Incorporation in Offshore Financial Centers:

Naughty or Nice?

Warren Bailey and Edith X. Liu *

Cornell University and Federal Reserve Board

5th May 2016

Abstract

This paper studies whether incorporation in an offshore financial center (OFC) enables

expropriation or offers a beneficial alternative legal and regulatory system. After controlling

for taxation and home country characteristics, we find that OFC-incorporated firms have lower

Tobin’s q, particularly if they are high growth or from high investor protection countries.

However, offshore incorporation can enhance value when home country investor protection is

weak or overly restrictive. Estimated responses to unanticipated legal changes and patterns in

institutional investor holdings echo these findings. Thus, the consequences of legal domicile

depend on firm, home country, and OFC characteristics.

Keywords: Offshore financial center, tax haven, law and finance, corporate governance,

regulatory arbitrage

JEL classifications: F21, F38, G15, G34

* Johnson Graduate School of Management, 387 Sage Hall, Ithaca NY 14850 U.S.A., (607) 255-4627,

wbb1@cornell.edu; Federal Reserve Board of Governors, 20th and Constitution Ave NW, Washington

DC 20551 U.S.A., (202-452-3913), edith.x.liu@frb.gov. We thank Judy Beckman, Murillo Campello,

Andrew Karolyi, Denis Sosyura, Robert J. Swieringa, Ying Wu, and seminar participants at Shanghai

Advanced Institute of Finance, University of International Business and Economics, 2013 McGill

Global Asset Management Conference, 2013 European FMA meetings, Queen Mary University

London, Chinese Academy of Sciences Institute of Systems Science, Tsinghua University PBCSF,

2014 Darden International Finance Conference, Chinese University of Hong Kong, Hong Kong

Polytechnic University, and Baruch College for helpful discussions.

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1. Introduction

There is much evidence that the legal, regulatory, and disclosure environment affects firm

value and quality. For example, Daines (2001) finds that US firms incorporated in Delaware

have higher value and are more likely to experience takeover attempts than other firms.1

Doidge, Karolyi, and Stulz (2004) find that cross-listing of non-US firms in the US is

associated with a substantially higher Tobin’s q relative to non-US firms that do not list in the

US.2

These findings suggest that the environment a firm selects can have substantial

consequences.3 A firm can choose an environment that signals its commitment to strong

governance and enhances its value. Alternatively, a firm can choose a lax environment that is

less expensive and intrusive, but allows managers to expropriate ordinary shareholders

(Fernandes, Lel, and Miller, 2010; Doidge, Karolyi, and Stulz, 2010). Some jurisdictions

attract incorporations with a high quality value-enhancing legal regime while others offer weak

law, regulation, and disclosure that may allow management and controlling shareholders to

benefit at the expense of other owners. Whether or not the incorporation of a firm enhances its

value should depend on the legal environment and characteristics of both the legal host country

and the home country.

1 Bebchuk and Cohen (2003) and Subramanian (2004) find this is sensitive to the time period. 2 The choice of venue to incorporate can affect other corporate decisions and, thus, value. See, for example, the findings of Wald and Long (2007) that leverage decisions of US firms depend on which US state a manufacturer chooses to incorporate in. 3 See, for example, poison pill studies like Malatesta and Walking (1988) and Comment and Schwert (1995).

2

This paper evaluates the consequences of incorporation in an alternative legal and

regulatory system commonly referred to as an offshore financial center (OFC). These

jurisdictions range from small independent nations to tiny island dependencies that structure

their legal and regulatory regimes to encourage many financial activities including

incorporation of foreign firms (Lane and Milesi-Ferretti, 2010). Places like Bermuda, the

British Virgin Islands, and the Cayman Islands compete with larger capital markets and can

offer low-cost, efficient legal conditions that can enhance firm value and pressure other

jurisdictions to improve (Morriss, 2010). At the same time, OFCs have been accused of

enabling mismanagement and expropriation by self-serving corporate managers or controlling

owners, in addition to tax evasion, accounting opacity, and other questionable or illegal

behavior (Suss, Williams, and Mendis, 2002; Zoromé, 2007).4 While the opposing views of

OFCs have largely been discussed qualitatively, we offer new data and empirical evidence with

which to evaluate these arguments.

Specifically, we study the impact of OFC incorporation on firm value, and how changes to

laws can differentially affect OFC incorporated firms versus firms incorporated in their home

country. We focus on OFCs because they represent an opportunity to study an extreme form of

jurisdiction-shopping or regulatory arbitrage5 that has become increasingly controversial.

4 See “At Orient Express, the Board holds All the Cards”, New York Times Dealbook, 29th November 2012,

(http://dealbook.nytimes.com/2012/11/29/at-orient-express-the-board-holds-all-the-cards/) for an example of

how managers can control a firm and their own employment conditions under Bermuda law. 5 For an example, see Houston, Lin, and Ma (2012) who report patterns in cross border banking flows that are

3

Using these unusual jurisdictions, we also evaluate the factors that govern the choice to

incorporate in an OFC and the associations between OFC incorporation and institutional

investor holdings. Finally, we analyze patterns in US class action lawsuits and SEC

enforcement actions across OFC and other firms.

We find that incorporation in an OFC is associated with five to fifteen percent lower stock

market value as measured by Tobin’s q. Furthermore, OFC incorporation is more likely to be

chosen by firms from home countries with relatively high levels of investor protection, after

controlling for tax differences, which suggests that these companies consciously select a

lower-quality legal and regulatory environment. This is consistent with the hypothesis that

OFC incorporation enables managers and insiders to exploit other shareholders, rather than

enhancing value with an efficient, low cost legal environment that offers advantages over

incorporation in the US, UK, or other large capital markets. We confirm this finding with an

identification strategy centered on two exogenous legal events, a tightening of US investor

protection and an easing of a particular OFC’s law regarding corporate restructuring. These

changes to the legal environment affected OFC and non OFC firms differently in a manner

consistent with our predictions.

On the other hand, we also provide some evidence that, after controlling for firm and

home country characteristics, OFC incorporation can enhance value for companies from

consistent with regulatory arbitrage.

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countries with a less developed legal and regulatory environment. Therefore, the impact of

incorporation in an OFC depends on the characteristics of the individual firm, the environment

in its home country, and the features of the OFC itself. Finally, we examine the impact of OFC

incorporation on the holdings of global institutional investors, and find no consistent evidence

that OFC incorporated firms are shunned by this group of minority shareholders.

The balance of this paper is organized as follows. Section 2 provides details on offshore

financial centers and what it means to incorporate there. Section 3 presents a model, testable

hypotheses, and an overview of data and empirical methodology. Section 4 presents empirical

results while Section 5 summarizes and concludes.

2. Offshore Financial Centers

What exactly is an offshore financial center? Zoromé (2007) cites definitions from IMF

publications. For example:

“…’the banking system, acting as financial entrepôt, acquires substantial external

accounts beyond those associated with economic activity in the country concerned,’ or

countries where the ratio of deposit banks’ external assets to exports of goods and

services is significantly higher than the world average.”

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“…a jurisdiction in which its international investment position assets, including as

resident all entities that have legal domicile in that jurisdiction, are close to or more

than 50 percent of GDP and in absolute terms more than $1 billion.”

The scale of activity in OFCs is difficult to measure as statistics are often sparse, but the

amount of financial activity in these places seems large. In Bermuda in 2003, for example

(Zoromé, 2007; Table 8), net export of financial services was 42% of GDP and portfolio assets

were over 100 times GDP. Jurisdictions with the 2003 ratio of net export of financial services to

GDP more than two standard deviations above the mean include Bahrain, Barbados, Bermuda,

Cayman Islands, Guernsey, Hong Kong, Isle of Man, Jersey, Latvia, Luxembourg, Mauritius,

Netherlands Antilles, Panama, Singapore, Switzerland, Uruguay, and Vanuatu. Offshore banks

hold about one-eighth of global bank assets. OFC activities can contribute significantly to

government revenue and employment in a small economy,6 diversifying away from traditional

sectors such as agriculture and tourism.

There are several major differences in corporate law and regulation across the three most

popular incorporation jurisdictions selected as legal domicile by listed companies.7 Bermuda

6 In 2001 for the British Virgin Islands, for example, Suss, Williams, and Mendis (2002) report there were over

300,000 “international business corporations”, and 15% of local employment and 55% of government revenue

related to OFC functions. 7 See Conyers, Dill, and Pearman’s “Comparison of Laws in Bermuda, the Cayman Islands, and the British

Virgin Islands Relating to Offshore Companies” http://www.conyersdill.com/ and Bickley (2004).

6

requires new companies to apply for approval, disclose beneficial ownership, register a

prospectus for securities issues, and make the registry of owners publicly available. There are

no such requirements in the British Virgin Islands or Cayman Islands. British Virgin Islands

companies must make their articles of association available publicly but Bermuda and Cayman

Islands companies need not. The British Virgin Islands also features minimal numbers of

directors and shareholders, management anonymity, and ease of transfer of corporate assets, in

addition to tax exemptions.8 Furthermore, there are different structures and rules for local

corporations versus foreign businesses that conduct no local business and use the OFC purely

as a legal base. In addition to tax benefits, “international business companies” in the British

Virgin Islands and the Cayman Islands enjoy fewer governance and disclosure requirements

than local corporations while Bermuda “exempted” companies are not subject to annual filings

or foreign ownership restrictions.

Existing research on OFCs centers on corporate and personal taxation. Hines and Rice

(1994) study the use of “tax haven” affiliates by US corporations. They find that these

affiliates account for about 20% of all US foreign direct investment, and suggest that they are

motivated by low tax rates. Desai, Foley, and Hines (2006) detail the characteristics of US

multinationals that use OFCs and the tax advantages these firms enjoy. Dyreng and Lindsey

8 See http://www.bviifc.gov.vg/FinancialSectors/CorporateBusiness/Advantages/tabid/157/Default.aspx. For a

specific example, see the discussion of Michael Kors Holdings incorporating in the British Virgin Islands while

being headquartered in Hong Kong at

http://dealbook.nytimes.com/2011/12/20/the-benefits-of-incorporating-abroad-in-an-age-of-globalization/ .

7

(2009) associate significant tax saving with use of OFC subsidiaries by non-OFC firms.

Bennedsen and Zeume (2015) study the impact of tax information exchange agreements

involving “tax haven” jurisdictions.

In addition to tax benefits, Morriss (2010) notes that OFCs help US corporations lower the

cost of insurance and employee healthcare.9 At the same time, he mentions prominent events

in which OFCs are associated with local government corruption, the narcotics trade, financial

fraud, and tax evasion. There is continued concern about the uses and abuses of OFCs given

involvement in recent scandals (International Monetary Fund, 2006, 2008; van der Does de

Willebois, Halter, Harrison, Park, and Sharman, 2011; Shaxson, 2011) and continued easy

creation of shell companies and other offshore entities.

A related problem for OFC incorporated companies is the opacity and uncertainty of

the laws, regulations, and practices that affect corporate governance in OFCs. Kun (2004)

contrasts the corporate governance implications of incorporation in Delaware versus

Bermuda. From Page 347, “Publicly available materials reveal a very sparse record of

corporate litigation and precedent in Bermuda. Similarly, few published commentaries on

Bermuda law exist. Bermuda corporate law purports to be based on English law, yet there is

only a limited body of case law that interprets the meaning and application of the major

9 An extreme example is hedge funds setting up offshore reinsurance units that invest back into the hedge fund.

See “With Lax Regulation, a Risky Industry Flourishes Offshore, in New York Times Dealbook, 5th September

2012, (http://dealbook.nytimes.com/2012/09/04/with-lax-regulation-a-risky-industry-flourishes-offshore/).

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aspects of Bermuda corporate law within the English law context.” Another dimension of

uncertainty is that, as with any multinational firm, a firm incorporated in an OFC but

operating elsewhere exists under two potentially conflicting legal and regulatory systems.

Furthermore, ownership, control, performance, and management can be obscured when an

operating company is owned by a publicly-traded holding company formed in the OFC

environment. Potential problems for shareholders of OFC incorporated companies are plainly

evident from statutes and cases. For example (Bickley, 2004), Cayman Islands law requires

90% shareholder approval of a takeover, but this is suspended for a single owner holding at

least 10% of the shares. The law does not require that all shareholders be bought out at the

same price. As another example, the Bermuda courts upheld the omission of shareholders’

right to appoint additional directors at special general meetings in the 1999 amendment to the

1981 Companies Act.10

While there is almost no academic research on OFCs, what little exists suggests that OFC

incorporation is associated with poorer-quality firms (Durnev, Li, and Magnan, 2015) and

weaker returns at merger and acquisition events (Col and Errunza, 2013). Dyreng, Hanlon, and

Maydew (2012) and Durnev, Li, and Magnan (2015) find greater earnings management among

non-OFC firms with OFC-incorporated subsidiaries. Evidence on announcement effects and

other dimensions of valuation surrounding the incorporation event in OFCs is mixed.11 Adding

10 Sinocan Holdings Ltd. versus JHY International Inc. and Sze Lin Tang, 2002. 11 Desai and Hines (2002) find an average announcement response of 1.7% for 19 US firms moving

9

to the controversy, some OFCs were heavily involved in the US securitization boom which fed

the recent financial crisis (Lane and Milesi-Ferretti, 2010). OFCs certainly offer substantially

different conditions to foreign companies relative to their home environments.

Whether OFCs facilitate healthy competition between jurisdictions or a “race to the

bottom” continues to be debated.12 In the model of Bar-Gill, Barzuza, and Bebchuk (2006),

reincorporation improves the welfare of shareholders on some dimensions (such as Delaware’s

court system or an OFC’s tax system) at the cost of freeing insiders to expropriate more wealth.

