The World of Mutual Funds

Ajay Khorana, Henri Servaes and Peter Tufano

Ajay Khorana The Darden School - University of Virginia Henri Servaes London Business School and CEPR Peter Tufano Harvard Business School and NBER Draft: April 25, 2003 Copyright ©2003 Ajay Khorana, Henri Servaes and Peter Tufano Working papers are in draft form. This working paper is distributed for purposes of comment and discussion only. It may not be reproduced without the permission of the copyright holder. Copies of working papers are available from the authors.

The World of Mutual Funds*

This paper studies the mutual fund industry in 55 countries around the world and tests various hypotheses for why the fund industry would be preferred by investors over two alternative asset management choices: “do-it-yourself” options where the investors purchases primary assets and “opaque financial institution” options, such as banking or insurance investments. Consistent with the law and economics literature, we find that the mutual fund industry is larger in countries with stronger rules, laws, and regulations, specifically where mutual fund investors’ rights are better protected. The industry is smaller in countries where barriers to entry are higher, measured by the time and cost required to set up a new fund. The mutual fund sector is smaller when banks face restrictions when entering the securities business. The fund industry is larger in countries with a wealthier and more educated population. Finally, the fund industry is larger in countries in which defined contribution pension plans are more prevalent. These results indicate that laws and regulation, supply-side, and demand-side factors all affect the size of the mutual fund industry.

Ajay Khorana Henri Servaes Peter Tufano The Darden School London Business School Harvard Business School University of Virginia and CEPR and NBER P.O. Box 6550 Regent’s Park Soldiers Field Charlottesville, VA 22906 London NW1 4SA Boston, MA 02163 KhoranaA@ hservaes@london.edu ptufano@hbs.edu darden.virginia.edu * We would like to thank Viral Acharya, Sally Buxton (Cadogan Financial), Kurt Cerulli (Cerulli Associates), Elizabeth Corley (Merrill Lynch Investment Managers), Neil Fatherly (KPMG), Julian Franks, Francisco Gomes, Diana Mackay (FERI Fund Market Information), Wolfgang Mansfeld (Union Asset Management and FEFSI), Ben Philips, Mark St. Giles (Cadogan Financial), Wanda Wallace, Rodney Williams (FERI Fund Market Information) and seminar participants at City University (London), College of William and Mary, HBS European Research Colloquium, London Business School, and Washington University for their useful comments and suggestions and Katie Boggs and Stefano Rossi for valuable research assistance. Financial support for this project was provided by the Division of Research of the Harvard Business School. Any opinions expressed are those of the authors and not those of any of the organizations which supported or provided information to this study.

Over the past few decades, there has been explosion of the mutual fund industry -- and an attendant

expansion of academic research on this topic. However, while the fund industry has grown all around the

world, academic studies of mutual funds have remained geographically narrow. While at the end of 2001,

funds originating in the U.S. accounted for only 15 percent of the number of funds available globally and

60 percent of the world’s fund assets,1 in the last ten years, almost every article published in the top

finance journals relates to the U.S. fund industry. While there have been isolated and excellent studies of

the fund industry in various countries,2 most readers of U.S. journals would probably be surprised that the

nation domiciling the second largest fund industry (measured by fund assets) outside the United States is

Luxembourg with 6.5 percent of world mutual fund assets or that France and Korea offer the second

largest number of mutual funds available worldwide (13 percent of the world total for each country).

In this paper, we study the fund industry around the world. We seek to understand why this form of

intermediated asset management has thrived more in some countries than in others. In functional terms, a

nation’s savers can choose to directly invest in primary securities (do-it-yourself), may invest their money

in transparent narrow financial intermediaries (funds), or may prefer more opaque and typically broader

financial intermediaries (banks or insurance firms).3 Under what circumstances have relatively transparent

financial intermediaries been preferred by a nation’s savers? One set of hypotheses, drawn from the

ample literature on law and economics, focuses on laws and regulations to explain differences in this

direction of financial development. Funds prosper when laws and regulations make this sort of

investment attractive relative to others. “Supply-side” hypotheses focus on competitive dynamics both

within the mutual fund industry and from elsewhere in the financial services sector to explain these

differences. For example, a concentrated banking sector could plausibly encourage or discourage the

formation of a strong fund industry. “Demand-side” hypotheses focus on characteristics of the potential

1 These data come from the Investment Company Institute’s 2001 Mutual Fund Fact Book, pages 100 and 101. 2 See, for example, Bams and Otten (2002), Blake and Timmermann (1998), Brown, Goetzmann, et al. (2001), Cai, Chan, and Yamada (1997), Dahlquist, Engstrom, and Soderlind (2000), and Dermine and Roller (1992). 3 A mutual fund is an example of a transparent investment vehicle in that its underlying assets are clearly identifiable and the value of the fund is marked-to-market on a daily basis and reflected in the Net Asset Value of the fund. Investing in a relatively opaque financial intermediary entails an investor’s claim that is not contractually linked to the underlying asset (e.g., bank deposits and insurance products).

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buyers of mutual funds in terms of, for example, their degree of wealth and education, to explain these

differences. Finally, the characteristics of the country’s securities trading environment may affect

investor choice. These are not mutually exclusive hypotheses -- rather all are likely to play some role in

explaining worldwide patterns in the fund industry.

Our goal is to study a broad sample of countries. We gather reliable data for 55 nations and measure

the size of each country’s mutual fund industry relative to the country’s assets in “primary” domestic

securities (which includes equities, bonds, and bank loans) that are available to prospective investors. For

completeness, we also report the size of national fund industry assets relative to the country’s GDP and

population. We study the industry as a whole, and equity and bond funds separately, because certain

hypotheses apply only to one of the two subsectors.

For the countries in our sample, the mutual fund industry comprises 16.8% of the primary financial

assets, on average, with a median of 4.3%. As we discuss in more detail subsequently, two countries are

substantial outliers -- the fund industries in Luxembourg and Ireland account for 484% and 82% of their

country’s primary assets. We treat these two extreme observations with some care in our work. The

naïve inclusion of these countries in multivariate analyses can produce misleading results, but given that

these are two interesting data points in our analysis, it would be inappropriate to just treat them as

outliers. Hence, we analyze the drivers of the fund industry for the remainder of our countries and

separately discuss the unique factors driving the growth of Luxembourg and Ireland.

We focus on four national characteristics to explain the size of the mutual fund industry across

countries: law and regulation; demand characteristics; supply side factors; and the nature of the securities

markets. Countries with a stronger judicial system, more stringent regulatory approval and disclosure

requirements for funds tend to have a larger fund industry. This suggests that stronger regulation may be

beneficial to the fund industry. Furthermore, for equity funds, the enforcement of insider trading rules

has an adverse effect on the size of the mutual fund industry, consistent with the view that failure to

enforce these rules discourages investors from purchasing equities directly.

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When considering supply side factors, we study characteristics of the financial sector that would

influence the speed of adoption of mutual funds. We examine the effect of bank concentration,

restrictions placed on banks to enter the securities business, the number of distribution channels available

for funds, the presence of an explicit deposit insurance system for banks, and the time and cost to set up a

new fund. We find that nations that restrict banks from entering the securities business have smaller

equity and bond fund sectors. In addition, countries with a more concentrated banking industry have

smaller bond fund sectors. There is some evidence that barriers to entry are associated with small fund

industries; in particular, the costs and time required to set up a new fund are negatively related to industry

size.

When considering demand side factors, we find that wealthier countries, measured by GDP per

capita, and countries with a more educated population have a larger mutual fund sector. These effects are

particularly pronounced for the equity sector, which may require a higher level of investor sophistication.

Internet penetration is also positively related to the size of the mutual fund sector, but it is highly

correlated with the other demand size variables. In addition, the mutual fund sector is larger in countries

where a larger fraction of pension plans are defined contribution plans.

We interviewed experts to explain the growth of Luxembourg and Ireland. Tiny Luxembourg grew to

be a European mutual fund hub, fueled by favorable bank secrecy and tax laws as well as its central

location. The growth of Ireland (Dublin in particular) on the other hand, was driven by a tax advantage

given to management companies and a highly educated labor force. In particular, until recently, fund

management companies paid a tax of only 10% on their income (relative to a 32% corporate income tax

in Ireland) and they were allowed extra deductions for rental payments.

