Mutual funds are gaining importance worldwide and the mutual fund industry has registered a spectacular growth in the entire world. The industry has doubled its size from 9.6 trillion in 1998 to 18 trillion in 2005, corresponding to an annual growth rate of 9% according to data of the Investment Company Institute1 (ICI). Although the largest industry is still located in the USA, European and Asian countries are closing the gap. The growth rate of the European (12%) and Asian (10%) mutual fund industry in the same period has surpassed that of the USA (7 % ). Despite its importance, the mutual fund industry outside the USA has received little academic attention. This study intends to fill this gap. Using a new database, this paper compares the mutual fund industry worldwide. The first part of the research uses unique features of the database and characterises the structure of the world mutual fund industry assessing aspects such as: age, asset shares, charges, industry concentration
See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/227373211 The Size and Structure of the World Mutual Fund Industry Article in European Financial Management · January 2009 DOI: 10.1111/j.1468-036X.2007.00428.x · Source: RePEc CITATIONS READS 22 155 1 author: Sofia B Ramos École Supérieure des Sciences Economiques et Commerciales 41 PUBLICATIONS 344 CITATIONS SEE PROFILE All content following this page was uploaded by Sofia B Ramos on 23 February 2017 The user has requested enhancement of the downloaded file All in-text references underlined in blue are added to the original document and are linked to publications on ResearchGate, letting you access and read them immediately European Financial Management, Vol 15, No 1, 2009, 145–180 doi: 10.1111/j.1468-036X.2007.00428.x The Size and Structure of the World Mutual Fund Industry Sofia B Ramos IBS- ISCTE Business School/CEMAF Av For¸cas Armadas, edif´ıcio ISCTE, 1649-026 Lisbon, Portugal E-mail: sofia.ramos@iscte.pt Abstract This paper analyses the mutual fund industry for 20 countries using a new database of more than 50,000 mutual funds The results suggest that more developed industries provide more benefits to investors as they diversify more internationally, charge lower annual charges and present more product sophistication The results also have important policy implications by emphasising the role of competition and contestability in industry development Fewer barriers to entry are positively associated with a larger industry, and concomitantly with more efficiency in terms of returns and fees Keywords: Mutual Funds, Competition, Mutual Fund Industry, Entry Barriers JEL classification: G15, G23 Introduction Mutual funds are gaining importance worldwide and the mutual fund industry has registered a spectacular growth in the entire world The industry has doubled its size from $9.6 trillion in 1998 to $18 trillion in 2005, corresponding to an annual growth rate of 9% according to data of the Investment Company Institute (ICI) Although the largest industry is still located in the USA, European and Asian countries are closing the gap The growth rate of the European (12%) and Asian (10%) mutual fund industry in the same period has surpassed that of the USA (7%) Despite its importance, the mutual fund industry outside the USA has received little academic attention This study intends to fill this gap Using a new database, this paper compares the mutual fund industry worldwide The first part of the research uses unique features of the database and characterises the structure of the world mutual fund industry assessing aspects such as: age, asset shares, charges, industry concentration I would like to thank to an anonymous referee, John Doukas (the editor), Miguel Ferreira and Ant´onio Freitas Miguel for helpful comments All errors are of my responsibility Financial support from Fundacao para a Ciencia e Tecnologia is greatly acknowledged (POCI/EGE/57002/2004) See www.ici.org C 2007 The Author Journal compilation C 2007 Blackwell Publishing Ltd 146 Sofia B Ramos and competition, among others The second part tries to understand the explanation for the cross-country differences of the world mutual fund industry This research draws on two major groups of previous studies on the mutual fund industry: first, studies that address individual countries (McDonald (1973) and Dermine and Roller (1992) for France, and Ward and Saunders (1976) for the UK), second, studies that have already addressed international differences in the industry For instance, Otten and Schweitzer (2002) compare the industry in the USA and five European countries in the late nineties Klapper et al (2004) study the determinants of mutual fund growth in the period 1992–98 for 40 developed and developing countries Khorana et al (2005) analyse cross-country differences in the relative size of the mutual fund industry in a sample of 56 countries They find that the mutual fund industry is larger in developed countries and countries with a strong institutional environment The industry is smaller in countries where entry barriers are higher, measured by the effort to set up a new fund The fund industry is larger in countries with a more educated population, and where the industry itself is older This study departs from previous ones by using a novel database of more than 50,000 mutual funds around the world Although the data does not cover a large number of countries as other studies (e.g Khorana et al., 2005), it provides a deeper look into the industry, offering new insights into the industry structure and