Competition from other jurisdictions limits the revenue that an OFC can extract from firms.13

Thus, the costs and benefits associated with a firm’s choice of jurisdiction for incorporation are

complex.

incorporation to an OFC from 1993 to 2002. Durnev, Li, and Magnan (2015) find that small positive

announcement effects for registering or establishing a subsidiary in an OFC vanish after 2002 while valuation is

lower except for UK and US firms establishing OFC subsidiaries. 12 For example, Hong Kong’s Jardine group of companies reorganized under a Bermuda-domiciled holding

company in 1984. Aspects of Bermuda law were thought to enhance the Keswick family’s control of the group,

which could protect the company once control of Hong Kong returned to China in 1997 or could increase the

power of insiders relative to minority shareholders. 13 For example, the maximum annual corporate franchise fee is about $30,000 in Bermuda and only a few

thousand dollars per year in the British Virgin Islands or the Cayman Islands (www.taxrates.cc). For comparison,

Delaware’s maximum annual fee is $180,000 (corp.delaware.gov/frtaxcalc.shtml). Public information on the

specific legal expenses of incorporation or re-incorporation of a substantial listed public company does not seem

available See, for example, http://www.calstrs.com/Newsroom/whats_new/cominghomeISS.aspx concerning

Tyco International. Tyco’s Form S-4 dated 10th December 2008 states (page 23) “…we do not expect these costs

to be material…” Consent of the shareholders and directors must be obtained. According to law firm websites, a

straightforward incorporation of a small firm would cost several thousand dollars in legal fees. See “The

incorporation business: They sell sea shells”, The Economist, 7th April 2012,

http://www.economist.com/node/21552197/print.

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An anecdote illustrates the ambiguous potential impact of incorporation in an OFC. In

2002, The Stanley Works (now known as Stanley Black & Decker, Inc.), a US listed

corporation that manufactures tools and hardware, announced plans to move its legal domicile

from Connecticut to Bermuda. While the company’s S-4/A filing of 10th July 2002 highlights

the tax benefits of Bermuda domicile, it also mentions potential corporate governance

problems. From Page 13: “…critics of the reincorporation, such as the Attorney General of the

State of Connecticut and the Connecticut State Treasurer, suggest that it could be more difficult

to enforce the legal rights of shareholders in Bermuda, that Bermuda law is difficult to

ascertain and that they do not know how protective the Bermuda legislature and courts will be

of shareholders.” Other impediments to shareholder lawsuits noted in the filing include legal

costs,14 enforcement difficulties,15 and lack of basic information.16 Under pressure from the

public and government, Stanley withdrew the proposal in August 2002.

A more recent development is the increase in problems associated with OFC-incorporated

Chinese companies listed in US and other non-Chinese stock markets. The growth of China’s

14 Page 14: “…Bermuda does not allow contingent fee arrangements for legal suits. Litigants must pay their

own legal fees and may be required to pay some of the costs of the winning party, including attorney fees.” 15 Page 26: “…it may be difficult for you to effect service of process within the United States or to enforce

judgments obtained against Stanley Bermuda in United States courts.” and Page 15: “There is no Bermuda law

or treaty between the U.S. and Bermuda providing for the enforcement in Bermuda of a monetary judgment

entered by a U.S. court.” 16 Page 17: “There is no government sponsored or private case reporting system on decisions of the Supreme

Court of Bermuda that publishes Bermuda cases in law books. … This body of case law is available through the

Supreme Court of Bermuda Library, which is generally accessible only to Bermuda barristers.”

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economy has outpaced legal, regulatory, and disclosure practices. Therefore, OFCs may

provide Chinese firms with a more secure and predictable legal system. Recently, however,

several Chinese corporations that listed in the US have suffered from management problems

and scandals (see, for example, Hung, Wong, and Zhang, 2011). Regulators explicitly

mention the entry of problematic Chinese companies to US stock markets using reverse

mergers (Public Company Accounting Oversight Board, 2011; United States Securities and

Exchange Commission, 2011). Furthermore, as mentioned above, some of the OFC-related

structures employed by Chinese firms to list a holding company in the U.S. appear to violate

Chinese law.17 Ang, Jiang, and Wu (2016) explore the characteristics of US listed Chinese

firms that have been involved in scandals. They find that listing by reverse merger, greater

earnings management, and weaker corporate governance predict greater likelihood of

scandal. As we detail later in this study, many of the recent US listings from China are

incorporated in offshore financial centers, particularly the Cayman Islands.18

3. Empirical design

3.1 Testable hypotheses

17 See “China’s Forbidden Investment, 1st March 2012,

http://www.rkmc.com/publications/articles/china-s-forbidden-investment. 18 See Darrough,, Huang, and Zhao (2012), Jindra, Voetmann, and Walkling (2012), and Givoly, Hayn, and

Lourie (2012) for further evidence on Chinese firms listed in the US and, in particular, on the associations

between reverse listing and firm quality.

12

Doidge, Karolyi, and Stulz (2004) present a simple model to explain whether a

non-US firm chooses to list on a US stock market. In deciding whether to list shares in the US,

a non-US firm’s controlling shareholders face a trade-off. The firm can grow faster by raising

additional capital under the extensive investor protection of the US legal and regulatory system,

though this constrains the ability of controlling shareholders to expropriate minority

shareholders. In the context of our study, we can imagine that the reverse occurs when

incorporation in an offshore financial center moves a firm to a less-demanding environment

that offers potential expropriation benefits to insiders. Therefore, we adapt the model of

Doidge, Karolyi, and Stulz (2004) to the case where investor protection afforded by

incorporation at home, ρhome, can exceed the investor protection when incorporated in an

offshore financial center, ρofc. Controlling shareholders are entitled to a fraction, k, of the firm’s

total cash flow. The firm realizes cash flow of C, plus potential additional cash flow from

growth opportunities, z, and potential additional cash flows, α, from benefits of OFC

incorporation such as lower taxes, fewer legal, regulatory, and administrative burdens, and

other cost reductions mentioned earlier. The firm’s insiders then selects a fraction, f, of firm

cash flow that they expropriate beyond the amount received from k times the total cash flow.19

Expropriation imposes a cost which is quadratic in the fraction expropriated and linearly

increasing in the quality of investor protection, ρ, in the jurisdiction where the firm chooses to

19 C is assumed exogenous. A more complex model would allow managerial decisions including legal domicile

to affect the distribution and value of C.

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incorporate. Furthermore, if the firm is incorporated not at home but in an OFC, it loses the

opportunity to finance growth opportunities worth z but gains the cost efficiencies and tax

benefits worth α.

Thus, controlling shareholders enjoy the following gain if incorporated at home:

k[(C+z) – f (C+z) – ½ b f2 ρhome(C+z)] + f(C+z) (1)

where b is a constant greater than zero. In (1), the term in brackets is the cash flow of the firm

minus expropriation and minus its deadweight cost. The term, f(C+z), on the right is the benefit

from expropriation enjoyed by controlling shareholders. The gain to controlling shareholders if

incorporated in an offshore financial center is:

k[(C+ α) – f (C+ α) – ½ b f2 ρofc(C+ α)] + f(C+ α) (2)

Solve the maximization problem over f in both home and OFC incorporated situations and

substitute the solution into the original expressions to yield the total gain of the controlling

shareholders if incorporated at home:

k(C+z) + ½ [(1-k)2/bρhomek](C+z) (3)

and their total gain if incorporated in an OFC:

k(C+ α) + ½ [(1-k)2/bρofck](C+ α) (4)

Controlling shareholders will choose to incorporate in an OFC if their gain, (4) exceeds that for

incorporation at home, (3). Let ϴ equal the parameter ½ (1-k)2/bk:

k(C+z) + (ϴ /ρhome)(C+z) < k(C+ α ) + (ϴ/ρofc)(C+α) (5a)

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The left-hand side is the payoff from incorporating at home and financing growth opportunities.

The right-hand side is the payoff from greater expropriation by incorporation in an OFC.

Rearranging yields:

k(α -z) + ϴ[((C+α) /ρofc) – ((C+z) /ρhome)] > 0 (5b)

The comparative statics in Doidge, Karolyi, and Stulz (2004) imply, first, that growth

opportunities dissuade firms from incorporating in an OFC, and, second, that expropriation

opportunities encourage firms to incorporate in an OFC. This is confirmed formally in (5b).

Controlling insiders will select incorporation in an OFC if growth opportunities, z, are low

relative to cost savings, α, and expropriation is much easier in an OFC (that is, 1/ρhome versus

1/ρofc).

We can also see how value differs from the point-of-view of minority outside shareholders

who do not enjoy any of the expropriation benefits of control. The firm’s cash flow to

controlling shareholders if incorporated at home, after they select the optimal f, is:

(C+z)(1 – [(ϴ/ρhome)(1+k)/(k(1-k)] (6a)

If incorporated in an OFC, the firm’s cash flow after controlling shareholders have selected the

optimal level of f is:

(C+ α)[1 – (ϴ/ρofc)(1+k)/(k(1-k))] (6b)

The results in Doidge, Karolyi, and Stulz (2004) imply that firms incorporated in an OFC sell at

a discount relative to firms incorporated at home when investor protection is greater at home,

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and that discount is increasing in growth opportunities and the distance between ρhome and ρofc.

In our case, the premium for incorporating at home, (6a) minus (6b), is slightly more

complicated because there is a potential cost saving benefit to OFC incorporation in our model.

OFC incorporation is value destroying when a firm that incorporates in an OFC has lower

value than if it incorporates at home:

(C+z)(1 – [(ϴ/ρhome)(1+k)/(k(1-k)] > (C+ α)[1 – (ϴ/ρofc)(1+k)/(k(1-k))] (7)

In equation (7), if the potential cost savings of incorporating in OFC, α, is equal to the cash

flow increase from growth opportunities, z, then the only determinant of the value difference is

the investor protection in the OFC versus at home. In this case, when total cash flow is the same

for OFC incorporation as home incorporation, there is value destruction if and only if the

investor protection at home is stronger than in offshore, ρhome> ρofc. In contrast, if the difference

between home and OFC investor protection is small, equation (7) holds if the cash flow from

additional financed growth opportunities, z, is much larger than the potential cost savings, α,

that is offered by OFC incorporation.

In thinking about how incorporation in an OFC can affect value, it may also be the case

that OFC incorporation offers a simple, low-cost, effective legal environment that is

value-enhancing for firms:

(C+z)(1 – [(ϴ/ρhome)(1+k)/(k(1-k)] < (C+ α)[1 – (ϴ/ρofc)(1+k)/(k(1-k))] (8)

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Equation (8) holds when either the investor protection of the OFC is actually stronger than the

investor protection of the home country, or if the cash flow benefit from cost savings from OFC

incorporation exceeds the expropriation costs and the cash flow from growth opportunities.

However, investor protection in OFCs is perceived to be weaker than investor protection in

typical developed home countries. In this case, value creation from OFC incorporation arises

from potentially large cost savings, α, that dominate the small differences in investor

protection.

With these theoretical findings in mind, we offer the following testable hypotheses. Some

of the predictions compare OFC-incorporated firms to control firms that share their home

country. Other predictions imply consequences from a change in incorporation to or from an

OFC. We begin with a simple null:

H0: Incorporation in an OFC is irrelevant and, after controlling for firm characteristics,

there is no valuation difference between firms from non OFC countries that incorporate in

an OFC versus firms from a non OFC country that incorporate in their own country.

Next we compare OFC incorporated firms to non OFC incorporated firms under the

assumption that OFCs offer a weaker legal and regulatory environment:

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H1: Incorporation in an OFC imposes weaker legal and regulatory discipline on firms,

enabling expropriation of minority shareholders and lowering firm value.

A specific implication of H1 and our adaptation of the model of Doidge, Karolyi, and Stulz

(2004) concerns the characteristics of OFC-incorporated firms:

H1a: Firms with low growth opportunities or relatively weak investor protection are more

likely to be incorporated in an OFC.

We also offer cross-sectional predictions based on firm and home country characteristics:

H1b: The effects predicted by H1 and H1a are heightened for firms located in high quality

environments that choose to incorporate in an OFC with a weaker legal and regulatory

regime.

H1 and its related hypotheses are based on the idea that the OFC offers an undemanding

environment that weakens firm value and quality because ρofc is less than ρhome. In contrast, we

can imagine how incorporation in an OFC can enhance value:

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H2: Incorporation in an OFC offers firms a more efficient legal and regulatory

environment that enhances firm value.

For a firm with growth opportunities operating in an underdeveloped legal and regulatory

environment, incorporation in an OFC can enable raising funds externally.20 Furthermore,

moving incorporation from a demanding and intrusive home environment to an OFC can lower

the cost of compliance, thereby enhancing the raw cash flow, C, available to shareholders.21

As was the case for H1, H2 implies additional detailed predictions. OFC incorporation is

more likely to benefit firms by offering a more efficient legal and regulatory environment. The

gains to valuation from being incorporated in an OFC are particularly large for firms with

substantial growth opportunities or originating in a weak home country environment.

Finally, we view the irrelevance (H0), value destruction (H1), and value creation (H2)

theories of OFC incorporation through the lens of institutional investor holdings:

H3: The signs of the valuation effects of OFC incorporation predicted by H0, H1, or H2

are mirrored in patterns in the holdings of institutional investors.

20 For example, Allen, Qian, and Qian (2005) and Bailey, Huang, and Yang (2011) describe the reliance on

retained earnings that results from limited opportunities to raise funds externally.in China’s capital market. 21 See, for example, Doidge, Karolyi, and Stulz (2010) on foreign firms terminating their SEC registration when

faced with the cost of complying with the Sarbanes-Oxley Act.

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3.2 Data

3.2.1 Firms and firm characteristics

We identify firms to include in the study as follows. First, we aggregate firms from

Datastream, Worldscope, ADR, and CompustatNA. The resulting firm names include the

current constituent list of all active, dead, and suspended firms from Datastream, all active

ADRs from adr.com and adrbnymellon.com, all firms on CompactD Worldscope CD-ROMs

from January 1992 to July 2006, and all firms on Compustat North America from January 1980

to January 2012. We identify the country of incorporation using the country code (the first two

digits) of the ISIN identifier, except for Compustat NA where we use the “fic” variable. For

ADRs, we use the ISIN code of the underlying issuer. To identify the address country, the

variables “Nation” in Datastream, “loc” in Compustat North America, and “Country” in ADRs

and Worldscope CompactD are used.

Given each firm is now associated with a country of incorporation and a country of

address, we next identify those firms with country of incorporation among the OFC countries

listed in International Monetary Fund (2006). This yields a total of 7,990 firms. We then

eliminate firms that have the same country of address and country of incorporation. This

excludes local OFC companies, leaving 1,286 distinct firms.22 We also identify candidates for

our control sample. We begin with all firms in Datastream with the same countries of address

20

as the previously-identified OFC-incorporated firms, and then eliminate firms for which

country of incorporation differs from address country. This yields a total of 20,029 non-OFC

firms. Next, each of the OFC and non-OFC firms is matched with its financial variables. We

obtain annual firm-level financial variables Total Assets, Sales, Long Term Debt, Total Debt,

Shareholder’s Equity, and Market Value of Equity from Datastream and Compustat North

America for January 1980 to December 2013.23 Firms with missing financial series for any of

the above variables are discarded.