The remainder of this paper is organized in four sections. The first section defines what we mean by

a “mutual fund” in light of varying institutional arrangements used around the world. The second section

describes the sources of our data on the world fund industry and provides a brief sketch of the industry.

This descriptive part of the paper provides stylized facts about the industry. The third section discusses

factors that might explain the differential penetration of the fund industry in different countries. The

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fourth section of the paper reports our findings and the fifth section offers conclusions. An Appendix

contains a detailed description of the variables employed and the associated data sources.

I. Mutual Funds, UCITS and Collective Investment Schemes

In the U.S., the mutual fund “industry” is defined largely by regulation, in particular by the

Investment Company Act of 1940 (the ’40 Act). The ’40 Act, as interpreted over the years, empowers the

SEC to oversee a variety of “investment companies,” which include “mutual funds, closed-end funds,

Unit Investment Trusts, Exchange Traded Funds, and interval funds,” as well as variable insurance

products, federally registered investment advisers, and public utility holding companies.4 The Act

functionally defines the set of investment companies overseen by the SEC. Interestingly, the popular

term “mutual fund” is neither defined nor used in the Act. While all of these investment companies are

vehicles to pool savers’ assets, they differ along various dimensions. American “mutual funds” are

management companies that (1) invest in diversified portfolios of assets; (2) are “open-end” in that they

will redeem their shares at net asset value (NAV) at any time upon shareholder request.5

In other countries, different names and definitions are used for similar businesses. The European

Union, in an attempt to create a “harmonized” fund industry has adopted a common definition of

“Undertakings for Collective Investment in Transferable Securities” or UCITS.6 Mirroring the U.S.

definition of mutual funds above, UCITS are defined as undertakings that (1) are collective investments in

transferable securities for the purpose of risk-sharing, and (2) are repurchased out of the assets of the fund

at net asset value.

Across the world, organizations have identified an analogue to mutual funds or UCITS, called

“collective investment schemes” or CIS. For example, the International Organization of Securities

4 SEC, Division of Investment Management Web Site, http://www.sec.gov/divisions/investment.shtml. 5 A “management company” is a catch-all phrase used in the Act to designate all investment companies other than specifically defined ones. For a further discussion, see the Investment Company Institute (industry association for the fund industry) http://www.ici.org/aboutfunds/organization_operation.html. 6 For the full text of the first of many directives about UCITS, see

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Commissions (IOSCO) defines a CIS as “an open end collective investment scheme that issues

redeemable units and invests primarily in transferable securities or money market instruments.”7

Thus, mutual funds, while going by a variety of names, are fairly comparable around the globe.8 In

this paper, we contrast mutual funds with other ways in which households might save and invest in

financial assets, which we characterize broadly as “do-it-yourself (DIY)” and “opaque financial

intermediaries.” Whereas a mutual fund is defined as a pooled diversified investment vehicle, “do-it-

yourself” investments are direct investments by households in primary securities (bonds, stocks or cash).

Whereas open-end mutual funds are defined world-wide in terms of their transparency in the form of their

redemption at net asset value of their underlying assets, “opaque financial investments” include pension,

insurance and banking products that invest in portfolios of assets which are not readily identifiable, and

where there is not always a direct contractual link between the value of their assets and claims by

investors. While we recognize that our categorization is imperfect, it captures three different modes of

household investing behavior. Our objective is to understand when the mutual fund model succeeds in

attracting assets relative to these other modes.

Our analysis takes the country as a unit of observation. As is perhaps not a surprise, determining the

“nationality” of a fund is complicated. A fund may be identified with many different countries. It may be

legally domiciled (registered) in one country, invest in securities of a second country, and be sold to

citizens of a third country. The first two geographical identities are fairly easy to ascertain, while for

many funds, the third is not. For this paper, we will identify funds’ nationalities by their legal domicile,

which determines the relevant regulation and the legal system. For many countries, such as the U.S., this

will also determine the nationality of its investors. In these instances, our country categorization also

captures the nature of competition among potential rivals and savings patterns of potential investors.

http://www.europefesco.org/DOCUMENTS/DIRECTIVES/Dir-85-611.PDF. As one might imagine, there has been on-going refinement of the notion of UCITS, including new discussions of what assets may be held and what types of investment companies can manage them. 7 International Organization of Securities Commissions, Report on Investment Management, 1994. Available on-line at http://www.iosco.org/iosco.html. 8 This is not to say that a fund in the U.S. is identical to a fund in Luxembourg or Germany. There are substantial differences in terms of marketing, regulation and other characteristics which we discuss below.

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However in a few instances -- notably Luxembourg are Ireland -- the link between domicile and investors

is more tenuous.

II. Sample Construction and Primer on the World Fund Industry

A. Data

We begin our sample construction with a list of the top 75 countries in the world based on GDP at the

end of 2001.9 This list is matched with countries identified as having a fund industry in publications of

either the Investment Company Institute (ICI) or Fédération Européenne des Fonds et Sociétés

d'Investissement (FEFSI).10 The asset size for the countries not listed in the ICI and FEFSI data sources

is gathered through web-based sources and discussions with industry experts. We are able to obtain a

sample of 55 countries with data on the relative size of the fund industry, measured as a fraction of the

universe of primary securities at or close to the end of 2001. This sample includes five countries with no

mutual fund industry. Unless we could obtain definitive evidence for a country, we did not assume the

lack of a fund industry and instead chose to classify the country as having missing data.

Mutual funds hold assets that are claims against companies and governments. We seek to compare the

funds’ investments with the universe of these claims, which we call “primary securities.” For

governments, these claims include sovereign debt. For corporations, these claims include equity,

corporate bonds, and commercial loans. Therefore, we gather data on the size of the equity, bond, and

bank loan market to calculate the size of the primary securities market for each country.11 In addition, we

gather data on (1) local laws, taxes, and regulation, (2) the structure of the financial sector (supply-side),

(3) characteristics of the retail investing public (demand-side), and (4) the trading costs and turnover of

equity markets. While we attempt to gather as many proxies as possible for the country characteristics,

9 While time-series data on industry size are available, we restrict ourselves to a cross-sectional analysis using data as of 2001 for two reasons. First, the inclusion of time-series data would substantially restrict the number of countries for which we have data available on industry size and for the explanatory variables. Second, there is little or no time series variation in many of the explanatory variables we employ. 10 FEFSI is an association of the mutual fund industry of the member states of the EU, the Czech Republic, Hungary, Norway, Poland, and Switzerland.

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the lack of consistency in the set of countries included in various data sources and surveys reduces the

number of data points we can employ in our specifications, especially in the multivariate analyses. The

Appendix lists and describes the explanatory variables employed in this study along with the source of the

data. Whenever possible, we use data from international sources which are comparable across countries

(e.g., World Bank, IMF, United Nations, OECD, IOSCO), but in some cases resort to country-by-country

data collection.

In section III, we discuss the hypotheses and the proxies used in greater detail, but we first turn to a

description of the size of the fund industry across the world.

B. Description of the World Fund Industry

Table I provides a description of the size of the mutual fund industry across the world at the end of

2001. At that time, the worldwide mutual fund industry held $11.7 trillion in assets. The countries with

the largest fraction of the industry (not reported in the table) were the U.S. (60 percent), Luxembourg (6.5

percent), France (6.1 percent), Italy (3.1 percent) and Japan (2.9 percent). The countries at the other end

of the spectrum included Bangladesh, Romania, and Sri Lanka. In addition, we identified five countries

with no fund sector -- Algeria, Burma, Libya, United Arab Emirates, and Yugoslavia (Serbia and

Montenegro).

We measure the assets under management (AUM) for each country relative to (i) the size of primary

securities as measured by the sum of the equity market and bond market capitalization and bank loans, (ii)

its GDP, and (iii) its population. Median assets under management as a function of the country’s GDP

are 8.8 percent with a high of 3991 percent for Luxembourg, followed by Ireland with 186 percent and a

low of 0.011 percent for Bangladesh (after excluding the countries with zero mutual fund assets). When

we measure assets under management relative to the universe of primary securities, the fund industry

holds 4.3 percent of all primary securities in the median country, with Luxembourg once again at the high

11 Our measure excludes privately-placed debt issued by corporations. Bank deposits are included implicitly since they are employed to finance bank loans and security purchases.