documenting important country and regional characteristics The main results are as follows: maturity (age) is an important factor explaining industry development, measured by the ratio of the mutual fund industry to GDP or the value of mutual funds per capita The investment industry needs large and liquid financial markets (Klapper et al., 2004), which in turn need time to grow as they benefit from positive network externalities However, cross-country differences in fund availability or with the weight of the industry to capital markets not show association with age This weak association with age is likely to come from the fact that in most countries, financial reforms fostering capital market and mutual fund industry development happened at the same time; therefore, when adjusting the development of the mutual fund industry for capital market development, the relation with age vanishes, as both are age dependent More developed industries are characterised by a higher share of equity mutual funds They also show less home bias, which might imply that larger industries enjoy better the benefits of international diversification and those funds achieve a better risk-return trade-off Countries where the industry is larger are associated with lower annual charges, consistent with mutual fund economies of scale However, more developed industries seem to impose larger initial and/or redemption charges This finding suggests that industries in an early stage of development have ‘fewer entry barriers’ in order to attract investors to the industry and by dissuading investors from redeeming shares, i.e ‘increasing barriers to departure’, a mutual fund is able to invest in a more risky portfolio thus enhancing performance More developed industries show a large market share of funds-of-funds, index funds, institutional funds, bond funds as well as new products such as ethical funds, and a small share of close-end funds The results highlight strong regional differences Europe presents higher levels of industry concentration than Asia and the USA In spite of concentration, Europe has the largest number of competing firms per million population, 5.2 against 1.95 in Asia and 2.19 in the USA, and a high level of industry contestability: the highest ratio of new funds and new companies per million population, which may be a result of the Investment C 2007 The Author Journal compilation C 2007 Blackwell Publishing Ltd The Size and Structure of the World Mutual Fund Industry 147 Services Direct that allows European Union (EU) companies to have an EU ‘passport’ and making the process of registering in a new country easier Notwithstanding, the high level of competition is not reflected in charges, as the annual average mutual fund charge in European and Asian countries is twice that of the USA The gap in fees may be explainable by lower average size of funds, larger distribution costs and less ‘investor-driven’ competition Concerning industry determinants, both the mutual fund industry and the sector of equity mutual funds are larger in countries with a wealthier population and where the industry is older Similar to other studies, legal factors like investor protection and insiders trading laws are important; however, industry age and country wealth have greater explanatory power In addition, there is a strong association between industry competition and industry development Larger industries have a larger number of competing companies and more contestability Therefore, the existence of entry barriers in the industry seems to hinder industry development Overall, the evidence suggests that investors are better off in more developed industries as they provide a better risk-return trade-off Mainly funds in more developed industries charge lower annual charges due to economies of scale and are likely to enjoy the gains of international diversification The results have important policy implications by emphasising the role of competition and contestability in industry development Fewer entry barriers are positively associated with a larger industry, and concomitantly with more efficiency for investors in terms of returns and fees The paper is organised as follows Section describes the data and the main indicators of this study Section characterises the features of the world mutual fund industry Section describes the potential determinants of the industry’s development Section analyses cross-country differences in the industry and their determinants Section concludes Data and Summary Statistics Data is drawn from Lipper Hindsight, an extensive database of more than 80,000 onshore and off-shore mutual funds To characterise the mutual fund industry around the world, I use information about more than 50,000 funds of 20 countries The sample countries are: Austria, Belgium, China, Finland, France, Germany, India, Italy, Japan, (South) Korea, Malaysia, Poland, Portugal, Singapore, Spain, Switzerland, Taiwan, Thailand, the UK and the USA) The information collected is from the year 2005 Individual funds are then aggregated by domicile country in order to give the size of the national mutual fund industry Some caveats are in order on this issue First, the mutual fund industry has global scope and many management companies sell funds in several countries It is also quite common that companies have funds domiciled abroad as off-shores Second, investors’ holdings in a country can be larger than the size of the national mutual fund industry since investors can hold mutual funds domiciled in other countries Therefore, the definition of size