We gather or construct firm-specific characteristics as follows. For each OFC and

non-OFC (control) firm, we construct annual balance sheet and income statement variables

such as Total Assets and capital expenditure (capex). Tobin’s q is computed for each firm-year

as (Market Value of Equity + Total Assets – Shareholder’s Equity) divided by Total Assets. We

also collect Insider Holdings (that is, the fraction of shares held by insiders) for both OFC and

potential control firms through Datastream.

3.2.2 Country characteristics

We collect standard proxies for the legal and investment environment in the address

countries of our OFC-incorporated and control firms. They can be thought of as specific

dimensions of the environment faced by corporations in a particular country or as general

23 Firms are matched by isinno in Datastream and gvkey in CompustatNA. Total Assets and Total Sales are

collected directly from Datastream in US dollars while other financial variables are ratios.

21

indicators of the overall quality of that environment. Following Doidge, Karolyi, and Stulz

(2004, 2007), Bailey, Karolyi, and Salva (2006), and other authors, we begin with the index of

anti-director rights from La Porta, Lopez-de-Silanes, Shleifer, and Vishny (1998). The

anti-director rights measure does not completely serve our purpose because many of our most

interesting and controversial firms come from the People’s Republic of China. The La Porta

et al (1998) indexes do not include this country given its complex and continuing evolution

away from a centrally-planned command economy. Allen, Qian, and Qian (2005) discuss

several dimensions of China’s legal and regulatory environment and compare to countries that

are included in the La Porta et al (1998) indexes. For the anti-director rights index, Allen, Qian,

and Qian (2005) estimate an index value of 3 for China while Pistor, Raiser, Gelfer (2000)

estimate an index value of 3.45 for Russia. We use these values into tests that employ the

anti-director rights index.

We collect additional country characteristics as follows. First, Djankov, LaPorta,

Lopez-de-Silanes, and Shleifer (2003) measure the time, in days, to complete two basic legal

procedures (collecting on a bounced check, evicting a tenant for nonpayment of rent) across

dozens of countries including China and many of our OFCs. They reflect the quality and

efficiency of the workings of a country’s legal system. Given that these two series are highly

correlated (ρ=0.4531), we select one, the eviction measure. Second, the World Bank’s ‘‘Doing

Business’’ indicators (DB) for disclosure, legal rights, credit information, director liability, and

22

shareholder suits (www.doingbusiness.org) measure other facets of the quality of the business

environment in a country annually starting with 2004. Given the often high correlations among

these measures, the eviction measure, and the anti-director rights index, we select one, the

disclosure quality indicator.

While these measures hold generally for developed nations, they may be poor proxies for

more complex and less transparent regulatory regimes, such as China. For example, Djankov,

LaPorta, Lopez-de-Silanes, and Shleifer (2003) reports values for the time it takes to collect on

a bad check or to evict a tenant for non-payment of rent, and their tables include China.

However, checks are not as widely used in China as in other economies and evictions do not

often involve formal legal proceedings. Moreover, the environment perceived by investors and

the measures tabulated by these sources can depend on whether the investor is a local or a

foreigner.

3.3 Methodology

We test our hypotheses from Section 3.1 with several specifications as follows. We regress

Tobin’s q or measures of institutional holdings on firm and home country characteristics and an

OFC dummy variable:

qit = α0 + α1Dofc,it + β’xit + δ’cit + εit (9)

23

Dofc,it is the OFC dummy which equals one if firm i is incorporated in an OFC in year t. xit is a

vector of firm characteristics, and cit is a vector of country characteristics. The observations are

firm-years for the two types of firms. We define OFC firms as those located in a non-OFC

country but incorporated in an OFC country, such as a US firm incorporated in the Cayman

Islands. In addition, our OFC sample includes a number of firms located in an OFC country but

incorporated in a different OFC country. Our non OFC (control) firms are both located and

incorporated in non OFC countries. Firms which are both located and incorporated in the same

OFC are excluded.24

An extension of (9) is a two-stage Heckit procedure to control for a specific self-selection

bias. The self-selection effect is akin to an omitted variable in (9) that is correlated with the

OFC dummy, Dofc,it. Because the omitted variable is a component of the error term in (9) and

correlated with the OFC dummy, our estimate of the slope coefficient on Dofc,it from equation (9)

will be biased. To correct for this, we estimate a two-stage procedure following Doidge,

Karolyi, and Stulz (2004) and Bailey, Karolyi, and Salva (2006). The first stage is a Probit

regression to identify factors that predict whether a firm chooses to incorporate in an OFC.

The dependent variable is the OFC dummy. The independent variables are country and firm

characteristics. The second stage regresses firm-specific Tobin’s q on the OFC dummy variable

24 This excludes the relatively large number of firms that are both located and incorporated in Hong Kong,

Ireland, Singapore, and Switzerland.

24

and country and firm characteristics as in (9), plus the inverse Mills ratio from the first stage.25

Another single-stage variation on (9) adds interaction terms between the explanatory variables

and the OFC dummy. This decomposes the OFC valuation discount or premium measured by

the slope, α1, on the OFC dummy in (9). To further refine our valuation comparison of OFC

incorporated firms versus non-OFC incorporated firms accounting for selection bias, we also

employ a matched estimator strategy. For each OFC incorporated firm, we select a comparable

non-OFC firm from the same country, industry, and year that has the closest propensity score.

For an alternative perspective on the impact of exogenous changes in law or regulation

on Tobin’s q, we exploit the impact of the Dodd - Frank of 2010 on executive compensation

and the Cayman Islands Company Law of 2009 on mergers and acquisitions. We apply a

difference-in-difference methodology to understand the effect of these legal changes on OFC

versus non-OFC incorporated firms. Finally, we study holdings of US institutional investors

using a specification similar to (9) to determine whether professional investors distinguish

OFC and non-OFC incorporated firms in a manner related to our hypotheses.

4. An overview of the data

Table 1 tabulates country of incorporation, country of address, and listing exchange for

the OFC-incorporated firms that we have identified. Panel A indicates a total of 1,286 distinct

25 We run the Heckit for Tobin’s q yearly, then report average coefficients: unlike Doidge, Karolyi, and Stulz

(2004) who use data for one year, 1997, we have data from a number of years.

25

OFC-incorporated firms. It is evident that country of incorporation is heavily concentrated in

two jurisdictions, Bermuda and the Cayman Islands, which account for over 80% of the sample

firms. The British Virgin Islands is a distant third, with less than 5% of the OFC sample firms.

Among the home countries for these OFC incorporated firms, Hong Kong firms account for

almost half of the OFC sample, Chinese firms about a quarter, and UK and USA firms less than

10% and 5% of the OFC sample firms respectively. Most Hong Kong address firms are

incorporated in Bermuda while most China address firms are incorporated in the Cayman

Islands. Most UK address firms incorporate in the nearby British Crown dependencies of

Jersey, Isle of Man, and Guernsey. Interestingly, the majority of OFC firms incorporated in the

Marshall Islands are Greek shipping firms.26 These findings suggest that some jurisdictions

cater to the needs of firms from specific home countries or perhaps even specialize in particular

clients or industries.

Panel B of Table 1 summarizes the exchanges where OFC-incorporated firms are listed.

We note several prominent patterns in these two panels. First, formal listings of OFC firms on

US exchanges (Panel B) are infrequent, except for Chinese firms listed on Nasdaq and

incorporated in the Cayman Islands. Panel C indicates several hundred firms headquartered in

Hong Kong, incorporated in Bermuda or the Cayman Islands, and listed in Hong Kong.

While Table 1 presents a geographical summary of the firms we have identified, our

26 The Marshall Islands offers special provisions for the incorporation of maritime shipping companies.

26

goal is to examine associations between valuation and the firm’s legal domicile. Matching

financial information to the firms yields missing observations that reduce the number of

available firms. Because firms can have missing observations for some variables and years,

firm-year observations with missing Total Assets, Shareholder’s Equity, and Market Value are

excluded as they are necessary to compute Tobin’s q. Furthermore, we drop firm-year

observations with negative Total Assets, Shareholder’s Equity, or Market Value. We also

exclude any firm with Total Assets less than 50 million US dollars to avoid both very small

firms and outliers when computing Tobin’s q. Finally, we winsorize extreme observations of

Tobin’s q at the top and bottom 1%. This yields a total of 1,090 firms for the OFC incorporated

sample and 12,493 firms for the control sample.

Table 2 summarizes the characteristics of the firms used in subsequent empirical tests.

Panel A reports averages of Tobin’s q broken down by country of incorporation and country of

address. Substantial differences across the rows and columns are evident. Hong Kong firms

that incorporate in Bermuda or the Cayman Islands have higher q than control firms, while

those that incorporate in the British Virgin Islands or other OFCs display an extremely low

average q of 0.608 and 0.706 respectively. A broadly similar pattern is observed for Chinese

firms, though the benchmark Tobin’s q of Chinese control firms is low at 0.773. In contrast, the

benchmark US and UK firms that incorporate at home have high q, and, while low q is also

evident for US and UK firms incorporated in the British Virgin Islands, the difference is not as

27

great as for Hong Kong and China address firms. The gap between OFC sample and control q is

less stark for UK and US firms and is statistically insignificant in most cases.

Panel B of Table 2 summarizes basic descriptive financial variables to better understand

the firms in our two samples. Firm size as measured by log of USD sales is always smaller for

OFC incorporated firms, though the difference with control firms is not statistically significant.

Firm size as measured by log of USD assets is statistically significantly smaller for all but

“Other” OFC incorporated firms compared to control group firms. Firms incorporated in

Bermuda, the British Virgin Islands, and the Cayman Islands tend to have lower capex (scaled

by total assets), while firms incorporated in other OFCs have higher capex compared to control

firms. However, the comparison of means and medians reveals some potentially large positive

outliers for capex. OFC incorporated firms typically have higher percentage of insider

ownership than non-OFC incorporated firms, though the difference is not statistically

significant. Firms incorporated in Bermuda or the British Virgin Islands have statistically

significantly higher sales growth than control firms while the opposite is found for firms

incorporated in the Cayman Islands or “Other” OFCs. However, the large differential between

means and medians for sales growth again suggests the presence of large outliers. Finally, when

comparing Debt/Equity, British Virgin Islands and Cayman Islands incorporated firms appear

to be less levered than their non-OFC counterparts, though this difference is statistically

significant for Cayman Islands incorporated firms only.

28

Table 2 also allows us to understand some of the structure of missing observations among

the firm-years of data. For example, Panel A shows 498 Bermuda-incorporated firms that are

included in the sample because each has at least partial data for Sales Growth, Log Sales, and

Debt to Equity while Panel B shows the number of non-missing Bermuda firm-years for these

variables range from 5,315 for Sales Growth to 5,799 for Assets. However, the pattern of

missing observations differs across series.

5. OFC incorporation and firm value

This section report results of the panel regressions following Doidge, Karolyi, and Stulz

(2004) and Bailey, Karolyi, and Salva (2006). The dependent variable is Tobin’s q.

Independent variables are characteristics of the firm and of its home country.

5.1 Single-state regressions to explain Tobin’s q

Panel A of Table 3 presents estimates of the panel regression, Equation (9), to explain firm

Tobin’s q. To correct for the outliers implied by Panel B of Table 2 for sales growth and capex,

we winsorize these two variables at 1% and 99% before conducting the regression analysis.

Specification (1) includes only a constant and the OFC dummy.27 It yields a negative and

marginally statistically significant coefficient of -0.050 (t-statistic = -1.88) on the OFC dummy

and a negligible r-squared.

27 For all panel regression specifications, standard errors are clustered by firm.

29

Next we add country governance measures, industry median q, sales growth, and year

fixed effects. The coefficient on the OFC dummy in specification (2) becomes more negative

and statistically significant, -0.081 (t-statistic = -3.58), and the r-squared rises to 18%. Firms

incorporated in an OFC have a lower Tobin’s q relative to counterparts incorporated

domestically, which is consistent with our hypothesis, H1, that OFC incorporation facilitates

managerial expropriation and reduces firm value. Specification (2) also displays a slope

coefficient on the ADR dummy that is significantly positive (0.163, t-statistic = 5.90). This

supports the findings of Foerster and Karolyi (1999) and Doidge, Karolyi, and Stulz (2004) that

firms which choose to cross-list tend to have higher valuation. Specification (2) also shows that

the anti-director rights measure has a positive and statistically significant slope coefficient

while the eviction measure has a significantly negative slope. Confirming other studies such as

La Porta et al (2002), this indicates that Tobin’s q is typically lower for firms from countries

with weaker investor protection or a less efficient legal system. Counter-intuitively, the

negative slope coefficient on the disclosure index suggests that firm value is lower for

countries with high corporate disclosure quality. Note, however, that there is considerable

multicollinearity among the country characteristics. For example, the anti-director rights index

has a correlation of -40% with the speed of evicting a tenant and 58% with the World Bank

disclosure index. Among the other variables in specification (2), sales growth has a strong and

30

significantly positive association with firm value, which sensibly suggests that growing firms

tend to earn high value in the stock market.

To investigate further, we replace our Eviction and World Bank disclosure index measures

with the original La Porta et al (1998) country governance variables of Judicial Efficiency and

Accounting Standards (which do not have values for Russia and China) in Specification (3).

With the original La Porta et al (1998) country governance variables, accounting standards

becomes significantly positive, anti-director rights remains significantly positive, and judicial

efficiency is statistically insignificant. More importantly, using the country governance

variables (Judicial Efficiency and Accounting Standards) that exclude China and Russia,

specification (3) shows a more negative and statistically significant slope coefficient on the

OFC indicator (-0.155, t-statistic = -6.23). This hints at the results of a later specification which

examines Chinese firms only, and yields a large positive and strongly significant OFC slope

coefficient.

Returning to country governance measures that include China, specification (4) adds

capex scaled by lagged total assets, home country tax rate,28 and percent insider holding. The

OFC dummy remains significantly negative (-0.078, t-statistic = -3.00). The slope coefficient

on Insider Holdings is significantly negative (-0.184, t-statistic = -7.78), indicating that large

insider holdings are associated with lower value. The positive slope coefficient on the ADR

28 Since most OFCs have negligible corporate income tax rates, the home tax rate can also be interpreted as the

difference between the non-OFC and OFC tax rate.

31

dummy and the negative slope coefficient on the Eviction measure both remain highly

statistically significant. To understand the impact of outliers of sales growth and capex,

Specification (4a) re-estimates regression (4) with unwinsorized values for these two variables.