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end with 484 percent, followed by Ireland with 82 percent. Finally, average mutual fund assets per capita

are $32,439, with a median of $585.

Given the dramatic size of the mutual fund industry in Luxembourg and Ireland by any measure,

additional discussion is warranted to explain this phenomenon. Favorable banking and tax laws have led

to a transformation of Luxembourg into a major center for offshore mutual funds. This growth was partly

fueled in 1992 when the German government decided to levy a 25 percent withholding tax on interest on

investment assets and bank deposits. This led to a movement of capital to Luxembourg-based fund

management subsidiaries of German banks. The benefit of Luxembourg as a tax haven has been further

accentuated by the presence of stringent bank secrecy laws, which are among the toughest in Europe. The

success of Ireland on the other hand, has been driven by the establishment of an International Financial

Services Center (IFSC) in Dublin which provided important incentives to fund operators in the form of a

reduced tax of 10 percent on income earned for specific types of servicing and financing operations. In

addition, they get a double tax deduction for rents. Finally, the harmonization of regulations permitting

funds to be sold throughout Europe facilitated the growth of these centralized hubs.

The $11.7 trillion of world fund assets are held in 55,004 open-end funds, with a median number of

288 funds per country. The U.S., which has the largest fund industry in terms of the share of assets held,

is also the largest in terms of the number of available mutual funds (8,307 funds at the end of 2001).

France and Korea are second and third with 7,144 and 7,117 funds respectively. It is intriguing to note

that there are over 55,000 different “products” in this industry -- a staggering number compared to almost

any other industry.

We also measure the size of the mutual fund industry across various asset classes. The total size of the

equity mutual fund industry (including balanced funds) is similar to that of the bond industry (including

money market funds). At the end of 2001, equity and bond fund assets stood at $5,783 billion and $5,340

billion respectively with median country assets of $9.3 billion and $12.9 billion across the two asset

classes.

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Median equity mutual fund assets (including balanced fund assets) as a percentage of the total

domestic equity market capitalization of the domiciled country stand at 12 percent (mean is 62.6 percent).

Bond and money market funds account for 6.3 percent of the domiciled country’s primary fixed income

investments in the median country (mean is 17.4 percent). This suggests that funds have been more

successful -- worldwide -- in capturing a share of equity assets than of bond assets.

III. Why Has the Fund Industry Thrived in Some Countries More Than In Others? Possible

Explanations and Summary Statistics

The results in Table I and the discussion above indicate that the fund industry is larger in some

countries than in others. Our goal is to explain the differences in the rate of adoption of funds relative to

other modes of investing, in particular relative to “do-it-yourself” investing (direct holdings of bonds,

government securities and stocks) or “opaque” investing (typically through pension funds, insurance

companies, hedge funds, and banks). The following table shows the alternative investment modes by

asset and by type.

Do-it-yourself Fund Opaque Money market and Bonds

Cash holdings and direct holdings of corporate and government bonds

Money market fund and Bond fund

Bank deposit and Medium/long term bank investment, e.g., CD

Equities Direct holdings of equities

Equity fund Pension fund, hedge fund.

In this section, we identify four sets of factors that would favor fund investing: laws and regulation,

supply-side considerations, demand-side considerations, and trading characteristics. The Appendix lists

the explanatory variables and Table II provides descriptive statistics.

A. Laws and Regulation

The legal and regulatory structure of a country can favor one mode of investment over another. For

example, a country that banned mutual funds or restricted their use in tax advantaged savings schemes,

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would naturally have low fund adoption. We identify three broad classes of legal and regulatory factors

that can potentially influence the size of the industry.

Overall legal environment. There is a large body of literature which documents how differences in

legal and regulatory environments affect financial development. We leverage these prior studies to

examine the impact of legal structure on the specific development of the fund industry. La Porta, Lopez-

de-Silanes, Shleifer and Vishny (LLSV) (1998) show that the quality of the legal system is important for

the enforcement of contracts and also captures the government’s general attitude towards business.

Investors face a tradeoff when evaluating intermediated products vs. Do-It-Yourself. We hypothesize that

individuals are more willing to invest via an intermediary (vs. Do-It-Yourself) if the quality of the legal

system is better, since the creation of a mutual fund involves an additional layer of contracts which need

to be enforced. Alternatively, investors may prefer intermediaries when the legal framework is weak

because the intermediaries substitute for the quality of the legal system. We use the five legal variables

employed by LLSV to capture the legal framework of a country: (1) efficiency of the judicial system, (2)

rule of law, (3) corruption, (4) risk of expropriation, and (5) risk of contract repudiation. These variables

are constructed such that higher values imply a higher quality legal system. Because the legal variables

are highly correlated, but each variable contains some unique information, we construct a new variable,

called “judicial quality,” which sums these five measures.

We also expect the enforcement of insider trading regulations to have an impact on the development

of the mutual fund industry.12 In particular, when insider trading regulations are enforced, investors may

be more willing to buy and hold individual securities directly; whereas when insider trading is not

punished, they are more likely to rely on an intermediary who is perhaps better informed. The LLSV

variables are useful to understand all contracts whereas the insider trading enforcement variable is most

relevant for equities because information asymmetries are less pronounced for bond and money market

investments.

12 Bhattacharya and Daouk (2002) note that it is not the mere presence of insider trading regulations, but their enforcement, that determines the cost of equity.

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The quality of accounting standards could also affect the growth of the mutual fund industry. If

accounting standards are weak, investors may be reluctant to invest in securities themselves, and rely

instead on financial intermediaries. This effect is likely to be more pronounced for equity investments.

Better accounting standards would also improve reporting provided by the fund itself, however, which

could increase investor confidence in this intermediary.

Fund Regulation. The rigor of the laws and rules in terms of how fund companies are governed and

regulated is likely to strengthen investor confidence and their willingness to invest in mutual funds. To

measure the extent of transparency and regulation at the level of the mutual fund, we use several

measures. In particular, we create dummies if the following conditions are met: (1) regulatory approval is

required to start a fund, (2) regulatory approval is required before issuing a mutual fund prospectus, (3)

custodians are required to be independent from the mutual fund family, and (4) mutual funds have to

make eight or more fee and performance disclosures in advertising and fund information. We aggregate

(1) and (2) into a single “approval” variable. More investor protection -- approvals, independent

custodians and disclosures -- provide investors with a higher level of comfort in using mutual funds as an

investment vehicle.13,14

In addition, we determine what procedures are in place to prevent conflicts of interest between the

fund management company and the fund investor. Three dummy variables are used to capture the

presence/absence of these procedures: (1) are funds allowed to have a significant participation in

companies in which they invest?,15 (2) is disclosure employed to deal with conflicts of interest?, and (3)

are there regulatory requirements or industry best practice standards regarding internal control? We

combine these into a single “dealing with conflicts” measure.

13 The actual number of disclosures is between four and ten, and half the countries require seven or fewer disclosures and half required eight or more. The reason for not including the actual level of disclosures is that this effect is unlikely to be linear. As soon as the number of disclosures is sufficient to understand actual fees charged and past performance, it is unlikely that additional disclosures would further enhance investor confidence. 14 We also gather data on whether fund income is deemed to be earned by fund investors when the income actually arises or only when it is distributed by the fund. If income is deemed earned when it arises, investors have a claim to the income. This feature increases investor confidence in the fund. Unfortunately, this variable may also capture tax effects. We therefore exclude it from the main analysis in the paper.

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While a certain level of regulation can be beneficial for fund investors, there can be substantial costs

to over-regulation in the form of greater entry barriers for mutual fund companies and hence a stifling of

competition within the industry. Excessive regulation can therefore hinder the development of the mutual

fund industry and potentially lead to the movement of fund management firms to less regulated financial

markets.16 (We measure the costs of fund startup later in the study.)

