187(.652)

Piercing FactorsFraud/Misrepresentation

Present 2.378(.395)

Not Discussed -1.557(.323)


Present 1.766(.307)

Not Discussed -1.245(.404)**

Commingling ofFunds

Present 1.476(.341)

Not Discussed -1.074(.484)*


Present .802(.372)*Not Discussed -.045(.457)

Non-FunctioningPresent .304(.455)

Not Discussed -.618(.528)Overlap

Present 1.011(.331)**

Not Discussed 1.330(.486)**

FairnessPresent 2.375(.476)

Not Discussed -1.299(.507)*

61


NonexistentPresent

Not DiscussedAssumption of Risk

Present

Not Discussed

Vol. 7, 2010

.505(.491)-.081(.582)

-2.297(1.197)f2.500(.772)**

constant -1.281(.206) -1.805 (230) -1.608(.247) -2.658(.263)

X2 15.383 50.867 61.387 703.242** (4) *** (5) *** (9) *** (27)

Cox and Snell R2 .016 .053 .064 .531Nagelkerke R2 .023 .075 .090 .744Percentage of Cases 68.1 68.1 68.2 91.6Classified Correctly(Constant OnlyModel= 68.1%)0 = Slope Coefficient SE Standard Error*p <.001 ** p <.010 *** p <.050 t p <.100

Model 2A: Tort and Not Discussed as Reference Variables (n=929)Variable Block I Block 2 Block 3 Block 4

Court Information 1 (SE) P (SE) P (SE) P (SE)Federal .209(.191) .139(.194) .108(.199) .380(.344)Appellate Court .681(.196) .712(.199) .707(.202) .880(.342)

Bankruptcy Court .549(.416) .489(.423) .268(.507) -.176(1.059)Supreme Court .733(.322)* .761(.328)* .775(.330)* .845(.577)


*** ****

Type of CaseContractStatutoryCriminalBankruptcy


Present

Not Discussed


Present

Not Discussed

Commingling ofFunds

62

.472(.176)**.170(.246).976(.434)*.536(.341)

.647(.303)*

.116(.440)1.634(.764)*-.026(.652)

2.424(.398)

-1.574(.322)

1.734(.305)

-1.295(.408)**


Present

Not DiscussedUndercapitalization(sum)

PresentNot Discussed

Non-FunctioningPresent

Not DiscussedOverlap


FairnessPresent

Not Discussed

NonexistentPresent


Present2.356(1.198)*

2.564(.766)**Not Discussed

constant -1.281(.206) -1.805(230) -2.118(.263) -3.294(.496)

X2 15.383 50.867 62.092 706.150** (4) *** (5) *** (9) *** (27)

Cox and Snell R2 .016 .053 .065 .532Nagelkerke R2 .023 .075 .091 .746Percentage of CasesClassified Correctly 68.1 68.1 68.0 91.5(Constant OnlyModel= 68.1%)3 = Slope Coefficient SE = Standard Error*p <.001 ** p < .010 *** p < .050 tp <. 100

Model 3A: Contract and Not Present as Reference Variables (n=929)Variable Block I Block 2 Block 3 Block 4

Court Information 3 (SE) 03 (SE) 13 (SE) B (SE)Federal .209(.191) .139(.194) .102(.199) .355(.331)Appellate Court .681(.196)** .712(.199)*** .686(.202)** .829(.340)*Bankruptcy Court .549(.416) .489(.423) .100(.504) -.485(1.059)Supreme Court .733(.322)* .761(.328)* .730(.330)* .785(.578)


*** ****

Type of CaseTortStatutory

-.447(.175)* -.385(.290).015(.242) -.075(.435)

63

1.453(.341)

-1.085(.484)*

.825(.371)*-.015(.454)

.326(.457)-.637(.526)

1.060(.335)**1.452(.492)**

2.364(.477)

-1.303(.509)**

.525(.487)-.143(.580)


CriminalBankruptcy


Present

Not Discussed


Present

Not Discussed

Commingling ofFunds

Present






FairnessPresent

Not DiscussedNonexistent


Assumption of RiskPresent

Not Discussed

.486(.415) 1.005(.725).378(.339) -.187(.652)

3.936(.465)

1.557(.323)

3.011(.463)

1.245(.404)**

2.550(.554)

1.074(.484)*

.847(.547)

.045(.457)

.922(.643)

.618(.528)

-.319(.540)-1.330(.486)**

3.674(.682)

1.299(.507)*

.586(.713)

.081(.582)

-4.797(1.442)**-2.500(.772)**

constant -1.281(.206) -1.805(.230) -1.608(.247) -4.748(.980)