The outliers among the original values of sales growth and capex greatly reduce the magnitude

and statistical significance of their slope coefficients. However, more importantly, the

magnitude and statistical significance of the slope coefficient on our variable of interest, OFC

dummy, remains consistently negative.

Specification (5) includes a proxy for labor costs, total employee costs, to capture

potential savings from OFC incorporation. This variable is only available starting in 1991 and

is missing from many firms, thus reducing the number of observations from 125,397 firm-years

in specification (4) to 50,964 in specification (5). As a result, the sample changes significantly,

and has a greater proportion of Chinese OFC firms. The slope coefficient on Total Employee

Costs is insignificant, and the slope coefficient on OFC dummy becomes insignificant.

A concern with these regressions is the possibility that the large number of OFC firms

from China (319) and Hong Kong (638) can inadvertently tilt results. Therefore, specifications

(6) and (7) are estimated for China only and Hong Kong only respectively.29 For China only,

the slope on the OFC dummy switches to strongly positive (0.491, t-statistic = 7.67) while the

slope on the DR dummy is insignificant, perhaps due to the small number of OFC incorporated

29 We manually classify firms as Hong Kong addressed firms often operate in China and are controlled by

Chinese directors and shareholders.

32

Chinese firms that do not have an ADR. The slope on capex is strongly positive. The positive

slope on the OFC dummy supports hypothesis H2: after controlling for other characteristics,

firms from a relatively less developed environment, China, appear to benefit from

incorporation in an OFC. Specification (8) for Hong Kong also yields a positive though

insignificant (0.063, t-statistic = 1.59) slope coefficient for the OFC dummy. 30

Panel B of Table 3 presents results of Fama-Macbeth regressions following the same

specifications as Panel A. We run yearly cross-sectional regressions, report averages of the

yearly slope coefficients, and compute t-statistics using Fama-Macbeth standard errors. The

Fama-Macbeth regressions reveal more negative and statistically significant slope coefficients

on the OFC dummy. In comparison to the pooled OLS estimate of -0.078 (t-statistic = -3.00)

for specification (4) in Panel A, the corresponding Fama-Macbeth regression in Panel B reports

a coefficient of -0.140 (t-statistic = -5.93). This difference suggests two things. First, the slope

coefficient on the OFC dummy tends to be less negative in the cross-section for the more recent

years, due to the increasing number of China and Hong Kong OFC-incorporated firms with

positive slope on OFC dummy. Second, the yearly estimates of the OFC slope coefficient tend

30 Hong Kong is considered a developed legal and regulatory jurisdiction so benefits of OFC incorporation to

firm value, H2, are less obvious. Legal publications suggest that Cayman Islands law is more flexible and

friendly to controlling shareholders than Hong Kong law on dimensions such as corporate migration, exit

strategies, share repurchases, mergers, and loans to insiders to buy shares. See, for example, Norton Rose

Fulbright “Thinking outside the box: using a BVI/Cayman Islands incorporated company in Hong Kong”,

January 2012, http://www.nortonrosefulbright.com/knowledge/publications/62447/thin .

33

to be fairly stable over time, leading to small Fama-MacBeth standard errors and larger

t-statistics. The Fama-MacBeth regressions also confirm the associations between valuation

and ADR dummy, anti-director rights, sales growth, percent insider holding, and capex found

in Panel A, and uncover significance for home tax rate. The China only Fama-MacBeth

regression, (6), confirms the significantly positive slope coefficient on OFC dummy while the

Hong Kong only Fama-MacBeth produces a marginally significantly negative slope on OFC

dummy unlike the insignificant finding in Panel A’s pooled regression.31

To further understand what factors drive the valuation difference between OFC

incorporated and home domiciled firms, we follow Doidge, Karolyi, and Stulz (2004) and

Bailey, Karolyi, and Salva (2006), and decompose the effects of OFC incorporation on firm

value by using interactive explanatory variables in regressions. We repeat the panel regressions

of Panel A of Table 3 with additional terms that interact each explanatory variable with the

OFC dummy. The results are presented in Panel C of Table 3.

We summarize the more interesting findings as follows. In three of four specifications

using the All Countries sample, the slope coefficient on Anti-Director Rights x OFC dummy is

significantly negative, ranging from -0.0783 to -0.0983. Thus, stock markets apply a discount

of about eight or ten percent to firms from high quality home environments that are

31 The Fama – MacBeth regressions weight each year equally while the pooled regressions put greater weight

on years with more firm-year observations. For Hong Kong, there are a greater number of firm-years from the

more recent period when there is a Tobin’s q premium associated with OFC listed firms.

34

incorporated in an OFC. Apparently, these firms evade high investor protection at home and

the estimated cost to minority shareholders results in a lower stock price. Similarly,

significantly negative slope coefficients on Sales Growth x OFC dummy range from -0.0632 to

-0.0973 for the All Countries sample of firms. Stock markets discount the value of growing

firms which would benefit from ready access to additional capital but instead are incorporated

in an OFC. The interactive regression results show that stock markets recognize the potential

for expropriation of minority shareholders by OFC incorporated firms, particularly for firms

that appear to be avoiding stronger legal jurisdiction and for growth firms where the potential

reduction in capital raising ability is more costly.

5.2 Self-selection bias and explaining Tobin’s q

While Table 3 shows that Tobin’s q is typically lower for firms incorporated in OFCs, the

single equation regressions do not account for potential self-selection bias: firms choose

whether or not to incorporate in an OFC. To correct for this, we explore two estimation

strategies, Heckman 2-stage (Heckit) and Matched estimator. In the Heckit 2-stage regressions,

the first-stage selection models the decision to incorporate off-shore as a probit while the

valuation (second) stage includes the inverse Mills Ratio as a control for self-selection into

OFC incorporation and the bias effect on the slope coefficient.

35

Highlights of the estimates presented in Table 4 are as follows. Across the three estimates

of the probit equation, the only consistent findings are that firms from countries that score high

on investor protection (anti-director rights index) and have low corporate tax rates are more

likely to incorporate in an OFC.32 This is consistent with H1: some firms seek to avoid the

scrutiny of their high quality home environment. The probit regressions are based on an

average of 5665 to 6851 cross-sectional observations per year and return average pseudo

r-squared ranging from 37 to 46 percent. The finding that relatively low home country

corporate tax rate is associated with OFC incorporation is intriguing. This result is likely

influenced by the large number of OFC incorporated firms from Hong Kong, a jurisdiction

with both high quality home country governance and low corporate taxes. Thus, while much

attention has been focused on OFC incorporation as a means of tax avoidance, this result

suggests that firms also use OFC incorporation to evade high quality investor protection and

governance in their home countries.

To correct for selection bias, we use the Inverse Mills Ratio from the probit or selection

equation in the second-stage Heckit estimates which also appear in Table 4. The results of the

Heckits are very similar to those of the Fama-Macbeth regressions presented in Panel B of

32 We confine our probit specification to country variables, and leave firm characteristics as controls in our

second-stage valuation equation. As discussed in Doidge, Karolyi, and Stulz (2004), while firm characteristics

clearly affect the decision to incorporate in an OFC, overlapping variables in both stage one (probit) and stage

two regressions can generate a multicollinearity problem, as the Inverse Mills Ratio is a function of the

overlapping variables.

36

Table 3. In particular, the slope on the OFC dummy is negative and strongly statistically

significant in the first (-0.1401, t-statistic = -6.52) and second (-0.1352, t-statistic = -5.80)

Heckit specifications. For the first and second Heckits, the slope coefficients on the Inverse

Mills Ratio are statistically insignificant. This suggests that either the impact of selection bias

in the single-stage regressions of Table 3 is not particularly strong or the country characteristics

used in the probit do not fully capture the selection decision.

As was the case for panel and Fama-MacBeth regressions reported in Table 3, the

inclusion of the Total Employee Costs variable in the third Heckit costs many observations.

Due to missing data for earlier years, this specification begins in 1991. The average number of

yearly observations in the cross-sectional Probit is based on all observations with non-missing

country characteristics. It rises to 6851 given the greater number of firm-years available for

1992 and later. At the same time, the average number of yearly observations in the

cross-sectional Heckit drops to 2214 given many missing observations for Total Employee

Costs. As a result, the third Heckit produces an insignificant slope on the OFC dummy.

Counterintuitively, it also yields a significantly positive slope on the Inverse Mill’s Ratio. We

suspect that these results can be explained by the preponderance of China and Hong Kong

firms, but we do not run the two-pass procedure separately for those two jurisdictions given the

number of available observations.

37

An alternative approach to the Heckman regressions of Table 4 directly matches treated

and control firms using firm characteristics. Again, we use a probit model to predict the

likelihood that a firm with certain characteristics will select OFC incorporation. Then, with the

predicted probability of OFC incorporation from the first stage probit or propensity score, we

match each treated (OFC) firm with a control (non-OFC) firm drawn from the same address

country, industry, and year. The average Tobin’s q is then computed and compared for OFC

incorporated firms and their non OFC incorporated matches.

Table 5 presents the results of these tests computed over all firms, for China address firms

only, for Hong Kong address firms only, and for all firms except those from China or Hong

Kong. Given the small country-industry-year bins used for the matching process, we remove

firm-year observations with outliers for sales growth or capex, rather than using the winsorized

values of these variables, to obtain the most precise possible matches. First, compare the

unmatched and matched probit regressions in Panel A. For example, the unmatched “All Firms”

probit has a pseudo r-squared of 62.9%. After the firms are matched and the probit is

re-estimated on the matched sample, the pseudo r-squared coefficient for “All Countries” and

“Matched” is reduced to only 1.52%. The dramatically lower pseudo r-squared is a

combination of the success of the matching estimator and the substantial explanatory power of

country effects.33 Even without country effects, when we separately evaluate China and Hong

33 We also conducted matches within bins based on alternative combinations of country, industry, and year. We

find country bins to be particularly effective in reducing the pseudo r-squared of the probit model.

38

Kong, we obtain large reductions in pseudo r-squared from firm characteristics, industry, and

year, although the reduction in matched pseudo r-squared for China is not nearly as large as that

for other categories.

Panel B of Table 5 reports the average Tobin’s q across categories and tests the

significance of the difference across OFC and non OFC firms. For all firms, the average

Tobin’s q is 1.152 for OFC incorporated firms and 0.939 for matching (“ATT”) non OFC

firms,34 with a t-statistic of 7.36 indicating the difference is statistically significant. However,

the other columns show that the significant positive valuation premium for OFC incorporated

firms is driven primarily by the large number of China and Hong Kong address firms. In

contrast, the matched “All except China and Hong Kong” category shows a discount of almost

9% on the value of OFC incorporated firms (1.279) relative to other firms (1.366). Therefore,

the distinct matching estimators for “China”, “Hong Kong”, and “All except China and Hong

Kong” collectively confirm our earlier conclusions from the pooled and Fama-Macbeth

regressions. OFC incorporation by a firm from a developed environment is associated with

lower value (H1) while OFC incorporation by a firm from an underdeveloped environment can

be associated with higher value (H2). However, the findings for Hong Kong, a highly

developed jurisdiction, do not fit this pattern.

34 We match each OFC firm with its nearest neighbor (NN=1). We also used nearest five neighbors, but if each

OFC firm requires at least five control firms within the Country, Industry, Year group, our sample becomes

greatly restricted. Results using nearest five neighbors exhibit a larger and a statistically stronger OFC discount

than those reported here.

39

5.4 Difference-in-difference tests

To further identify the effect of OFC incorporation on firm value, we conduct

difference-in-difference analysis based on two events that we expect to change the costs and

benefits of OFC incorporation for certain firms. Since firms selected their OFC incorporation

status prior to these events, the analysis allows us clear identification of the valuation effect

of OFC incorporation.35

Our first difference-in-difference analysis is centered on the US legal response to the

global financial crisis of 2008. The Dodd – Frank Act was approved by the US Congress and

signed into law by President Obama on 21st July 2010. While the primary purpose of the act

is, as its title indicates, “The Wall Street Reform and Consumer Protection Act”, it also has

implications for governance, disclosure, and compensation for all corporations registered

with the US SEC. Mandates for SEC implementation in Sections 953 and 954 include

35 In an earlier draft, we investigated the effect on firm value of moving incorporation to an OFC. The number

of such events is small and we find that on average Tobin’s q declines from 2.65 in the two years prior to

re-incorporating to an OFC to 1.25. However, these events are coupled with other corporate events such as

reorganizations and mergers that simultaneously affect firm value. Moreover, these corporate decisions are more

likely to suffer from self-selection bias. For these reasons, we do not further analyze re-incorporation events,

although preliminary results are available upon request. Col, Liao, and Zeume (2016) study several hundred

inversion events that combine re-incorporations with mergers or other reorganizations across dozens of

countries, focusing on the effect of taxation on the volume of inversions. They also present some cursory

evidence that inversions into poor governance host countries lower Tobin’s q, which parallels our findings.

However, inversions combine both relocation to a new jurisdiction and a merger or reorganization, and the

valuation effects are not easy to empirically disentangle.

40

disclosure and justification of executive compensation, rules for selecting compensation

consultants and determining the independence of compensation committee members, and

shareholder votes on aspects of executive compensation. A particularly important feature of

the legislation is “no fault clawback” of executive compensation triggered by a material

financial restatement. The SEC can prohibit firms that lack such a policy from listing on a US

securities market. These elements of Dodd – Frank effectively tighten corporate governance

and investor protection in the US capital market.

This tightening of disclosure and regulatory standards provides a quasi-experimental

setting to understand the effect of OFC incorporation. Compare US address firms incorporated

in the US to US firms incorporated in an OFC. Since Dodd-Frank increases corporate

governance and disclosure standards, we would expect both types of US firms to experience a

positive change in their valuation. However, we argue that shareholders of OFC incorporated

firms cannot capture the full value of the higher quality governance and disclosure standards

due to costlier monitoring and enforcement.36 Therefore, we expect that, while Dodd-Frank

increases Tobin’s q for all firms due to tighter supervision, OFC incorporated firms will have

a smaller valuation gain relative to their US incorporated counterparts. Our prediction for the 36 First, these firms require extra legal and accounting help in complying with such regulations, particularly if

they are more likely to be scrutinized by US regulators, shareholders, and creditors given the general reputation

of OFCs. Second, the SEC considers OFC-incorporated firms “foreign issuers”, and SEC Rule 12h-6 relaxed

some regulatory controls on such firms starting in 2007. Specifically, foreign issues registered in the US have

been allowed much more freedom to deregister and, thus, escape the US legal, regulatory, and disclosure

environment (Fernandes, Lel, and Miller, 2010).