Taxes. The public finance literature is replete with examples of how tax policy can affect investment

decisions [see, for example, Poterba and Samwick (2003)]. We would expect that funds would grow

stronger when tax rules make these investments attractive relative to others, through (1) tax preferences,

(2) laws which make tax avoidance easier with certain types of investments, and (3) the absence of tax

policies which impose multiple taxes on the same returns (double taxation). In addition, in countries

where fund management companies receive a more favorable tax treatment of their earned income (e.g.,

Ireland), one is likely to observe a larger mutual fund industry.

Unfortunately, data on the precise treatment of income from mutual fund investments across the

world cannot be obtained for a large cross-section of countries. We therefore limit ourselves to two key

tax policy variables. First, we obtain data on the tax rate paid by the fund management company -- this is

equal to the corporate tax rate in all countries, except Ireland. Second, we determine whether the country

allows securities in bearer form; investors are more likely to be able to avoid taxation of investment

income completely in countries where bearer securities are allowed because tracing income back to the

investors is difficult.17 Therefore, if investing in the fund creates more of a paper trail, which could be

tracked by tax authorities, investors may be more interested in buying the underlying securities

themselves.

15 We obtain this information from a survey conducted by IOSCO for OECD countries. The term “significant participation” is not defined in the survey. 16 It would be useful to include measures of the direct and indirect costs of regulation, but such data are not available for a large number of countries. Franks, Schaefer, and Staunton (1998) compare the direct regulatory costs for the investment management industry across three countries. They find that the costs in the U.K. are twice as high as the U.S. and four times as high as France. 17 Note that many countries that allow bearer securities require taxes to be withheld at the source. This withholding tax can often be avoided, however, when the income is received outside the country. Another related factor is

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Data. Table II, Panel A, includes summary statistics for the legal, regulatory and governance

variables described above. Note that insider trading laws are enforced in only 63% of the countries in our

sample. For the legal and regulatory variables at the level of the mutual fund, we find that 49 percent of

our sample countries require regulatory approval to start a fund and a large majority (98 percent), require

formal approval of a fund’s prospectus. In 44 percent of the cases, custodians are required to be

independent from the fund management organization. Also, in approximately half the countries, investors

are allowed to hold bearer securities.

B. Supply Side Considerations

Characteristics of the financial service sector, which we call the supply-side, can affect the size of the

mutual fund industry. One important player in the financial service sector is the banking industry. In the

U.S., mutual fund growth (especially the growth of money market funds) came at the expense of the

banking sector, whereas in Europe (outside of the U.K.), banks have been the primary promoters and

distributors of funds. This evidence suggests that it is ambiguous whether a strong or concentrated

banking sector would inhibit the growth of the fund industry or whether banks would use fund products as

another way to collect household assets. Table II, Panel B, shows that bank concentration, measured

using a Herfindahl index based on the asset size of the five largest banks has a median value of 0.68

(0.70) suggesting a moderate to high level of concentration of bank assets.

Furthermore, restrictions placed on banks to enter the securities business will have a negative effect

on their ability to offer mutual funds. Our measure of restrictiveness, which is based on Barth, Caprior,

and Levin (2001), ranges from 1 (least restrictive) to 4 (most restrictive). The median in our sample is 2.

Since a number of countries use the banking sector to distribute mutual funds, the presence of a

deposit insurance system for the banking sector could also affect the size of the fund industry. On the one

hand, the presence of a deposit insurance system (especially if mispriced) would favor insured deposits

whether the country is a tax haven for mutual funds in that no taxes are withheld from investments and there is bank secrecy. However, the lack of systematic data across countries prevents us from investigating this in more detail.

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over uninsured money market mutual funds, inhibiting the growth of the mutual fund sector. However, if

deposit insurance provides investors in bank-distributed funds a false sense of security, then this could

lead to a larger fund sector. Two-thirds of the countries in the sample have a deposit insurance scheme.

A greater breadth of available distribution channels (i.e. banks, broker-dealers, direct sales, insurance

companies and sales via financial planners) through which fund companies can sell their products to retail

investors, is likely to have a positive influence on the size of the mutual fund sector. The average country

in our sample has 3.65 distribution channels.

Additional supply side factors are the costs and time involved in setting up a fund and obtaining the

necessary regulatory approval. A significant cost and time commitment can act as a barrier to entry for

new funds to come to the marketplace and lead to a smaller fund industry. As shown in Table II, Panel

B, the median number of days to set up a new fund is 90 across all countries, with a high of 270 days for

Malaysia and Singapore followed by 225 days for the U.S. and a low of 28 days for New Zealand.

Similarly, there are large differences across countries in the costs to set-up a fund. We also report set-up

costs, which average $75,450 per fund or 0.056% of average annual assets under management. We do not

expect the relation between industry development and the time required to open a new fund to be linear.

In fact, as long as the delay is not ‘unreasonable’, the time commitment may not have much of an impact

on industry size. We therefore divide the sample into two groups using a cut-off of 60 days (high set-up

time dummy).18

C. Demand Side Considerations

In most countries, the mutual fund industry is a relatively recent financial innovation. A longstanding

literature on the diffusion of innovation shows that the characteristics of consumers influence the speed of

adoption (Rogers, 1995). We hypothesize that funds will be adopted -- and the industry will be larger --

when consumers are more sophisticated, have greater wealth (and hence investing experience) and have

18 This cut-off is somewhat arbitrary, but the qualitative nature of our results does not change for alternative cutoffs between 60 and 90 days.

14

access to better information.19 We use the following measures to capture wealth and investor

sophistication: per capita GDP, the literacy rate, and the average years of education received.20 To

capture access to information, we include newspaper circulation divided by population and internet

penetration. Internet penetration can also measure distribution capabilities of a country since many fund

complexes use the internet as a distribution channel. Table II, Panel C, reports that in the median country

in our sample, an individual receives 14.5 years of education (including part time and adult education)

with a median literacy rate of 99.8 percent. The median country has a GDP per capita of $8,980 and a

14% internet penetration rate.

The size of the potential fund market would affect its attractiveness to fund vendors. We include the

size of the population and the savings rate as a percentage of GDP as explanatory variables to capture this

effect. We do not expect a fund industry to emerge in countries with a small population because there are

certain fixed costs in organizing the industry and setting up the legal framework. In addition, we expect

the industry to be larger in countries with a higher savings rate.

Finally, while we have implicitly described the fund industry as a retail phenomenon, we recognize

that pension policy has had an impact on the development of the industry. Defined contribution (DC)

plan assets are sometimes invested in mutual funds. To capture this stimulus to the growth of mutual

funds, we collect information on the relative proportions of DC and defined benefit (DB) pension plans

by country. DC plans comprise about 40% of pension plans in the median country in our sample.

D. Trading characteristics

By definition, mutual funds stand ready to redeem shares at net asset value on a regular basis. This

implies that the quality and reliability of the reported net asset values are important for fund complexes

and investors. The more stale the observed market prices, the greater the scope for discretion on the part

19 Most innovations are modeled as “s-curve” phenomena, and therefore the size could partially be a function of the age of the industry. However, it is difficult to date the age of the industry across the countries in our sample. 20 In the specifications reported throughout the paper, we employ the level of per capita GDP and education as explanatory variables. Using the logarithm of both variables instead does not affect the results.

15

of the fund in setting the net asset values. This lowers the inherent transparency and hence desirability of

mutual funds as investment vehicles. We use the frequency of trading (i.e., average share turnover on the

domestic exchange) as a measure of the quality of the reported net asset value.

In addition, the trading costs paid by investors who buy their own securities relative to those paid by

institutional investors are also likely to be important. If this gap is large, we expect investors to turn more

to mutual funds instead of buying securities themselves. While we do not have data on the gap between

trading costs of institutions and individuals, we assume that this gap increases with the level of

institutional trading costs. The sum of commissions and fees paid by institutional investors for equity

trades are included in our analysis for this purpose. Both turnover and trading costs are obviously only

relevant proxies for studying the size of the equity mutual fund sector. As illustrated in Panel D of Table

II, institutional trading costs in the median country are 31 basis points, while the median share turnover is

60% per annum.

E. Correlations and Caveats

Before turning to the analysis of the determinants of the development of the fund sector, we study the

correlation structure across the independent variables in our sample.21 A number of conclusions emerge.