X2 15.383 50.867 61.387 703.242** (4) *** (5) *** (9) *** (27)

Cox and Snell R2 .016 .053 .064 .531

Nagelkerke R2 .023 .075 .090 .744

Percentage of Cases 68.1 68.1 68.2 91.6Classified Correctly

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Vol. 7, 2010


(Constant OnlyModel= 68.1%)p= Slope Coefficient SE =Standard Error*p < .001 ** p <.010 *** p <.050 f p <.100

Model 4A: Tort and Not Present as ReferenceVariable Block I Block 2

Court Information P (SE) P (SE)Federal .209(.191) .139(.194)Appellate Court .681(.196) .712(.199)

** **

Bankruptcy Court .549(.416) .489(.423)Supreme Court .733(.322)* .761(.328)*

Party InformationEntity



Present

Not Discussed


Present


Present






Fairness

Variables (n=929)Block 3 Block 4P (SE) P (SE).108(.199) .380(.334).707(.202) .880(.342)

*** *

.268(.507) -. 176(1.059)

.775(.330)* .845(.577)

.887(.153) .777(.158) .517(.263)*** ****

.472(.176)**

.170(.246).976(.434)*.536(.341)

Present

Not Discussed

.647(.303)*

.116(.440)1.634(.764)*-.026(.652)

3.998(.469)

1.574(.322)

3.029(.466)

1.295(.408)**

2.538(.555)

1.085(.484)*

.840(.544)

.015(.454)

.963(.645)

.637(.526)

-.392(.542)-1.452(.492)**

3.668(.685)

1.574(.322)

65


NonexistentPresent


Present

Not Discussed

Vol. 7, 2010

.667(.709)

.143(.580)

-4.921(1.441)**

-2.564(.766)**

constant -1.281 -1.805 -2.118 -5.330(.206)*** (.230)*** (.263)*** (.983)***

X2 15.383 50.867 62.092 706.150** (4) *** (5) *** (9) *** (27)

Cox and Snell R2 .016 .053 .065 .532Nagelkerke R2 .023 .075 .091 .746Percentage of CasesClassified Correctly 68.1 68.1 68.2 91.5(Constant OnlyModel = 68.1%)1 = Slope Coefficient SE = Standard Error*p <.001 ** p <.010 *** p <.050 t p <.100

Model IB: Contract and Not Discussed as Reference Variables Excluding

VariC

FedeAppBanSupr

PEntity

Parent-Subsidiary Cases (n=565)able Block 1ourt Information 0 (SE)ral .254(.252)ellate Court .306(.264)kruptcy Court .192(.491)eme Court .328(.404)arty Information

Type of CaseTortStatutoryCriminalBankruptcy


Present

Not Discussed


Present

Not DiscussedCommingling of

Block 2p (SE).138(.257).321(.269).157(.498).327(.411)

Block 3p (SE).067(.265).313(.271).035(.603).307(.413)

Block 4P (SE).217(.416).538(.437)-.472(1.166).616(.688)

.754(.185) .709(.189) .594(.307)*** ***t

-.220(.210).234(.299).391(.452).083(.393)

-.293(.336).225(.561).973(.780)-.380(.722)

2.035(.426)

-2.040(.423)

1.495(.359)

-.920(.522) t

66


FundsPresent






FairnessPresent




Not Discussed

1.217(.405)**-1.034(.607) t

.859(.427)*

.307(.598)

.470(.517)-1.348(.699)t

.645(.449)1.647(.698)*

2.364(.564)

-1.217(.669) t

.559(.594)

.118(.830)

-2.607(l.361)

2.744(1.159)*

constant -.751 -1.174 -1.096 -2.081(.277)** (.302)*** (.315)** (.544)***1.505 (4) 18.686** (5) 21.423* (9) 429.711

*** (27)

Cox and Snell R2 .003 .033 .037 .533Nagelkerke R2 .004 .044 .050 .721Percentage of CasesClassified Correctly 60.7 58.6 61.4 89.4(Constant OnlyModel =60.7%)P = Slope Coefficient SE = Standard Error*p< .001 ** p < .010 *** p< .050t p <.100

Model 2B: Tort and Not Discussed as Reference Variables Excluding

VariC

FedAppBanSup

Parent-Subsidiary Cases (n=565)able Block Iourt Information P (SE)eral .254(.252)ellate Court .306(.264)kruptcy Court .192(.491)reme Court .328(.404)


Type of CaseContractStatutory

Block 2P (SE).138(.257).321(.269).157(.498).327(.411)

Block 3P (SE).071(.265).324(.271).110(.609).323(.413)

.754(.185) .731(.187)

Block 4P (SE).233(.417).571(.438)-.249(1.183).644(.685)