41

relative values of OFC and non OFC US corporations around the enactment of Dodd – Frank

is:

H4: Given specific corporate governance and investor protection elements of the

Dodd – Frank Act, the valuation discount on US address firms incorporated in an

OFC relative to US firms incorporated in the US widens starting in 2010.

To test H4, we re-estimate our basic regression specification with US address firms only and

two additional variables, an event intercept dummy equal to one for firm-years in or after

2010 and the difference-in-difference interactive equal to this event dummy times the OFC

dummy. In addition, we use the data in the 5 year period prior to the event to test the parallel

trends assumption that supports difference-in-difference analysis (Roberts and Whited, 2012).

While our Dodd–Frank difference-in-difference examines the valuation impact of a

change in home country law, our second difference-in-difference examines a change in

corporate governance in one of our most important OFC jurisdictions, the Cayman Islands.

The Cayman Islands Company Law passed in May 2009 clarifies and eases the process of

merging a Cayman Islands incorporated firm with another firm, or taking that firm private.37

Ogier Group (2009) notes: “Prior to these amendments, mergers of Cayman Islands companies

37 A 2011amendment reduced from ¾ to 2/3 the fraction of shareholders needed to approve a merger.

42

had to be effected by means of schemes of arrangement and ancillary orders... approved by

majorities of the shareholders … and by the court, [and] … designed to make a scheme which

had been so approved binding upon a dissenting minority.” and “… no means existed to vest

the assets and liabilities of one company in another company by operation of law.” They

conclude with: “These new provisions introduce a more efficient and cost effective means of

merging or consolidating companies … without recourse to the courts … whilst preserving

protections for secured creditors and dissenting minority shareholders.“ Therefore, we argue

that the new law enhances firm value by maintaining investor rights while allowing insiders

with a higher assessment of firm value to execute a takeover efficiently:38

H5: The Cayman Islands Company Law of 2009 increased the relative value of firms

incorporated in the Cayman Islands.

To test H5, we re-estimate our basic regression specifications over all treated and control

firms and include an event intercept dummy equal to one for firm-years in or after 2009 and

the difference-in-difference interactive equal to this event dummy times a dummy variable set

to one for firms incorporated in the Cayman Islands.

38 Our interpretation of this legal event is inspired by Lewis and Hall (2012), who cite several episodes of

Chinese firms taken private by managers willing to pay a premium above stock market value, perhaps with the

intention of eventually re-listing in Hong Kong or China.

43

Before continuing to empirical results, we consider whether the Dodd – Frank and

Cayman corporate law events are appropriate for use in a difference-in-difference study of

the impact of incorporation in an OFC. First, did firms self-select for treatment (that is,

incorporate in an OFC versus incorporate elsewhere) in anticipation of either event? We

selected firms with non-missing data and unchanged incorporation status for at least one year

before and after each of the two events. Second, were these legal changes intended to

manipulate the outcomes we are studying, that is, the value of OFC-incorporated firms

relative to other firms? The bulk of Dodd – Frank is directed at financial institutions, rather

than non-financial firms, in response to the recent credit crisis. The motivation for the 2009

revision of Cayman Islands corporate law is not transparent but was probably concerned with

creating more efficient procedures to attract foreign incorporations, perhaps to compete with

other jurisdictions. Thus, it seems reasonable to assume that these laws were intended to

serve broader purposes, rather than specifically targeting the valuation of offshore

incorporated firms. On the other hand, the passage of these laws clearly affects the benefits of

being incorporated in an OFC and, therefore, the value of OFC-incorporated firms.

Table 6 summarizes results of the difference-in-difference regression tests to explain

Tobin’s q. The slope coefficient on Event x OFC dummy (or Event x Cayman OFC dummy)

is the difference-in-difference measure of the change in Tobin’s q of treatment (OFC) firms

in response to the exogenous legal or regulatory event relative to the control (non OFC)

44

firms. The slope coefficient on Time x OFC dummy (or Time x Cayman OFC dummy)

estimates the importance of the difference between control and treatment groups in the five

years prior to the event. It is a test of the parallel trends assumption suggested by Roberts and

Whited (2012). The column towards the left centers on the Dodd –Frank Act of 2010 and

tests H4 with the sample of US address firms. The estimated slope coefficient on the term,

Event x OFC, is a statistically significant -0.1698 (t-statistic = -2.17). With the increase in

investor protection due to the Dodd - Frank Act, OFC-incorporated US address firms lost value

relative to US-incorporated US address firms. This is consistent with H4 and illustrates how

OFC incorporation can affect firm value. Furthermore, the lack of statistical significance

(t-statistic = -1.36) for the coefficient Time x OFC dummy in the parallel trends test suggests

that there are no pre-event differences between treatment and control groups.

The four columns on the right of Table 6 focus on the Cayman Islands company law event

of 2009 and test H5. We conduct tests over all firms and subsets of US, Chinese, and Hong

Kong firms. H5 predicts that the Cayman Islands company law increases the value of Cayman

incorporated firms relative to other firms. The “All Firms” regression yields strong evidence in

favor of H5. The slope coefficient on the Event x Cayman OFC dummy term is 0.3335

(t-statistic = 7.16) and is strongly statistically significant. However, the statistically significant

parallel trends test (the slope coefficient on Time x Cayman OFC dummy, -0.0667 with

45

t-statistic = -2.30) warns that the apparent effect of the 2009 event might be explained by

pre-existing differences in treated and control firm characteristics.

Across the country subsamples, the effect of the 2009 event on firms incorporated in the

Caymans is significantly negative for US address firms and strongly positive for Chinese and

Hong Kong address firms. Thus, the data appear consistent with H5 for all but US address

firms. This suggests that the improvements to Cayman Islands company law in 2009 were

valuable for firms from China and Hong Kong. For US firms already operating in the

high-quality US legal, regulatory, and disclosure environment, the effect of the law change is

negative (-0.4689, t-statistic = -2.18). However, the parallel trends test is statistically

significant for China (t-statistic = -3.66) and marginally significant for Hong Kong (t-statistic =

-1.75), which indicates some trend differences between the control and treatment groups may

have existed prior to the event and suggests some caution about the strength of this conclusion.

On balance, the exogenous Dodd – Frank Act and Cayman Islands Company Act events

further identify the differential effect on valuation of OFC versus non OFC incorporation. As

our model, testable hypotheses, and earlier empirical results suggest, differences in law,

regulation, and disclosure across jurisdictions drive patterns in valuation. Our

difference-in-difference tests validate this interpretation for US address firms and Dodd –

Frank and for US address firms and the change in Cayman Islands corporate law.

46

6. OFC incorporation, private lawsuits, and regulatory actions

Thus far, we have examined the effect of incorporation jurisdiction on firm value directly,

which aggregates a number of legal, regulatory, accounting, and governance channels. In this

section, we focus on the legal and regulatory channels reflected in lawsuits and regulatory

actions. In particular, we test whether OFC incorporated firms are more likely to experience

shareholder lawsuits, government lawsuits, or government regulatory actions. To do this, we

search for private class action lawsuits (http://securities.stanford.edu/filings.html) and

enforcement actions of the US Securities and Exchange Commission 39 for each sample firm

that is clearly subject to the US legal and regulatory environment (that is, US address, US

incorporation, or listing on a US securities market).

Figure 1 plots the frequency of shareholder class action lawsuits and SEC enforcement

actions. For each year, the percentage of firms that experience a private class action lawsuit or

SEC enforcement action is plotted separately for OFC incorporated firms versus other firms.

The plot begins with 1995 because there are only a handful of lawsuits and SEC actions prior to

that time. While the plot is very simple, it shows that there are periods when the frequency of

these events for OFC incorporated firms under US jurisdiction is double or triple the rate for

other firms under US jurisdiction.

39 We collect litigation, trading suspensions, administrative proceedings, administrative law decisions,

administrative law orders, opinions, and accounting and auditing enforcement releases from www.sec.gov.

47

Next, we relate individual firm records of lawsuits and SEC actions to OFC incorporation

with a cross-sectional probit regression:40

Legal/regulatory actioni = α0 + α1Dofc,i + β’xi + εi (10)

We include only those legal and regulatory events that occur within the five years following an

initial public offering.41 The fixed window following each initial public offering ensures that

the observational window under SEC supervision is clearly identified and comparable across

firms. Legal/regulatory actioni is our binary variable to identify if the ith firm has experienced a

private lawsuit or SEC enforcement action during the post IPO window. Dofc,i is the OFC

dummy variable and xi is a vector of several firm characteristics. There is one observation for

each firm in the sample. Our parameter of interest, α1, measures how OFC incorporation relates

to the likelihood of experiencing a legal or regulatory action within five years of initial listing

and monitoring by the SEC.

xi includes a dummy set to one for firms in high-technology industries, the firm’s industry

median q, and the firm’s percentage of insider holdings. These variables capture the potential

40 Lowry and Shu (2002) inspire our litigation and SEC action measure. However, their aim is to jointly

explain litigation risk and IPO returns, and they employ two equations to deal with the resulting simultaneity

problem. See also Lin, Pukthuanthong, and Walker (2013). In contrast, we are interested in associations between

experiencing litigation or SEC action and OFC incorporation. 41 Thus, the regressions include only firms that had an IPO within our sample period.

48

for conflict between management and shareholders or regulators. For example, industry

median q identifies growth firms, the high technology industry dummy indicates firms whose

future cash flows are difficult to estimate, and percentage of insiders indicates firms with

potentially more severe agency problems. We also include two characteristics of the firm’s

address country, the anti-director rights index and disclosure quality, because home country

conditions can affect the likelihood of litigation or SEC enforcement in the US. For example,

even though a foreign firm is listed in the US, its disclosure can be of lower quality than

comparable US firms (Lang, Raedy, and Wilson, 2006). Furthermore, Cheng, Srinivasan, and

Yu (2013) describe greater “transactions costs” associated with a lawsuit or regulatory action

against a non US firm such as gathering information and evidence, deposing corporate

managers, and pursuing remedies in a firm’s distant and less developed home country.

Table 7 presents the results of these regression estimates. In specification (1), the

legal/regulatory action dummy is regressed on the OFC dummy and a constant. The slope

coefficient on OFC dummy is positive and statistically significant, indicating that firms

incorporated in an OFC are more likely to experience shareholder lawsuits and SEC actions

than other firms. In particular, the constant from specification (1), -1.4681, suggests that the

unconditional probability that a firm experiences a lawsuit or SEC action is about 7%. The

constant, -1.4681, plus the slope on OFC dummy, 0.5527, times one to indicate an OFC

incorporated firm suggests the probability an OFC incorporated firm experiences a lawsuit or

49

SEC action is about 18%, that is, over two-and-a-half times higher than a comparable non OFC

firm. Specifications (2) through (5) also yield a positive slope for OFC dummy that is

statistically significant or, in one case, marginally statistically significant. Thus, the increased

likelihood of a lawsuit or SEC action for an OFC incorporated firm is not subsumed by

technology firms, high priced growth firms, high fraction of insider ownership, or origins in

countries with relatively weak protection of minority investors.

However, in specifications that include the firm’s address country Disclosure Quality, (6)

and (7), the significance of the slope on OFC dummy vanishes. This suggests that the apparent

significance of OFC incorporation is subsumed by the quality of the home country’s disclosure

environment. The marginally significantly positive slope on disclosure quality echoes the

finding (Cheng, Srinivasan, and Yu, 2013) that a less-developed home country environment

can inhibit legal and regulatory action against US listed foreign firms.42

Finally, given the suspicion that it is actually the nature of the underlying home country

that influences lawsuits and SEC actions, we present one final specification, (8), in Table 7.

The legal/regulatory action dummy is regressed on the OFC dummy and a dummy for firms

with address in China. The estimated slope on the China dummy is significant and positive

while the slope on the OFC dummy is insignificant. On balance, the findings on OFC

incorporation and the likelihood of a lawsuit or SEC action echo what we report for Tobin’s q.43

42 A related idea is that disclosure can enable or deter litigation. See Field, Lowry, and Shu (2005). 43 We do not re-use the earlier difference-in-difference regressions because our two events occur after 2008 and,

50

7. OFC incorporation and US institutional investor holdings

Our prior tests concentrate on an observed measure of corporate value, Tobin’s q, and its

relation to incorporation in an offshore financial center. We can think of the stock market value

reflected in Tobin’s q as the consensus judgment of stock market participants on OFC

incorporation and other firm characteristics. For a different angle on how investors assess

OFC incorporation, we examine the difference in the attractiveness of OFC and non OFC

incorporated firms to institutional investors. If professional investors value good corporate

governance (Leuz, Lins, and Warnock, 2008), then institutional ownership in OFC versus non

OFC firms should differ if OFC incorporation has material implications for governance.44

Moreover, the difference in holdings can vary across institutional investor type, depending on

their loyalty to management and sensitivity to corporate actions.

We augment our existing data series with annual observations of institutional holdings for

stocks around the world developed by Ferreira and Matos (2008) and made available through

thus, a five year post IPO window of lawsuit data is not possible because our sample end in 2013. 44 See, for example, Cohen, Gompers, and Vuolteenaho (2002) and Nagel (2005) for evidence on the relative

skills of institutional investors. Furthermore, mutual funds that trade frequently (Chen, Jegadeesh, and Wermers,

2000), have high turnover (Wermers, 2000), or score highly on implementing stock selection (Cremers and

Petajisto, 2009) typically display at least marginally better performance. Yan and Zhang (2009) find that the

trades of institutions which they classify as short term predict future stock returns. On the other hand, broad

examinations of the mutual fund industry such as Gruber (1996) and French (2008) report average

underperformance in excess of 60 basis points per year.

51

Wharton Data Research Services (WRDS).45 The underlying source is FactSet Lionshare,

which combines information on US institutional investors from SEC filings (13F and N-30D)

and non-US institutional ownership data from national regulatory agencies, mutual fund

industry directories, company reports, and other sources. The data covers 5,337 institutions and

over 35,000 equity securities worldwide.46 We study both the fraction of a particular firm’s

shares held by institutions and the number of institutions that hold a particular firm’s shares.

For certain tests, we follow the categorizations of Ferreira and Matos (2008): “independent”

institutions are mutual fund managers and investment advisors while “grey” institutions are

bank trusts, insurance companies, pension funds, endowments, and other institutions that are

more likely to be loyal to corporate managers of firms they hold and, therefore, less responsive

to managerial actions that are potentially detrimental to shareholders.