First, all the legal variables are highly correlated with each other and with the quality of accounting

standards. Second, wealth, education, literacy, newspaper circulation, and internet penetration are also all

highly correlated. These conclusions are perhaps not surprising, but they suggest that the variables in this

group should not be included in the same regression. Third, there is also a strong correlation between the

legal variables and the buyer characteristics; this confirms the findings by LLSV (1998), who report a

strong relation between GDP per capita and the legal variables. This correlation is more problematic for

our purposes because it makes it difficult to distinguish between the effects of the legal environment and

buyer characteristics. As a result of these observations, we construct summary statistics (judicial quality,

21 For the sake of brevity, the correlations are not reported in a table.

16

approvals and conflicts of interest) as described above and use parsimonious sets of explanatory variables

in our specifications.

Two limitations of our analyses need to be pointed out. First, we recognize that some of the

explanatory variables employed in our analysis are endogenous. While the general laws and regulations

in a country are unlikely to be affected by the fund industry, fund regulation may be influenced by the

size of the sector. It still useful to illustrate these relationships, even though it is not always possible to

ascribe causality. Second, our analysis is of the cross section of nations in 2001; data limitations prevent

us from examining industry size over time, and, even if time series data were available on industry size,

several of the key explanatory variables do not change over time.

IV. Results

A. Univariate Regression Results

We start by presenting the coefficients of univariate regressions of our measure of fund size (assets

under management in the mutual fund industry as a fraction of the universe of primary securities in the

nation) on the explanatory variables discussed previously. These results are displayed in Table III. We

report findings for the entire industry as well as for equity and bond funds separately. In the analysis,

balanced funds are combined with equity funds and bond funds include money market funds. For sake of

brevity, we do not report the intercept, but just the coefficient on the explanatory variable, its p-value and

adjusted r-squared. In addition, variables that are only expected to affect equity funds are not included in

the subset for bonds. We boldface the variables significant at the 10% level or better.

Luxembourg and Ireland are excluded from all specifications. Both countries are extremely

important in the industry, but we have already discussed the factors that led to their success. In addition,

both countries attract funds from across Europe, which implies that national characteristics are less

relevant in explaining their development.

17

Laws and regulations matter. The results indicate that the mutual fund sector is larger in countries

with a better legal environment, when new fund starts and fund prospectuses need to be approved, and

when substantial fee and performance disclosures are required.

There is also some evidence that the bond fund sector is smaller when the banking sector is more

concentrated. The industry is smaller in countries with a higher fund set-up time and costs relative to

average fund size, but the effect is only significant for the full sample.

Wealthier countries with a more educated population, higher internet penetration and with a greater

proportion of defined contribution plans also have a larger mutual fund sector. The results on country

wealth and investor sophistication hold for the entire industry as well as for equity funds. Finally, the

bond fund sector relative to the nation’s bond and bank loan market is larger in countries with a larger

population.

In terms of explanatory power, education is the most important variable for the entire sector. For

equity funds, education, judicial system quality, and required approvals are equally important, while the

approvals variable has the highest explanatory power for bond funds.

B. Multivariate Regression Results

The above results are only suggestive because we examine one explanatory variable at the time.

Table IV contains a multivariate analysis for the overall industry, whereas Tables V and VI focus on

equity and bond funds separately. For all these analyses, the unit of observation is the country, and the

relative size of the fund sector is the dependent variable.

All funds. Model (i) of Table IV includes judicial system quality and the required approvals variable.

Only the approvals variable is significantly positive when both variables are included simultaneously.

This suggests that regulations specific to the fund industry are more closely related to the size of the

nation’s fund sector than is the country’s overall legal and regulatory environment. In model (ii), the

importance of required approvals persists, even in the presence of our measure of investor wealth. This

result is economically important: nations demanding that regulators approve both fund starts and

18

prospectuses have a fund sector that is 5.90 percentage points larger than countries that only require one

of these approvals. This represents about 87% of the mean industry size (computed after removing

Luxembourg and Ireland). Thus, the fund industry holds a larger fraction of a country’s primary

securities when investors are protected through a more elaborate approvals process.

In model (v), we include two fund-specific regulatory variables: approvals and the high-disclosure

dummy. Both are positively and significantly related to size of the fund industry. Thus, nations that

require fee and performance disclosures above the median have a fund sector that is 7.5 percentage points

larger than nations that do not.

In model (viii) we include a dummy variable to measure whether the country allows bearer securities.

Interestingly, this variable is negative and significant, indicating that the industry is smaller in countries

that allow bearer securities. As it turns out, this result is driven by the bond fund sector. We therefore

defer the explanation of this finding until we have analyzed that sector in detail.

Models (vi), (vii), and (viii) add our measures of barriers to entry. These models indicate that the

industry is smaller when barriers to entry are higher, in particular when it takes 60 days or more to set up

a fund (high set-up time variable) and when set-up costs are a larger fraction of average fund size. For

example, nations with relative set-up costs at the 25th global percentile (0.014%) have a fund sector 6.8

percentage points larger than nations with relative set-up costs at the 75th percentile (0.070%) (based on

model (vii)).

Models (ii), (iii), (vi), and (viii) indicate that investor wealth and education are also important

determinants of the size of the industry.22 In this cross-section of nations, increasing GDP/Capita from its

25th percentile ($3,783) to its 75th percentile ($23,755) increases the relative size of the industry by 5.65

percentage points (based on model (vi)). Note that the effect of investor wealth is not significant in

models (iv) and (v). Model (iv) includes the proportion of DC pension plans; the coefficient on this

variable is positive and significant at the 10% level. This is consistent with our prediction that the mutual

22 Internet penetration, when included with the approvals variable, is also significant in all specifications. We exclude it from the models reported in Tables IV, V, and VI because it is highly correlated with GDP per capita.

19

funds industry has benefited from the introduction of DC plans. Increasing DC pension plans from its

25th percentile (12.5%) to its 75th percentile (80%) increases industry size by 4.65 percentage points

(based on model (iv)).

Equity funds. Table V focuses only on equity funds. The first three models mimic those of Table IV.

Note that in this analysis the quality of the judicial system is an important determinant of the size of the

equity fund sector. However, because of multicollinearity, we do not include measures of country wealth

and education when the judicial system quality variable is included.

Models (iv) through (vii) contain a dummy variable to capture whether insider trading restrictions are

enforced. This variable is negative and highly significant: if insider trading restrictions are enforced,

investors are more willing to buy equities on their own and consequently the mutual fund sector is less

developed. Models (vi) and (vii) further illustrate the role of fund-specific regulatory effects: in

particular, there is some evidence that nations with more regulatory or industry requirements addressing

conflicts of interest between the fund and its investors have larger equity fund sectors.

On the supply side, in model (vii), we also include the degree to which banks are restricted from

entering the securities business, but this variable is not significant. It turns out that the variable is highly

correlated with the conflicts variable. When we just focus on approvals and restrictions from entering the

securities business in model (viii), we find that restrictions placed on banks negatively affect industry

size. Thus, despite the fact the mutual fund industry in the U.S. grew at the expense of the banking

sector, this analysis provides some evidence to suggest that this is not the case in the rest of the world.

On the demand side, GDP per capita and investor education are positively related to the size of the

equity fund industry, as illustrated in models (ii) and (iii). Finally, model (v) illustrates a significant

association between the use of DC plans and industry size.

Overall, the economic significance of the results is generally larger than what we observe in Table IV.

For example, nations with GDP per capita at the 25th percentile have an equity fund sector 8.7 percentage

points smaller than nations at the 75th percentile, compared to 5.65 percentage points in Table IV. The

20

same change for DC plans leads to a 12.3 percentage point difference in industry size, compared to 4.65

percentage points in Table IV.

Bond funds. Table VI focuses on bond funds. Again, we find that the approval variable is positively

related to industry size, although the magnitude of the coefficient is smaller than for equity funds. The

coefficient on our measure of judicial system quality is negative and marginally significant in model (i),

but it is insignificant when combined with any of the other explanatory variables.