.594(.307)t

.148(.212) .409(.359)

.273(.302) .290(.560)

67


CriminalBankruptcy


Present

Not Discussed


Present






Not Discussed

OverlapPresent

Not DiscussedFairness

Present

Not Discussed

NonexistentPresent


Present

Not Discussed

.569(.477) 1.396(.830) t

.163(.396) -.212(.719)

2.085(.427)

-2.036(.422)

1.460(.359)

-.953(.526) t

1.216(.404)**-1 .025(.609)t

.882(.426)*

.312(.594)

.473(518)-1.351(.697)t

.662(.450)1.733(.704)*

2.358(.564)

-1.189(.667)t

.585(.590)

.054(.828)

-2.650(l.364)

2.721(1.150)*

constant -.751 -1.174 -1.298 -2.527(.277)** (.302)*** (.343)*** (.601)***

X2 1.505 (4) 18.686** (5) 20.810* (9) 430.265*** (27)

Cox and Snell R2 .003 .033 .036 .533Nagelkerke R2 .004 .044 .049 .722Percentage of CasesClassified Correctly 60.7 58.6 61.6 89.6(Constant OnlyModel = 60.7%)

68

Vol. 7, 2010


p = Slope Coefficient SE = Standard Error*p <.001 ** p <.010 *** p <.050 t p <.100

Model 3B: Contract and Not Present as Reference Variables Excluding

VariC

FedAppBanSup

Parent-Subsidiary Cases (n=565)able Block Iourt Information P (SE)eral .254(.252)ellate Court .306(.264)kruptcy Court .192(.491)reme Court .328(.404)


Type of CaseTortStatutoryCriminalBankruptcy


Present

Not Discussed


Present








FairnessPresent



Block 2p (SE).138(.257).321(.269).157(.498).327(.411)

.754(.185)

Block 3p (SE).067(.265).313(.271).035(.603).307(.413)

.709(.189)

-.220(.210).234(.299).391(.452).083(.393)

Block 4p (SE).217(.416).538(.437)-.472(1.166).616(.688)

.594(.307)t

-.293(.336).225(.561).973(.780)-.380(.722)

4.075(.554)

2.040(.423)

2.416(.590)

.920(.522) t

2.252(.693)**1.034(.607) t

.552(.698)-.307(.598)

1.817(.822)*1.348(.699) t

-1.001(.787)-1.647(.698)*

3.582(.856)

1.217(.669) t

.441(1.008)-.118(.830)

69



Not Discussed

Vol. 7, 2010

-5.350(1.796)**

-2.744(1.159)

constant -.751 (.277)** -1.174 -1.096 -3.826(.302)*** (.315)** (1.216)**

x2 1.505 (4) 18.686** (5) 21.423* (9) 429.711***(27)


P = Slope Coefficient SE = Standard Error*p <.001 ** p <.010 *** p <.050 t p <.100

Model 4B: Tort and Not Present as Reference Variables Excluding Parent-Subsidiary Cases (n=565)

Variable Block I Block 2 Block 3 Block 4Court Information P (SE) P (SE) P (SE) P (SE)

ral .254(.252)ellate Court .306(.264)kruptcy Court .192(.491)reme Court .328(.404)




Present

Not Discussed


Present



Undercapitalization

.138(.257)

.321(.269)

.157(.498)

.327(.411)

.071(.265)

.324(.271)

.110(.609)

.323(.413)

.754(.185) .731(.187)

.148(.212)

.273(.302)

.569(.477)

.163(.396)

.233(.417)

.571(.438)-.249(1.183).644(.685)

.594(.307)t

.409(.359)

.290(.560)1.396(.830)t-.212(.719)

4.121(.558)

2.036(.422)

2.413(.590)

.953(.526)t

2.241(.693)**1.025(.608)t

FedeAppBanSup

70


(sum)Present

Not DiscussedNon-Functioning


OverlapPresent

Not Discussed

FairnessPresent




Not Discussed

constant -.751 (.277)** -1.174 -1.298 -4.261 (1.259)(.302)*** (.343)*** **

X2 1.505 (4) 18.686** (5) 20.810* (9) 430.265***(27)


3 = Slope Coefficient SE Standard Error*p <.001 ** p <.010 *** p <.050 t p <.100

71

.569(.694)-.312(.594)

1.824(.821)**1.351(.697)t

-1.070(.789)-1.733(.704)**

3.547(.856)

1.1 89(.667)t

.531(1.002)-.054(.828)

-5.371(1.792)**

-2.72 1(1.150)*

Documents

An Empirical Study of Piercing the Corporate Veil - Berkeley Law