Table 8 presents estimates of pooled regressions along the lines of equation (9) and our

previously described regressions (Panel A of Table 3) to explain Tobin’s q. Panel A presents

results for all types of institutions broken down by the address country of the listed firm. For

the percent of equity held by institutions, there are some intuitive findings among the control

variables: firms from address countries with high quality investor protection (anti-director

rights), China and Hong Kong firms with a US cross listing, and firms with relatively low

insider ownership attract relatively high institutional ownership. Furthermore, adjusted

45 The file, “WRDS_HOLDINGS_BY_FIRM_MSCI”, is located in the “FactSet” category of WRDS. 46 See Ferreria and Matos (2008) for further detail on the data source.

52

r-squared is 64% for one specification with all firms, 25% for China address firms, and 17% for

Hong Kong address firms. The slope coefficient of interest, on OFC dummy, is significantly

positive for China and Hong Kong address firms, but switches signs for “All countries”

depending on the specification. In particular, in specification (2), with Judicial Efficiency and

Accounting Standards from La Porta et al (1998) (missing for China and Russia and, thus,

excluding those countries), the slope coefficient on OFC dummy is negative and significant.

This suggests that OFC-incorporated firms have a lower percentage of shareholders that are

institutional investors, which echoes our findings on the valuation discount for

OFC-incorporated firms. However, this result reverses when Evict and Disclosure Quality

(which have non-missing values for China and Russia, and, thus, allow us to include them) are

used as country characteristics. This suggests that the institutional investors override their

aversion to OFC-incorporated firms from a few countries, which echoes our earlier findings for

valuation, and is consistent with the significantly positive slopes on the OFC dummy in

specifications (3) and (4) for China only and Hong Kong only.

Specifications (5) through (8) seek to explain the number of institutions that hold shares

of each OFC firm, rather than the percentage holding. Specification (6), for “All countries” but

excluding China and Russia (because of the Judicial Efficiency and Accounting Standards

variables), has a significantly negative slope, -90.90 (t-statistic = -11.22) on OFC dummy.

This indicates that, on average, ninety fewer institutions invest in a typical OFC-incorporated

53

firm, and is consistent with an aversion to these firms. The slope coefficients on OFC dummy

are insignificant in the other specifications.

Panel B of Table 8 separately examines investor institution types using “independent”

and “grey” categories following Ferreira and Matos (2008). 47 As was the case for percent

institutional holding studied in Panel A, the slope on the OFC dummy switches sign depending

on the specification of control variables. It appears that institutional investors, particularly

those that are “Independent”, typically hold relatively less of the shares of OFC-incorporated

firms.48

For a final look at institutional investor holdings of OFC incorporated firms, we conduct

difference-in-difference analysis around the same Dodd – Frank and Cayman Islands corporate

law events studied for Tobin’s q in Table 6. Table 9 summarizes regressions that explain the

percentage of equity held by institutions and the number of institutional investors. The critical

slope coefficients are for Event x OFC dummy for the Dodd – Frank 2010 event and Event x

Cayman OFC dummy for the Cayman Islands corporate law 2009 event. None of the

coefficients for percent institutional holdings are statistically significant, indicating that these

events had no impact on holdings of OFC versus other firms. For number of institutional

47 The number of institutional holders by “independent” or “grey” type is not available in the Ferriera and

Matos (2008) data. 48 We also estimated the matched estimator for institutional holdings following the same format as the matched

estimator for Tobin’s q in Table 5. The results are broadly similar to those reported in Table 8 and are available

in the appendix..

54

investors, there is only one case, “US address firms” for the 2009 Cayman Islands event, for

which the slope on Event x Cayman OFC dummy is statistically significant. It is negative,

suggesting that the new Caymans law repelled some institutional investors from

Cayman-incorporated firms located in the US. However, this finding is associated with a

statistically significant parallel trends test, which indicates that, on average, the OFC and

control groups had different trends in the pre-event sample.

On balance, the relationship between OFC incorporation and global institutional investor

holdings is not as clear as that for OFC incorporation and valuation. However, reminiscent of

the results for firm value, we find that OFC incorporated firms tend to have a smaller fraction

of shares owned by a smaller number of institutional investors, except for special cases like

firms from China or Hong Kong.

8. Summary and conclusions

This paper examines a rarely-explored dimension of the choices corporations make

concerning their legal, regulatory, and disclosure environment. Offshore financial centers

present both the hope of a more efficient, low cost legal home and the fear of a poorly-regulated

environment that benefits managers and other insiders at the expense of ordinary shareholders.

A variety of specifications to explain Tobin’s q show that, typically, incorporation in an

offshore financial center reduces value by five to fifteen percent. This destruction of firm value

55

is larger for firms from high investor protection countries and for growth firms. We confirm

this finding with difference-in-difference tests: a tightening of in US investor protection

increases the value of US firms more than their OFC incorporated counterparts, while an easing

of restrictions on control decisions by majority shareholders of Cayman domiciled firms

further decreases the value of US firms incorporated in the Cayman Islands. Furthermore, the

probit regressions in our Heckit estimates indicate that OFC incorporation is more likely to be

selected by a company from a high investor protection country, suggesting that, indeed, some

“naughty” companies seek to evade a high quality environment in order to exploit minority

shareholders. However, by examining subsets of firms identified by home country, this value

destruction is not universal. In particular, we find that local firms from China appear to add

value by incorporation in an OFC. Thus, it seems there is nothing inherently naughty or nice

about the use of OFCs as legal domiciles by corporations, but rather the effect of OFC

incorporation depends on the characteristics of the firm, its home country, and the OFC where

it is incorporated.

Our findings offer new support for the idea that law and regulation are significant to

firm value (La Porta, Lopez-de-Silanes, Shleifer, and Vishny, 2002) and, in particular, affect

firm value across international borders and across time as laws and regulations change. We also

offer new insights for the global policy debate about the contributions or costs of offshore

financial centers to capital markets, shareholders, and governments. Do they represent healthy

56

competition or do they merely enable exploitation of minority shareholders? The issue is not

exclusively related to tax avoidance but also has governance implications.

57

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63

Figure 1. The frequency of shareholder class action lawsuits and SEC actions against OFC incorporated firms versus other firms For each year, the percentage of firms that experience a private class action lawsuit or SEC enforcement action is plotted separately for OFC incorporated firms

versus other firms. The plot begins with 1995 because there are only a handful of such events prior to that time.

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

4.5%

Other OFC

64

Table 1. The distribution of country of incorporation, country of address, and exchange listing for sample firms

This table summarizes the country of incorporation and country of address of sample firms. Panel A tabulates sample firms without double-counting cross listings. A

supplement to Table 1 Panel A (available upon request) details the distribution of sample firms for countries of incorporation or countries of average classified as “Other” in

Panel A. Sample firms are incorporated in an OFC but with address not in an OFC as identified from Datastream, www.adr.com, Worldscope, or CompustatNA.

Panel A: Unique Firms

Country of Incorporation

Address

Country Bermuda

Cayman

Islands

British

Virgin

Islands Jersey

Marshall

Islands

Isle of

Man Guernsey Cyprus

Netherland

Antilles Bahamas Panama Others Total

Hong Kong 381 251 5 0 1 0 0 0 0 0 0 0 638

China 88 211 17 1 0 1 0 0 0 0 0 1 319

UK 11 4 18 27 0 27 12 4 0 0 0 6 109

US 20 8 2 0 11 0 0 0 1 3 3 4 52

Singapore 41 2 0 0 0 0 0 0 1 0 0 0 44

Canada 4 6 6 2 0 0 0 0 0 1 0 1 20

Greece 1 0 0 1 17 0 0 0 0 0 0 0 19

Norway 9 2 0 0 0 0 0 4 0 0 0 0 15

Australia 7 0 1 1 0 0 0 0 0 0 0 0 9

Others 16 9 5 4 3 3 3 2 4 2 3 7 61

Total 578 493 54 36 32 31 15 10 6 6 6 19 1286

65

Table 1 continued.

Panel B: Listings on stock exchanges   

Country of Incorporation

Exchange Address Country Bermuda

Cayman

Islands

British Virgin

Islands Other Total

NYSE Hong Kong 2 0 1 1 4

China 0 16 0 0 16

UK 0 0 0 1 1

US 11 3 0 10 24

Other 6 0 0 10 16

NASDAQ Hong Kong 2 1 1 0 4

China 0 31 11 1 43

UK 2 0 0 0 2

US 4 2 0 5 11

Other 1 1 1 10 13

OTC Hong Kong 1 2 1 0 4

China 1 7 2 0 10

UK 0 0 1 1 2

US 4 1 2 2 11

Other 2 0 1 3 6

London Hong Kong 0 1 1 0 2

China 0 3 1 1 5

UK 9 2 14 58 86

US 0 1 0 0 1

Other 4 1 3 9 17

Hong Kong Hong Kong 277 191 0 0 468

China 48 128 0 0 176

UK 0 0 0 0 0

US 0 0 0 0 0

Other 3 1 0 0 4

Singapore Hong Kong 14 0 0 0 14

China 18 8 1 0 27

UK 0 0 0 0 0

US 0 0 0 0 0

Other 35 1 0 2 38

66

Table 2. Summary statistics on individual company characteristics

Sample firms are incorporated in an OFC but with address not in an OFC as identified from Datastream, www.adr.com, Worldscope, or CompustatNA. Their

financials are collected from Datastream and CompustatNA. Control firms have Datastream country of address in one of the countries of address in the

OFC-incorporated sample but are not incorporated in an OFC. Both sets of firms are screened to exclude firms with Total Assets less than 10 million US dollars or

no data on Total Assets, Sales, Long Term Debt, Total Debt, Shareholder’s Equity, or Market Value of Equity. In Panel B, assets and sales are logs, insider holdings

and sales growth are percentages, and capex is scaled by lagged assets. We remove resulting extreme observations of Tobin’s q by dropping the top and bottom 1%,

Each variable in the table is averaged across firm-years by firm, and the table in turn reports averages across firms. *, **, and *** indicate difference is statistically

significant at 10%, 5%, or 1% level respectively.

Panel A: Tobin’s q

Country of Incorporation

Control Bermuda Cayman Islands British Virgin Islands Other

Mean

Tobin's

q

Firms

Mean

Tobin's

q

Firms

Difference

with

Control

Mean

Tobin's

q

Firms

Difference

with

Control

Mean

Tobin's

q

Firms

Difference

with

Control

Mean

Tobin's

q

Firms

Difference

with

Control

Hong Kong 1.0302 94 1.1036 337 0.0735 1.5108 214 0.4807 *** 0.60849 4 -0.4217 ** 0.70677 1 -0.3234

China 0.7731 120 1.1363 84 0.3632 *** 1.5501 203 0.7770 *** 0.63994 15 -0.1332 * 0.65589 1 -0.1172

UK 1.3999 887 1.4027 4 0.0028 1.4682 3 0.0683 1.07993 7 -0.3199 1.80707 42 0.4072 ***

US 1.5126 2655 1.3180 10 -0.1946 1.1552 7 -0.3575 1.26553 2 -0.2471 1.36104 18 -0.1516

Singapore 1.1850 381 1.1370 32 -0.0480 1.2150 2 0.0301 3.09671 1 1.9117 ***

Canada 1.2241 386 1.1626 2 -0.0615 0.9690 4 -0.2551 2.11308 5 0.8890 *** 3.17941 1 1.9553 ***

Norway 1.2164 71 1.7804 9 0.5640 ** 1.2274 2 0.0111 1.21026 4 -0.0061

Netherlands 1.4584 147 2.7222 1 1.2638 * 1.47620 3 0.0178

Greece 1.1803 127 1.1085 1 -0.0718 1.10330 18 -0.0770

Other 1.2828 7625 1.3955 18 0.1127 1.0797 8 -0.2031 0.67341 5 -0.6094 ** 1.02952 22 -0.2533 **

67

Table 2 continued.

Panel B: Other firm characteristics

Country of Incorporation

Control Bermuda Cayman Islands British Virgin Islands Other

Mean

Median

Firm-

Years

Mean

Median

Firm-

Years

Difference

with

Control

Mean

Median

Firm-

Years

Difference

with

Control

Mean

Median

Firm-

Years

Difference

with

Control

Mean

Median

Firm-

Years

Difference

with

Control

Sales 13.030 180911 11.896 5645 -1.134 12.119 2848 -0.911 11.231 179 -1.799 12.654 943 -0.376

12.868 11.971 12.112 11.359 12.724

Assets 13.223 181823 12.710 5799 -0.513 *** 13.003 3605 -0.220 *** 12.312 234 -0.911 *** 13.822 1091 0.599 ***

12.942 12.443 12.801 11.956 13.703

Capex 0.465 161436 0.067 5389 -0.397 *** 0.066 3316 -0.398 *** 0.103 210 -0.362 *** 0.705 973 0.241 ***

0.042 0.028 0.035 0.037 0.046

Insider

Holding 0.351 139585 0.531 5424 0.882 0.539 3371 0.188 0.402 167 0.051 0.323 897 -0.028

0.323 0.578 0.562 0.272 0.242

Sales Growth 0.843 171994 1.459 5315 0.616 *** 0.393 2612 -0.451 *** 1.481 161 0.637 ** 0.520 864 -0.324 ***

0.045 0.061 0.129 0.061 0.073

Debt/ Equity 0.806 182435 0.826 5426 0.020 0.289 3331 -0.516 ** 0.311 224 -0.495 0.864 1072 0.058

0.251 0.079 0.036 0.001 0.366

68

Table 3. Regressions to explain individual firm Tobin’s q

This table reports regression estimates for specifications with Tobin’s q as dependent variable. Firm characteristics include a

dummy set to 1 for firms incorporated in an OFC and a dummy indicating a Level II or III cross listing on a US stock

exchange, percent of equity held by insiders, total employment costs, capital expenditure, and sales growth. Characteristics

of the corporate address country include Anti-Director Rights index of La Porta et al (1998) with values inserted for China

and Russia (Allen, Qian, and Qian, 2005; Pistor, Raiser, and Gelfer, 2000), legal system efficiency reflected in days to evict

a tenant from Djankov et al (2003), World Bank’s disclosure quality index, and home country corporate income tax rate for

2006. Original Judicial Efficiency and Accounting Standards, with no values for China, from La Porta et al (1998), are also

are included in some specifications. In Panel A, all specifications include year fixed effects, and standard errors are clustered

by firm. All specifications use sales growth and capex winsorized at the 1% and 99% levels, except for (4a) which uses

unwinsorized sales growth and capex for comparison. T-statistics are reported and observations are firm-years. In Panel B,

all coefficients are averages of yearly cross-sectional regressions, and t-statistics use Fama - MacBeth standard errors. Panel

C reports a variation on the regressions of Panel A including interactive terms.