In models (vi) through (viii) we find that countries which allow investors to own bearer securities

have a smaller bond fund industry. Our discussions with industry experts provide an explanation for this

result. The typical bond investor in many countries tends to purchase bonds at the time of issuance and

holds the securities until maturity, relying on fixed coupon payments for income. Bearer bonds allow

these investors to clip their bond coupons and redeem them for cash; if this happens outside the investor’s

home country, taxes are generally not withheld. Also, the fund purchase may establish a paper trail,

which makes it more difficult to avoid income or estate taxes.

In models (v) and (vii) the concentration of the banking sector is negatively related to industry size,

and this effect is significant in model (vii).23 This suggests that in nations where banks have market

power, the fund industry has experienced limited growth. In models (iv) and (viii), we include the

restrictions imposed on banks when entering the securities business. This variable is negative and

significant in model (viii), which provides some evidence that the bond fund sector is smaller when banks

are prohibited from entering the securities business or face regulatory hurdles when doing so.

In contrast to the equity fund sector, investor wealth and education are not related to the size of the

bond fund industry (models (ii) and (iii)). While we do not have a satisfactory explanation for this

finding, it does suggest that some of the forces affecting the development of the equity and bond fund

sectors are different.

23 It turns out that this variable is highly correlated with the approvals variable, so that both variables are insignificant in the specification of model (v).

21

V. Conclusion

In this paper, we study the mutual fund industry around the world. We document the development of

the mutual fund sector relative to other ways in which households invest in financial assets. Our sample

consists of 55 countries with total mutual fund assets of $11.7 trillion at the end of 2001, held in over

55,000 funds. For the median country, the industry comprises 8.8% of GDP and 4.3% of all primary

securities. However, there is substantial cross-country variation in the development of the fund industry.

Luxembourg and Ireland are the countries with the largest industry relative to the size of the economy.

Both countries have engaged in domicile competition. Luxembourg has benefited tremendously from its

stringent bank secrecy laws whereas a favorable tax treatment of fund management companies is

responsible for the significant growth of Ireland.

For the remaining countries, a combination of demand side, supply side, and legal and regulatory

factors help explain why the fund industry is larger in some countries than in others. Our results

corroborate the findings from the law and economics literature that countries’ rules and norms affect

financial development. In particular, strong legal and regulatory factors have a positive impact on the size

of the mutual fund industry, especially fund industry regulations addressing the process approving fund

starts, mandating fee and performance disclosures, and handling conflicts of interest between the fund

management company and fund shareholders. Failure to enforce insider trading laws has a positive effect

on the size of the equity fund industry, perhaps because fund investors are less confident of trading on

their own against better-informed insiders.

Not all regulations and restrictions enhance the growth of the fund industry. Specifically, we find that

the fund sector is smaller when there are restrictions placed on banks when considering entry into the

securities business, and when it is more costly and time-consuming to set up a fund.

On the demand side, investor wealth and education have a positive impact on industry size, but only

for equity funds. In addition, the fund sector is larger in countries where a greater proportion of pension

funds are of the defined contribution type.

22

As we acknowledge elsewhere, these results are imperfect and cannot be interpreted without caution.

Our conclusions lay out working hypotheses rather than put issues to rest. For example, our finding that

some types of regulation -- typically regulations that protect fund shareholder rights -- lead to a larger

fund industry, needs substantial additional study, probably in the form of detailed country-level analysis.

Regulators are writing standards in the European Union and elsewhere to establish the appropriate forms

of protection for fund investors. With literally trillions of dollars or Euros of wealth entrusted to the fund

industry, non-partisan scholarship should inform this debate.

23

Appendix: Definitions and Sources of Potential Explanatory Variables (Names of variables used in the regression models have been italicized) Determinant Variable Source Panel A: Legal, Regulatory and Governance Characteristics General Investor Protection

Efficiency of judicial system Rule of law Corruption Risk of expropriation Risk of contract repudiation (all these variables are scaled between 1 and 10, a higher number representing a better judicial system, less corruption, and lower risk of expropriation and repudiation) Summed up value of above variables (Judicial system quality)

LLSV ( 1998)

Transparency of reporting

Accounting standards (Between 0 and 100 -- a higher number implies better standards)

LLSV (1998)

Insider trading Insider trading enforced (=1 if Yes)

Bhattacharya and Daouk (2002)

Mutual Fund Investor Protection

Does fund startup require regulatory approval? (=1 if Yes) Does the prospectus require regulatory approval? (=1 if Yes) Summed up value of above variables (Approvals) Do custodians need to be independent? (=1 if Yes) (Custodians independent) Dummy if number of required fee and performance disclosures in fund advertising and prospectus is above the median (=1 if Yes) (High disclosures)

KPMG (http://www.kpmg.ie/industries/fs/funds2002/index.htm), Thompson and Choi (2001), IOSCO (2002)

Taxation Tax rate paid by mutual fund families Opportunity for tax evasion: are bearer securities allowed? (Bearer securities allowed) (=1 if Yes)

KPMG, EIU (2003)

24

Determinant Variable Source Potential conflicts of interest between the fund and fund investors

Are there regulatory requirements or industry best practice standards on internal control? (=1 if Yes) The fund cannot have a significant participation in the company in which it invests? (=1 if Yes) Can the fund use disclosure to deal with potential conflicts? (=1 if Yes) Summed up value of the above variables (Dealing with conflicts)

Thompson and Choi (2001)

Panel B: Characteristics of Potential Fund Suppliers Concentration of banking sector

Percentage of total banking assets held by top 5 banks (Bank concentration)

Cetorelli and Gambera (2001)

Breadth of distribution channels

Number of channels available to sell funds: banks, broker-dealers, direct sales, insurance companies, financial planners (Distribution channels)

IOSCO (2002)

Restrictions for banks to enter the securities business

Categorization of degree of restrictiveness (1 – 4, 4 is most restrictive) (Securities business restrictions)

Barth, Caprior and Levin (2001)

Ease of entry into the fund industry

Cost of setting up a new fund Time required to set up a new fund Indicator variable which equals one if it takes more than 60 days to set-up a fund (High Set-up Time)

KPMG

Presence of government-supported competitive financial products

Existence of explicit deposit insurance for bank deposits (Presence of deposit insurance) (=1 if Yes)

Demirgüç-Kunt and Sobaci (2000)

25

Determinant Variable Source Panel C: Characteristics of Potential Fund Buyers Measures of economic development

Per capita GDP World Bank (2003)

Market size Population Savings / GDP

World Bank (2003), EIU (2003)

Education and literacy of population

Total years of education averaged for men and women (includes part time education) (Education) Literacy rate (%)

World Bank (2003)

Presence of information sources

Newspaper circulation/Population Number of internet users/Population

Dyck and Zingales (2003), World Bank (2003)

Allocation of pension assets

Fraction of pension plans that are defined contribution (Defined contribution funds / total pension funds)

Mercer Consulting (2003)

Panel D: Trading Characteristics Reliability of Reported Net Asset Value

Frequency of trading (annualized) (Share turnover)

World Bank (2003)

Cost of transacting in equity markets

The lowest level of commissions and fees charged to institutional investors as percentage of value traded (Lowest trading cost payable)

http://www.elkins-mcsherry.com/

26

References Bams, D. and R. Otten, 2002, European mutual fund performance, European Financial Management 8,

75-101. Barth, J.R., Caprio Jr., G., and R. Levine, 2001, The regulation and supervision of banks around the

world: A new database, World Bank working paper. Bhattacharya, U., and H. Daouk, 2002, The world price of insider trading, The Journal of Finance 57, 75-

108. Blake, D. and A. Timmermann, 1998, Mutual fund performance: Evidence for the U.K., European

Finance Review 2, 57-77. Brown, S. J., Goetzmann, W. N., Hiraki, T., Otsuki, T., and N. Shiraishi, 2001, The Japanese open-end

fund puzzle, Journal of Business 74, 59-77. Cai, J., Chan, K. C., and T. Yamada, 1997, The performance of Japanese mutual funds, The Review of

Financial Studies 10, 237-273. Cetorelli, N. and M. Gambera, 2001, Banking market structure, financial dependence, and growth:

International evidence from industry data, The Journal of Finance 56, 617-648. Dahlquist, M., Engstrom, S., and P. Soderlind, 2000, Performance and characteristics of Swedish mutual

funds, Journal of Financial and Quantitative Analysis 35, 409-423. Demirgüç-Kunt, A. and T. Sobaci, 2000, Deposit insurance around the world: A data base, World Bank

working paper. Dermine, Jean and L.H. Röller, 1992, Economies of scale and scope in french mutual funds, Journal of

Financial Intermediation 2, 83-90. Dyck, A. and L. Zingales, 2003, Private benefits of control: An international comparison, Journal of

Finance, forthcoming. Economist Intelligence Unit (EIU), 2003, CountryData database. Franks, J. R., Schaefer, S. M., and M. D. Staunton, 1998, The direct and compliance costs of financial

regulation, Journal of Banking and Finance 21, 1547-1572. International Organization of Securities Commissions (IOSCO), 2002, Performance presentation

standards for collective investment schemes. La Porta, R., Lopez-de-Silanes, F., and Shleifer, A., and R. W. Vishny, 1998, Law and finance, Journal of

Political Economy 106, 1113-1155. Mercer Consulting, 2003, Defined contribution retirement plans around the world: A guide for employers. Poterba, James M. and Andrew A. Samwick, 2003, Taxation and household portfolio composition: U.S.

evidence from the 1980s and 1990s, Journal of Public Economics 87, 5-38. Rogers, Everett M., 1995, The diffusion of innovations, 4th ed, Free Press.

27

Thompson, John K. and Sang-Mok Choi, 2001, Governance systems for collective investment schemes in

OECD countries, OECD Financial Affairs Division, Occasional Paper, No 1. World Bank, 2003, World development indicators database.

28

29

ow

Table I Size of the Mutual Fund Industry around the World at the end of 2001

This table reports the total, mean, median, standard deviation, sample size, and the high and low values of various measures of the size of the mutual fund industry (referred to as Assets under management (AUM)) around the world. The assets under management are measured relative to a country’s GDP, primary securities which include the equity markets + bond markets + bank loans, and population. AUM is also reported separately for equity funds (including balanced funds) and bonds funds (including money market funds). All asset size figures are at the end of 2001 and are in U.S. dollars. N ighTotal Mean Median Standard

Dev.H L

Assets under management (AUM) ($ billions) 11,744 192.535 10.180 896.515 61 6974.976 0AUM / GDP 0.873 0.088 5.22 58 39.913 0AUM / Primary securities 0.168 0.043 0.655 55 4.845 0AUM / Population ($ 000s) 32.439 0.585 219.145 59 1686.044 0Number of funds (N) 55,004 1018.593 288.5 1924.815 54 8307 0Average size of fund ($ millions) 91.363 47.029 135.7 49 839.650 0.417 Equity AUM – including balanced ($ billions)

5,783 160.663 9.349 570.174 36 3430.935 0Equity AUM – excluding balanced ($ billions) 4,869 118.760 8.184 480.996 41 3089.576 0Bonds AUM – including money market ($ billions) 5,340 140.529 12.904 520.082 38 3200.076 0Bonds AUM – excluding money market ($ billions) 2,359 57.540 7.434 163.713 41 1003.668 0 Equity AUM – incl. bal. / Domestic market cap. 0.626 0.120 2.568 32 14.668 0Equity AUM – excl. bal. / Domestic market cap. 0.435 0.074 2.016 36 12.182 0Bonds AUM – incl. money mkt / Total credit market 0.174 0.063 0.551 32 3.174 0Bonds AUM – excl. money mkt / Bond market 9.050 0.059 53.527 36 321.29 0

Table II

Descriptive Statistics on Explanatory Variables This table provides descriptive statistics on the various explanatory variables categorized across (i) legal, governance and regulatory characteristics, (ii) supply side characteristics, (iii) demand side characteristics, and (iv) trading characteristics. This table includes only data for countries for which fund industry size is available. The Appendix provides a description of each of the variables along with the various data sources used. Panel A: Legal, Regulatory, and Governance Characteristics Mean Median Standard

DevN Low High

Efficiency of judicial system (higher implies more efficient) 7.79 8.00 2.19 41 2.50 10.00Rule of law (higher implies better rule of law) 7.42 8.22 2.42 41 1.90 10.00Corruption (higher implies less corruption) 7.34 8.22 2.22 41 2.15 10.00Risk of expropriation (higher implies lower risk) 8.43 9.12 1.43 41 5.22 9.98Risk of contract repudiation (higher implies lower risk) 7.99 8.71 1.62 41 4.80 9.98

Judicial system quality (sum of above) 38.97 39.73 9.12 41 20.42 49.96 Insider trading laws enforced (=1 if Yes) 0.63 1.00 0.49 57 0.00 1.00Accounting standards (higher implies better standards) 63.53 64.00 11.32 36 31.00 83.00 Regulatory approval to start fund (=1 if Yes) 0.49 0.00 0.51 45 0.00 1.00Regulatory approval for prospectus (=1 of Yes) 0.98 1.00 0.15 44 0.00 1.00

Approvals (sum of above) 1.48 1.00 0.51 44 1.00 2.00 Custodians independent (=1 if Yes) 0.44 0.00 0.50 45 0.00 1.00High number of disclosures (=1 if Yes) 0.53 1.00 0.52 17 0.00 1.00 Internal control requirements / industry best practice (=1 if Yes) 0.77 1.00 0.43 26 0.00 1.00Funds cannot have significant participation (=1 if Yes) 0.92 1.00 0.27 26 0.00 1.00Disclosure employed to deal with conflicts (=1 if Yes) 0.77 1.00 0.42 27 0.00 1.00

Dealing with conflicts (sum of above) 2.44 3.00 0.71 25 1.00 3.00 Bearer securities allowed (=1 if Yes) 0.51 1.00 0.51 39 0.00 1.00Tax rate paid by fund families (%) 30.00 30.32 6.98 34 10.00 45.00

30

Table II (continued) Panel B: Supply Side Characteristics Mean Median Standard

DevN Low High

Bank asset concentration 0.68 0.70 0.21 34 0.20 0.99Presence of deposit insurance (=1 if Yes) 0.66 1.00 0.48 59 0.00 1.00Restrictions on security business (Higher is more restrictive) 1.80 2.00 0.81 41 1.00 4.00Number of distribution channels 3.65 4.00 1.17 17 1.00 5.00Time to set up new fund (days) 110.23 90.00 61.67 39 28.00 270.00Set-up time is 60 days or more (High set-up time Dummy) 0.87 1.00 0.34 39 0.00 1.00Cost to set up new fund (USD 000) 75.45 28.25 148.62 21 2.38 625.00Set-up cost / Average fund size (x 1000) 0.56 0.29 0.86 20 0.04 3.92

Panel C: Buyer characteristics

Per capita GDP (USD 000) 12.72 8.98 11.49 58 0.33 42.24Savings / GDP (%) 23.65 22.70 7.27 58 11.92 51.50Population (millions) 79.61 19.35 211.42 59 0.45 1271.23Literacy rate (%) 96.47 99.80 9.16 56 51.40 100.00Years of education 13.92 14.50 2.21 33 9.50 17.00Newspaper circulation / population

2.46 2.20 1.80 34 0.20 8.00Internet users / population 0.16 0.14 0.14 56 0.00 0.49Defined contribution pension plans / total pension plans 43.81 39.25 33.42

36 0.00 100.00

Panel D: Trading characteristics

Share turnover (%) 76.00 60.26 79.67 51 3.40 475.46Lowest trading costs payable (basis points) 36.95 31.39 19.86 39 8.35 94.06

31

Table III

Univariate Regressions Explaining the Size of the Mutual Fund Industry Across Countries