69

Table 3. Regressions to explain individual firm Tobin’s q (continued)

Panel A: Pooled cross sectional time series regressions

All Countries China Hong Kong

(1) (2) (3) (4) (4a) (5) (6) (7)

OFC dummy -0.050 -0.081 -0.155 -0.078 -0.065 -0.012 0.491 0.063

-1.88 -3.58 -6.23 -3.00 -2.44 -0.42 7.67 1.59

DR dummy 0.163 0.169 0.142 0.156 0.183 0.137 0.239

5.90 5.71 4.69 5.06 4.54 0.97 2.42

Anti-Director Rights 0.026 0.022 0.011 0.000 0.003

4.83 2.89 1.40 0.01 0.30

Evict -0.000 0.000 0.000 0.000

-5.66 -5.57 -7.35 -2.54

Disclosure quality -0.014 0.002 0.000 -0.005

-4.31 -0.48 -0.04 -0.98

Sales growth 0.228 0.231 0.185 0.000 0.149 0.117 0.070

-29.66 29.21 21.43 -0.75 15.09 3.54 3.37

Industry Median q 1.020 1.035 1.025 1.059 1.015 0.654 0.947

36.54 36.77 29.66 29.80 14.39 3.61 7.32

Percent Insider -0.184 -0.193 -0.228 -0.068 -0.356

-7.78 -8.07 -7.43 -0.79 -2.40

Capex/Assets 1.118 0.003 1.063 1.170 1.571

16.13 1.45 11.58 4.43 6.40

Address Tax Rate 0.030 -0.037 0.416

0.29 -0.36 2.93

Judicial Efficiency -0.003

-0.60

Accounting Standards 0.002

2.19

Total Employee Costs/Assets 0.009

1.04

Constant 1.317 0.095 -0.170 0.048 0.198 -0.245 -0.559 0.459

247.44 2.14 -2.21 0.71 2.85 -2.04 -2.14 0.91

Observations 194602 180190 175235 125397 125397 50964 3808 6635

Adjusted r-squared 0.00 0.18 0.18 0.20 0.18 0.17 0.23 0.16

70

Table 3. Regressions to explain individual firm Tobin’s q (continued)

Panel B: Fama - MacBeth yearly regressions

All Countries China Hong Kong

(1) (2) (3) (4) (4a) (5) (6) (7)

OFC dummy -0.068 -0.152 -0.193 -0.140 -0.135 0.035 0.491 -0.414

-1.94 -7.01 -10.59 -5.93 -5.47 1.23 7.07 -1.92

DR dummy 0.097 0.134 0.101 0.108 0.103 -0.045 -0.070

3.75 5.81 4.62 4.78 1.98 -0.38 -0.4

Anti-Director Rights 0.046 0.040 0.054 0.048 0.004

4.5 4.85 4.52 4.02 0.36

Evict 0.000 0.000 0.000 0.001

0.55 0.88 0.53 0.78

Disclosure quality -0.013 0.001 0.002 -0.014 0.074 0.132

-3.11 0.29 0.66 -0.28 2.09 1.14

Sales growth 0.268 0.283 0.196 0.046 0.042 0.431 1.172

11.96 13.21 11.22 2.38 0.44 2.73 7.84

Industry Median q 0.985 1.026 0.981 1.001 1.446

39.09 42.63 45.17 44.17 2.68

Percent Insider -0.075 -0.082 -0.129 -0.050 -0.586

-2.57 -2.76 -0.37 -1.23 -2.64

Capex/Assets 1.245 0.664 -1.798 0.955 1.741

16.78 8.69 -0.71 4.43 6.01

Address Tax Rate 0.324 0.285 0.022

2.55 2.22 0.04

Judicial Efficiency -0.003

-0.44

Accounting Standards -0.006

-1.5

Total Employee Costs/Assets 1.001

0.94

Constant 1.344 0.061 0.376 -0.233 -0.166 -0.246 0.239 0.318

55.45 0.94 1.61 -1.99 -1.39 -0.42 1.67 0.95

Observations 194602 180190 175235 125397 125397 51030 3808 6635

Adjusted r-squared 0.00 0.18 0.17 0.21 0.20 0.19 0.31 0.27

Number of years 34 33 33 33 33 22 22 33

71

Table 3. Regressions to explain individual firm Tobin’s q (continued)

Panel C: Regressions with interactive terms

All Countries China Hong Kong

(1) (2) (3) (4) (5) (6)

DR dummy x OFC dummy -0.0954 -0.0944 -0.1229 -0.1151 -0.4012

-0.97 -0.62 -1.13 -0.96 -2.10

Anti-Director Rights x OFC dummy -0.0973 -0.0442 -0.0738 -0.0983

-4.07 -1.06 -2.78 -3.14

Evict x OFC dummy -0.0004 0.0003 0.0004

-0.99 0.66 0.93

Disclosure quality x OFC dummy 0.0169 0.0414 0.0669

1.21 2.61 3.25

Sales growth x OFC dummy -0.0632 -0.0973 -0.0719 0.0024 0.0711 0.0937

-2.90 -4.24 -3.16 0.09 1.06 2.26

Industry Median q x OFC dummy 0.0239 -0.0200 -0.0732 0.0442 1.2081 -0.2373

0.24 -0.20 -0.65 0.33 4.53 -1.06

Percent Insider x OFC dummy -0.0373 -0.0698 -0.0641 -0.0783

-0.45 -0.76 -0.39 -0.23

Capex/Assets x OFC dummy 0.2696 0.2642 1.9777 0.2319

1.25 1.02 4.11 0.38

Address Tax Rate x OFC dummy 0.0522

0.74

Judicial Efficiency x OFC dummy 0.0037

0.50

Accounting Standards x OFC dummy 1.1003 0.1175

2.60 0.23

Total Employee Costs/Assets x OFC dummy 0.2238

1.98

OFC dummy 0.2507 -0.6749 -0.3689 -0.3998 -0.8775 0.3594

1.42 -1.15 -1.31 -1.16 -3.25 1.09

DR dummy 0.1659 0.1751 0.1418 0.1885 0.0765 0.3174

5.77 5.76 4.49 4.35 0.52 2.72

Anti-Director Rights 0.0309 0.0231 0.0239 0.0167

5.61 2.96 2.87 1.50

Evict -0.0002 -0.0003 -0.0002

-4.10 -4.49 -2.07

Disclosure quality -0.0148 -0.0066 -0.0095

72

-4.51 -1.56 -1.93

Sales growth 0.2355 0.2402 0.1945 0.1458 0.0651 0.0001

28.57 28.58 20.59 13.62 1.19 0.00

Industry Median q 1.0173 1.0352 1.0294 1.0092 -0.2614 1.1337

35.75 36.14 29.02 13.28 -2.17 6.00

Percent Insider -0.1803 -0.2166 0.0026 -0.2756

-7.29 -6.65 0.02 -0.93

Capex/Assets 1.0917 1.0184 0.0545 1.4259

14.89 10.35 0.30 2.60

Address Tax Rate -0.1401 0.3388

-1.24 2.17

Judicial Efficiency -0.0031

-0.68

Accounting Standards 0.0022

1.99

Total Employee Costs/Assets 0.0080

1.02

Constant 0.0801 -0.1586 0.0753 0.2314 0.8471 0.2234

1.76 -2.03 1.09 0.70 4.83 0.40

Observations 180190 175235 125397 51030 3808 6635

Adjusted r-squared 0.1803 0.1812 0.2047 0.1696 0.26403 0.1653

73

Table 4. Heckman selection bias correction for regressions to explain Tobin’s q

The table reports averages of yearly Heckman regressions including the inverse Mill’s ratio predicted probability of

selecting into an OFC. See previous tables for definitions of other variables. Specifications 1 and 2 use full1980 to 2013

sample. Specification 3 starts in 1991 due to no earlier data for total employee costs. Regressions include firm-years of all

countries. Sales growth and capex are winsorized at the 1% and 99% levels.

Probit (1) Heckit (1) Probit (2) Heckit (2) Probit (3) Heckit (3)

OFC dummy -0.1401 -0.1352 0.0225

-6.52 -5.80 0.64

Inverse Mills Ratio -0.0082 0.0086 0.0208

-1.62 0.59 2.08

DR dummy 0.0954 0.0875 0.2029

32.76 4.21 7.20

Anti-Director Rights 0.4787 0.4913 0.3956

10.50 10.64 8.76

Evict -0.0006 0.0003 0.0013

-2.71 0.86 5.53

Disclosure quality -0.0079 0.0010 0.0778

-0.28 0.03 2.69

Address Tax Rate -11.0342 -15.7021 -17.4457

-25.18 -10.64 -19.08

GNP (log) 0.3155 0.4479

7.28 12.87

Sales (log) 0.0148 -0.0292

0.90 -2.30

Percent Insider -0.1727 -0.1816 -0.2580

-6.02 -6.99 -14.83

Sales growth 0.2680 0.2061 0.1681

4.18 9.47 5.89

Industry Median q 1.0411 1.0148 0.8364

5.82 49.61 12.23

Capex/Assets 1.1748 0.9124

15.09 7.72

Total Employee Costs/Assets 0.3509

7.37

Constant -0.5748 0.1817 -1.9503 0.0917 -1.8992 0.2643

-6.52 7.32 -16.41 1.90 -12.63 4.69

Average Yearly Observations 5778 4136 5655 3797 6851 2214

Average Yearly Pseudo r-squared 0.37 0.40 0.46

Average Yearly r-squared 0.17 0.19 0.18

74

Table 5. Matched estimator selection bias correction for Tobin’s q

This table compares Tobin’s q of OFC incorporated firms and matched non OFC firms. The first panel summarizes the matching process with probit regressionsfor

both unmatched raw and matched samples. Matching starts with the propensity score from the pre-matched probit regression on the entire sample with fixed effects;

matches are the closest propensity score match drawn from non OFC firms of the same address country, industry, and year. Outliers of sales growth and capex which

are winsorized in earlier tests are trimmed for these tests given the small buckets from which matches are drawn. The second panel summarizes Tobin’s q for OFC

incorporated firms and two sets of matched non-OFC firms. Matching is done with “nearest neighbors” equal to one. z-statistic is reported beneath each slope

coefficient estimate. Employment cost variables excluded due to missing observations. “ATT” denotes average treatment effect of the treated.

Panel A: Probit regression with OFC dummy as dependent variable

All Countries China Hong Kong All except China and Hong Kong

Unmatched Matched Unmatched Matched Unmatched Matched Unmatched Matched

Sales growth 0.236 0.0994 0.185 0.0212 0.124 0.105 0.306 0.274

5.473 1.315 1.731 0.142 1.663 0.929 5.846 2.807

Sales (log) 0.0726 0.0666 -0.0987 -0.0145 0.177 0.0957 0.0185 0.0235

3.006 1.76 -1.682 -0.175 3.079 1.574 0.544 0.48

Capex/Assets 1.019 1.128 1.791 2.108 2.472 1.726 0.602 -0.405

2.576 1.672 2.131 1.778 2.842 1.757 1.139 -0.528

Percent Insider 0.95 0.873 4.24 2.087 -0.35 0.36 0.00225 -0.218

6.37 2.639 8.021 3.195 -0.61 0.523 0.00989 -0.588

Constant -2.723 -1.473 0.733 -0.65 -1.415 -1.819 -1.648 -0.248

-3.803 -1.054 0.806 -0.533 -1.222 -1.497 -2.108 -0.179

Observations 67,671 7,521 3,091 1,735 4,465 3,869 55,146 1,766

Pseudo r-squared 0.629 0.0152 0.534 0.0656 0.176 0.0128 0.232 0.00462

75

Table 5. Matched estimator selection bias correction for Tobin’s q (continued)

Panel B: Average Tobin's q

All Countries China Hong Kong All except China and Hong Kong

Untreated ATT Untreated ATT Untreated ATT Untreated ATT

OFC 1.152 1.152 1.34 1.34 1.037 1.037 1.279 1.279

Non-OFC 1.216 0.939 0.76 0.83 0.830 0.876 1.358 1.366

Difference -0.064 0.213 0.57 0.50 0.207 0.161 -0.079 -0.088

t-statistic -5.000 7.360 22.25 16.59 8.830 7.040 -2.90 -2.74

OFC observations 5,534 5,534 1,373 1,373 3,204 3,204 943 943

Non OFC observations 9,593 1,987 1,461 1,003 921 893 7,211 3,003

76

Table 6. Summary of difference-in-difference regressions to explain Tobin’s q

This table presents regressions to measure the impact of changes in law on valuation of OFC versus non OFC incorporated firms. Specification 4 from Panel A of

Table 3 is augmented with an event intercept dummy equal to one for firm-years in or after the event year and the difference-in-difference interactive equal to this

event dummy times the OFC dummy. An additional specification tests the parallel trends assumption (Roberts and Whited, 2012). We do not report the slopes on

control variables. The Dodd – Frank Act (2010) is studied with US address firms listed on the NYSE, NYSE MKT, NASDAQ, or NASDAQ SmallCap markets. The

Total Employee Cost variable is excluded because it generates large numbers of missing observations. To study the Cayman Islands Company Act (2009), “Cayman

OFC dummy” is a dummy variable equal to one if the firm is incorporated in the Cayman Islands.