This table reports univariate OLS regressions explaining the relative size of the mutual fund industry across countries. The dependent variable is the size of the mutual fund sector as a fraction of the primary securities (equities, bonds, and bank loans) in each country. Separate regression results for equity funds (including balanced) and bond funds (including money market funds) are also reported. Industry size is measured at the end of 2001. nm indicates that the analysis is not meaningful for the specific type of fund. Luxembourg and Ireland are excluded from the analysis. The Appendix provides a description of the explanatory variables along with the data sources used. All funds Equity, including balanced Bond and Money Market Name Coefficient p-value Adj. R-sq p-valueCoefficient Adj. R-sq Coefficient p-value Adj. R-sq Judicial System Quality 0.0033 0.01 0.13 0.0095 0.02 0.14 -0.0012 0.45 -0.01 Approvals 0.0614 0.02 0.13 0.1426

0.02 0.14 0.0683 0.00 0.24High disclosures 0.0816 0.08 0.15 0.1192 0.15 0.08 0.0625 0.10 0.12 Insider trading laws enforced 0.0560 0.01 0.11 -0.0153 0.84 -0.03 nm

Nm nmAccounting standards 0.0017 0.15 0.03 0.0030 0.38 -0.01 -0.0008 0.53 0.00 Custodians independent -0.0310 0.23 0.01 -0.0714 0.26 0.01 -0.0248 0.32 0.00 Dealing with conflicts 0.0503 0.28 0.01 0.1266 0.24 0.02 nm

nm nmBearer securities allowed -0.0204 0.33 0.03 0.0883 0.14 0.05 -0.0317 0.14 0.05 Tax rate paid by fund families -0.0015 0.42 -0.01 0.0056 0.27 0.01 -0.0019 0.33 -0.00

Bank concentration -0.0561 0.40 -0.01 0.0033 0.99 -0.04 -0.1077 0.08 0.09 Presence of deposit insurance 0.0132 0.55 -0.01 -0.0911 0.25 0.01 -0.0004 0.99 -0.03Restrictions on security business -0.0129 0.41 -0.01 -0.0506 0.20 0.03 0.0003 0.98 -0.04Number of distribution channels 0.0038 0.88 0.00 0.0430 0.33 0.00 -0.0198 0.33 0.00 High set-up time -0.0862 0.09 0.06 -0.0201 0.86 -0.04 -0.0586 0.17 0.04Cost to set up new fund (USD 000s) 0.0002 0.41 -0.02 -0.0003 0.46 -0.03 0.0002 0.38 -0.01 Cost to set up new fund / average fund size (x 1000) -0.1149 0.09 0.14 nm nm nm nm nm nm

Per capita GDP (USD 000s) 0.0027 0.00 0.15 0.0056 0.05 0.09 0.0000 0.99 -0.03 Savings / GDP (%) -0.0022 0.12 0.03 -0.0024 0.53 -0.02 -0.0020 0.19 0.03Population (millions) 0.0000 0.48 -0.01 -0.0003 0.61 -0.02 0.0004 0.06 0.08 Literacy rate (%) 0.0021 0.17 0.02 0.0210 0.11 0.05 -0.0012 0.81 -0.03Years of education 0.0196 0.00 0.23 0.0455 0.03 0.14 0.0053 0.50 -0.02 Newspaper circulation / population 0.0077 0.33 -0.00 0.0197 0.30 0.00 -0.0033 0.65 -0.03Internet users / population 0.1971 0.00 0.20 0.4027 0.04 0.10 -0.0068 0.93 -0.03 Defined contribution funds / total pension funds 0.0008 0.05 0.09 0.0013 0.20 0.03 0.0002 0.68 -0.03

Share turnover 0.0001 0.58 -0.01 0.0004 0.46 -0.01 nm nm nmLowest trading cost payable (basis points) -0.0010 0.14 0.04 -0.0021 0.25 0.01 nm nm nm

32

Table IV

Explaining the Size of the Mutual Fund Industry Across Countries

This table reports multivariate OLS regressions explaining the relative size of the mutual fund industry. The dependent variable is the size of the mutual fund sector as a fraction of the primary securities (equities, bonds, and bank loans) in each country at the end of 2001. Luxembourg and Ireland are excluded from the analysis. The figures in parentheses are p-values. The Appendix provides a description of the explanatory variables along with the data sources used.

(i) (ii) (iii) (iv) (v) (vi) (vii) (viii)

Constant -0.116 -0.035 (0.17) (0.37)

-0.318 (0.00)

-0.059 (0.25)

0.007 (0.94)

0.031 (0.60)

0.032 (0.67)

0.082 (0.05)

Judicial System Quality 0.003 (0.12)

Approvals

0.071 0.059 (0.01) (0.01)

0.080 (0.00)

0.067 (0.02)

0.079 (0.10)

0.056 (0.03)

0.092 (0.05)

0.043 (0.02)

High Disclosures 0.075 (0.10)

Bearer Securities Allowed -0.040 (0.03)

High Set-up Time -0.084 (0.06)

-0.104 (0.00)

Set-up cost / Avg. fund size -0.122 (0.05)

GDP / Capita 0.002 (0.04)

0.001 (0.27)

-0.002 (0.36)

0.003 (0.02)

0.003 (0.00)

Education 0.020(0.00)

Def. contrib. / Tot. pension 0.001 (0.10)

Adjusted R-squared 0.20 0.21 0.40 0.24 0.21 0.29 0.38 0.52

N 30 38 25 30 15 32 14 27

33

Table V

Explaining the Size of the Equity Fund Sector Across Countries This table reports multivariate OLS regressions explaining the relative size of the equity mutual fund industry. The dependent variable is the size of the equity mutual fund sector (including balanced funds) as a fraction of the equity market capitalization in each country at the end of 2001. Luxembourg and Ireland are excluded from the analysis. The figures in parentheses are p-values. The Appendix provides a description of the explanatory variables along with the data sources used.

(i) (ii) (iii) (iv) (v) (vi) (vii) (viii)

Constant -0.450(0.05)

-0.116 (0.26)

-0.774 (0.04)

-0.468 (0.03)

-0.477 (0.03)

-0.737 (0.02)

-0.742 (0.04)

0.071 (0.55)

Judicial System Quality 0.010 (0.04)

0.012 (0.01)

0.011 (0.01)

0.013 (0.03)

0.013 (0.03)

Approvals

0.152(0.02)

0.135 (0.03)

0.170 (0.02)

0.195 (0.00)

0.184 (0.00)

0.204 (0.01)

0.203 (0.01)

0.159 (0.02)

Insider Trading Enforced -0.184 (0.03)

-0.206 (0.02)

-0.282 (0.01)

-0.282 (0.01)

Dealing with Conflicts 0.097 (0.08)

0.098 (0.13)

Securities Business Restrictions 0.001 (0.98)

-0.067 (0.09)

GDP / Capita 0.005 (0.10)

Education 0.049(0.04)

Def. contrib. / Tot. pension 0.002 (0.05)

Adjusted R-squared 0.25 0.19 0.28 0.37 0.45 0.45 0.41 0.20

N 27 31 23 27 26 19 19 26

34

Table VI

Explaining the Size of the Bond Fund Sector Across Countries

This table reports multivariate OLS regressions explaining the relative size of the bond mutual fund industry. The dependent variable is the size of the bond mutual fund sector (including money market funds) as a fraction of the bond and bank loan market in each country at the end of 2001. Luxembourg and Ireland are excluded from the analysis. The figures in parentheses are p-values. The Appendix provides a description of the explanatory variables along with the various data sources used.

(i) (ii) (iii) (iv) (v) (vi) (vii) (viii)

Constant 0.106 -0.010 (0.80)(0.20)

-0.082 (0.52)

-0.003 (0.94)

0.092 (0.27)

0.008 (0.78)

0.169 (0.00)

0.046 (0.23)

Judicial System Quality -0.003 (0.09)

Approvals

0.068 0.070 (0.01) (0.04)

0.069 (0.01)

0.070 (0.01)

0.040 (0.20)

0.052 (0.01)

0.062 (0.01)

Bearer Securities Allowed -0.040 (0.05)

-0.052 (0.02)

-0.058 (0.02)

Bank Concentration -0.096 (0.16)

-0.114 (0.02)

Sec. Business Restrictions -0.011 (0.46)

-0.024 (0.09)

GDP / Capita -0.001 (0.38)

Education 0.004(0.64)

Adjusted R-squared 0.29 0.23 0.23 0.21 0.20 0.23 0.31 0.28

N 26 29 22 26 23 25 21 23

35