Dodd – Frank Act of 2010 Cayman Islands Company Act of 2009

US address firms All firms US address firms China address firms Hong Kong address firms

Event x OFC dummy -0.1698

-2.17

Event x Cayman OFC dummy 0.3335 -0.4689 0.2899 0.3512

7.61 -2.18 4.11 5.15

Cayman OFC dummy 0.0657 -0.1443 0.1801 -0.0302

1.47 -1.27 2.12 -0.52

OFC dummy -0.0857 -0.1155 -0.0935 0.2662 0.0073

-0.58 -3.40 -0.80 2.92 0.13

Event dummy 0.0511 0.1141 0.1242 0.1169 0.1075

1.80 10.17 4.80 1.85 2.09

Parallel trends test (Time x OFC dummy) -0.1067

-1.36

Parallel trends test (Time x Cayman OFC dummy) -0.0667 0.0186 -0.2311 -0.0551

-2.30 0.57 -3.66 -1.75

Observations 1480 15204 1608 971 1167

Adjusted r-squared 0.0729 0.1730 0.1349 0.2233 0.1687

77

Table 7. Regressions to explain the incidence of shareholder lawsuits and SEC enforcement actions

This table reports cross-sectional probit regressions to predict the probability that OFC incorporated firms experience legal

or regulatory problems. The dependent variable is an indicator variable that is set to one if a firm has experienced a private

shareholder lawsuit or SEC enforcement action during the five year period following its initial public offering. Given the

five year observational period, we include all firms under the supervision of SEC (US-related) from our primary sample that

went public between January 1980 and December 2008. Control variables are defined in earlier tables. Country

characteristics are included because many firms have non US address: Australia (5), Bermuda (3), Brazil, Canada (5), Chile

(2) China (31), Germany (3), France (2), Greece (9), Hong Kong (2), Ireland, Japan (3), Mexico, Netherlands (2), Norway

(6), Russia, Taiwan (4), South Africa (3), and UK. Tech dummy is set to one if a firm is classified as Biotechnology,

Internet, Computer Hardware or Services, Software, Semiconductors, Mobile Telecom, or Medical Equipment. z-statistic is

reported beneath each coefficient estimate.

(1) (2) (3) (4) (5) (6) (7) (8)

OFC dummy 0.5527 0.5976 0.5516 0.6756 0.6112 0.3817 0.4644 -0.2635

2.21 2.35 2.20 2.25 1.90 1.23 1.10 -0.55

Tech dummy 0.5210 0.5167

2.00 1.53

Industry Median q -0.0161 -0.1210

-0.07 -0.38

Percent Insider -0.4080 -0.6075

-0.78 -1.02

Anti-Director Rights 0.0002 -0.0993

0.00 -0.61

Disclosure quality 0.1432 0.1668

1.62 1.76

China address dummy 1.2292

2.34

Constant -1.4681 -1.6188 -1.4453 -1.2884 -1.4694 -2.4671 -1.9321 -1.4681

-10.5 -9.73 -3.86 -6.97 -2.31 -3.87 -1.85 -10.5

Observations 233 233 233 170 229 230 166 233

Pseudo r-squared 0.0326 0.0592 0.0326 0.0415 0.0387 0.0621 0.1107 0.0787

78

Table 8. Regressions to explain individual firm institutional ownership

This table reports regression estimates to explain measures of institutional ownership. Firm characteristics include a dummy

set to 1 for firms incorporated in an OFC and a dummy indicating a Level II or III cross listing on a US stock exchange,

percent of equity held by insiders, total employment costs, capital expenditure, and sales growth. Characteristics of the

corporate address country include Anti-Director Rights index of La Porta et al (1998) with values inserted for China and

Russia (Allen, Qian, and Qian, 2005; Pistor, Raiser, and Gelfer, 2000), legal system efficiency reflected in days to evict a

tenant from Djankov et al (2003), World Bank’s disclosure quality index, and home country corporate income tax rate for

2006. Original Judicial Efficiency and Accounting Standards, with no values for China and Russia, from La Porta et al

(1998), are also are included in some specifications. In Panel A, all specifications include year fixed effects, and standard

errors are clustered by firm. T-statistics are reported and observations are firm-years. In Panel B, all coefficients are

averages of yearly cross-sectional regressions, and t-statistics use Fama - MacBeth standard errors. Following Ferreira and

Matos (2008), “independent” institutions are mutual fund managers and investment advisors while “grey” institutions such

as bank trusts and insurance companies are more likely to be loyal to corporate managers of firms they invest in. The time

span of results is constrained by the FactSet/LionShares database which commences with 1999.

79

Table 8. Regressions to explain individual firm institutional ownership (continued)

Panel A: Pooled cross sectional time series regressions by individual firm’s address country

Percent of equity held by institutions Number of institutional investors

All countries China Hong Kong All countries China Hong Kong

(1) (2) (3) (4) (5) (6) (7) (8)

OFC dummy 0.0379 -0.1901 0.0381 0.0175 8.84 -90.90 15.43 -14.86

5.56 -15.48 2.73 2.25 1.24 -11.22 1.52 -1.64

DR dummy 0.0187 0.0199 0.1799 0.0598 125.70 141.99 23.23 97.10

1.52 1.38 4.86 3.83 8.09 8.63 2.19 4.77

Anti-Director Rights 0.0494 0.1630 24.70 60.92

13.49 22.88 8.52 13.24

Evict -0.0010 -0.33

-44.04 -15.31

Disclosure quality -0.0336 -16.53

-19.83 -10.28

Sales growth -0.0074 -0.0115 0.0031 0.0027 -14.85 -14.84 3.82 0.79

-2.83 -3.72 0.46 0.97 -6.58 -6.72 1.25 0.57

Industry Median Q 0.1201 0.2752 0.0613 0.0102 157.06 212.99 20.54 -12.74

13.26 19.84 1.27 0.65 7.31 10.81 1.28 -1.28

Percent Insider -0.2798 -0.0435 -0.1478 -175.93 -12.00 -60.92

-20.50 -1.02 -5.14 -18.77 -1.56 -3.04

Capex/Assets 0.0732 0.2140 0.1367 38.03 102.57 22.35

2.46 2.62 3.24 1.26 2.79 0.81

Address Tax Rate 0.4706 151.74

12.85 5.21

Judicial Efficiency -0.0121 2.81

-4.27 1.69

Accounting Standards -0.0013 -2.50

-2.46 -6.81

Constant 0.2057 -0.6071 -0.0664 0.0796 -19.49 -282.55 -20.33 59.49

9.70 -16.40 -1.05 3.59 -0.74 -9.25 -1.03 3.45

Observations 62953 69092 1599 3928 62953 69092 1599 3928

Adjusted r-squared 0.64 0.31 0.25 0.17 0.29 0.16 0.08 0.27

80

Table 8. Regressions to explain individual firm institutional ownership (continued)

Panel B: Pooled cross sectional time series regressions by institution type

Percent of equity held by institutions, all countries

Independent institutions Grey institutions

(1) (2) (3) (4)

OFC dummy 0.037 -0.179 0.001 -0.011

5.64 -15.13 1.56 -15.51

DR dummy 0.016 0.016 0.003 0.004

1.34 1.17 3.05 3.67

Anti-Director Rights 0.047 0.155 0.003 0.008

13.33 22.85 8.92 15.02

Evict -0.001 0.000

-44.11 -15.83

Disclosure quality -0.032 -0.002

-19.53 -13.51

Sales growth -0.006 -0.010 -0.001 -0.002

-2.39 -3.33 -5.60 -5.37

Industry Median q 0.112 0.259 0.008 0.016

12.75 19.46 5.28 12.45

Percent Insider -0.262 -0.018

-20.18 -16.56

Capex/Assets 0.073 0.000

2.57 0.07

Address Tax Rate 0.454 0.016

12.99 5.72

Judicial Efficiency -0.011 -0.001

-4.27 -3.60

Accounting Standards -0.001 0.000

-2.27 -4.35

Constant 0.190 -0.583 0.016 -0.024

9.39 -16.53 5.97 -9.49

Observations 62953 69092 62953 69092

Adjusted r-squared 0.64 0.31 0.22 0.10

81

Table 9. Summary of difference-in-difference regressions to explain institutional ownership

This table measures the impact of changes in law on institutional investment (percent held, number of institutions) in OFC versus non OFC incorporated firms.

Specifications 1 and 5 from Panel A of Table 8 are augmented with an event intercept dummy equal to one for firm-years in or after the event year and the

difference-in-difference interactive equal to this event dummy times the OFC dummy. An additional specification tests the parallel trends assumption (Roberts and

Whited, 2012). We do not report the slopes on control variables, The Dodd – Frank Act (2010) is studied with US address firms listed on the NYSE, NYSE MKT,

NASDAQ, or NASDAQ SmallCap markets. “Cayman OFC dummy” is a dummy variable equal to one for firms incorporated in the Cayman Islands.

Dodd – Frank Act of 2010 Cayman Islands Company Act of 2009

US address firms All firms US address firms China address firms Hong Kong address firms

Percent held Number Percent held Number Percent held Number Percent held Number Percent held Number

Event x OFC dummy 0.0173 -13.32

1.01 -0.68

Event x Cayman OFC dummy 0.0085 -1.52 0.0132 -51.56 0.0038 1.95 0.0013 3.43

1.02 -0.45 0.42 -2.21 0.30 0.44 0.21 1.68

Cayman OFC dummy -0.0036 -1.57 0.0768 14.64 0.0262 19.83 0.0001 -2.45

-0.29 -0.20 1.58 0.15 1.54 2.75 0.01 -0.51

OFC dummy -0.0336 19.28 0.0751 18.79 -0.0248 0.81 0.0152 17.00 0.0192 -18.66

-0.77 0.24 8.06 2.35 -0.50 0.01 0.62 1.56 1.78 -1.79

Event dummy -0.0021 -15.12 -0.0159 -26.87 -0.0122 10.16 -0.0049 4.07 -0.0006 4.20

-0.35 -3.32 -5.51 -5.89 -2.09 1.93 -0.33 0.77 -0.12 1.66

Parallel trends test -0.0215 -11.99

(Time x OFC dummy) -1.60 -0.67

Parallel trends test -0.0080 6.17 0.0363 13.32 -0.0014 1.90 -0.0019 -1.11

(Time x Cayman OFC dummy) -1.83 2.40 14.65 5.34 -0.17 0.72 -0.61 -1.09

Observations 1420 1420 8810 8810 1376 1376 586 586 972 972

Adjusted r-squared 0.4762 0.6443 0.6401 0.5209 0.4706 0.6935 0.3029 0.3120 0.2111 0.4266

82

Supplement to Table 8. Matched estimator selection bias correction for institutional ownership

This table compares institutional ownership of OFC incorporated firms and matched non OFC firms. The matching process is summarized by probit regressions for

both unmatched raw and matched samples similar to what is reported in Panel A of Table 5 but estimated over the smaller sample period (starting 1999) for

institutional investor data. Matching starts with the propensity score from the pre-matched probit regression on the entire sample with year, industry, and country

fixed effects; matches are the closest propensity score match drawn from non OFC firms of the same address country, industry, and year. The first panel

summarizes the firm’s percent institutional holding for OFC incorporated firms and two sets of matched non-OFC firms. The second panel summarizes the number

of institutional owners per firm. Matching is done with “nearest neighbors” equal to one. z-statistic is reported beneath each slope coefficient estimate. Employment

cost variables excluded due to missing observations. “ATT” denotes average treatment effect of the treated. Following Ferreira and Matos (2008), “independent”

institutions are mutual fund managers and investment advisors while “grey” institutions such as bank trusts and insurance companies are more likely to be loyal to

corporate managers of firms they invest in. The time span of results is constrained by the FactSet/LionShares database which commences with 1999.

Panel A: Probit regression with OFC dummy as dependent variable

All Countries China Hong Kong All except China and Hong Kong

Unmatched Matched Unmatched Matched Unmatched Matched Unmatched Matched

Sales growth 0.348 0.124 0.0121 -0.0462 0.118 0.0681 0.434 0.385

6.25 1.164 0.038 -0.146 1.262 0.414 6.9 2.929

log Sales 0.0498 0.0629 -0.24 -0.0642 0.181 0.116 0.0105 0.0428

1.535 1.205 -1.563 -0.431 2.797 1.566 0.231 0.675

Capex/Assets 1.462 2.227 4.119 4.06 3.579 2.106 0.694 -0.43

3.07 2.507 2.013 1.759 2.931 1.736 1.191 -0.495

Percent Insider 0.0909 -0.4 -0.65 -2.334 -0.309 0.469 0.144 0.138

0.392 -0.94 -0.661 -2.203 -0.53 0.642 0.556 0.3

Constant -2.042 -0.973 4.199 2.482 -1.163 -1.762 -1.476 -0.628

-2.627 -0.777 1.86 1.314 -1.105 -1.54 -1.608 -0.441

Pseudo r-squared 0.7 0.0134 0.155 0.127 0.15 0.0182 0.182 0.0075

Observations 29,823 3,955 501 421 2,873 2,547 21,656 961

83

Supplement to Table 8. Matched estimator selection bias correction for institutional ownership (continued)

Panel B: Average institutional ownership measures

All countries China Hong KongAll except China and Hong Kong

Untreated ATT Untreated ATT Untreated ATT Untreated ATT

OFC observations 3,006 3,006 364 364 2,120 2,120 514 514

Non OFC observations 4,728 949 68 57 554 427 4,106 447

Percentage of equity held by all institutions

OFC 0.1032 0.1032 0.0925 0.0928 0.0654 0.0654 0.2674 0.2674

Non-OFC 0.2785 0.0748 0.0479 0.0449 0.0258 0.0287 0.3164 0.2882

Difference -0.1753 0.0284 0.0447 0.0479 0.0396 0.0367 -0.0491 -0.0208

t-statistic -29.07 2.13 4.01 5.85 9.96 9.38 -3.43 -1.12

Percentage of equity held by independent institutions

OFC 0.0997 0.0997 0.0896 0.0898 0.0631 0.0631 0.2584 0.2584

Non-OFC 0.2649 0.0708 0.0442 0.0401 0.0247 0.0276 0.3009 0.2760

Difference -0.1652 0.0289 0.0454 0.0497 0.0384 0.0355 -0.0425 -0.0176

t-statistic -28.87 2.31 4.21 6.59 9.92 9.47 -3.13 -0.99

Percentage of equity held by grey institutions

OFC 0.0035 0.0035 0.0029 0.0029 0.0023 0.0023 0.0089 0.0089

Non-OFC 0.0137 0.0040 0.0036 0.0048 0.0010 0.0011 0.0155 0.0121

Difference -0.0101 -0.000

5 -0.0007 -0.0018 0.0013 0.0012 -0.0066 -0.0032

t-statistic -15.04 -0.27 -0.88 -1.26 4.49 4.19 -3.85 -2.52

Number of all institutional investors

OFC 39.77 39.77 30.76 30.85 21.93 21.97 119.98 119.98

Non-OFC 96.60 37.06 22.10 10.57 24.88 23.64 107.51 91.85

Difference -56.84 2.71 8.66 20.28 -2.96 -1.67 12.46 28.13

t-statistic -13.93 0.27 1.64 5.99 -1.22 -0.35 1.22 1.79