of the mutual fund industry adopted in this study refers to funds domiciled in the country, not to the assets managed by firms in the country or the holdings of country investors in mutual funds Table describes the main features of this sample, like the aggregated value of the industry and the number of funds in each country The total size of the mutual fund See also Khorana et al (2005) for a similar approach C 2007 The Author Journal compilation C 2007 Blackwell Publishing Ltd 148 Sofia B Ramos industry is the sum of the net assets of all open-end funds domiciled in the country The total funds add up to more than $11 trillion In absolute terms, the mutual fund industry is larger in the USA, accounting for 65% of the value of assets, followed by France that represents 9% The European industry represents around 28% of the value of the assets, while Asia only 6% The mutual fund industry is smaller in countries like Malaysia, Thailand and Poland Results are consistent with previous studies that find that the European mutual fund industry is lagging behind the American (Otten and Schweitzer, 2002) Lipper distinguishes between primary funds and sub-funds This distinction arises because many countries allow funds to break up into several classes Usually funds that offer multiple share classes represent ownership interests in the same portfolio, but have a different fee structure The total number is 52,286 funds of which 33,182 funds are primary Again, the USA has the largest number of funds, 35% of the total number of funds (20% of primary), followed by the UK and Korea, while Poland has the smallest number of funds European countries account for 40% of the total number of funds Previous studies have already reported that the number of funds was much larger in Europe than in the USA in 1997, which results in a smaller average size of the funds in Europe (see e.g Otten and Schweitzer, 2002; Otten and Bams, 2002) Table also reports the number and market value of Undertakings for Collective Investment in Tradable Securities (UCITS) The UCITS are collective funds that can be sold across national boundaries within the European Union (EU) in accordance with the ‘Undertakings for Collective Investment in Tradable Securities’ Directive These funds can be marketed within all countries that are a part of the European Union, provided that the fund and fund managers are registered within the domestic country The majority of funds domiciled in EU countries are also UCITS and account for 77% of the value of the EU funds Only Poland has a small number of UCITS, explained by their late entry into EU Characteristics of the Mutual Fund Industry Worldwide This section explores information on industry age, asset structure, charges, geographic focus, industry concentration and competition and fund strategies of the mutual fund industry Development of the mutual fund industry Industry size The figures presented in Table need to be compared with the wealth and financial development of the countries The development of the mutual fund industry is measured using the following indicators: the ratio of the size of mutual fund industry to the Gross Domestic Product (MF/GDP), which takes into account the total wealth of the country; the mutual fund value per capita (MFpc), which considers the dollar amount that each individual invests on average in the domestic industry; the ratio of the size of the mutual fund industry to the size of bond and stock markets (MF/SBM) that compares the industry with the size of the securities markets, adjusting for the general development The coverage of funds by Lipper Hindsight was compared with the statistics on mutual funds provided by the Investment Company Institute (see www.ici.org) The total net asset value in our sample is $11.5 trillion, which compares with $13.1 trillion ICI for the countries included in our sample C 2007 The Author Journal compilation C 2007 Blackwell Publishing Ltd The Size and Structure of the World Mutual Fund Industry 149 Table Mutual fund industry around the world: descriptive statistics This table presents the sample main descriptive statistics on (1) the industry value, which equals the total net asset value of all open-end funds (in USD million), (2) the number of funds, distinguishing between primary and non-primary funds (Total refers to sum of primary and non-primary funds), (3) the Undertakings for Collective Investment in Tradable Securities (UCITS) presenting the number and total value of funds (in USD million) The latter information refers only to EU countries The last rows present descriptive statistics Data is from Lipper Hindsight Number Funds Countries Industry value (USD million) Austria Belgium China Finland France Germany India Italy Japan Korea Malaysia Poland Portugal Singapore Spain Switzerland Taiwan Thailand UK USA Total Mean Standard Deviation Primary Total UCITS Number Value (USD million) 97,868 98,518 44,474 50,520 986,627 388,349 39,445 453,762 371,816 174,106 12,382 16,542 35,741 29,792 309,009 145,980 61,275 13,061 649,010 7,567,572 1,864 976 167 430 4,916 1,190 557 1,028 2,635 4,916 378 114 232 599 2,691 610 502 497 2,089 6,791 2,683 1,264 172 753 5,750 1,211 1,454 1,087 2,635 5,059 1,905 123 232 754 2,692 898 505 497 4,168 18,444 2,281 508 95,957 49,209 599 2,214 1,081 43,579 505,563 362,661 959 440,036 218 2,034 32,480 2,692 309,009 3,380 546,943 11,545,848 577,292 1,664,986 33,182 1,659 1,875 52,286 2,614 4,062 13,937 1,394 1,160 2,387,470 238,747 216,016 of financial markets and the number of funds per million inhabitants (N Funds/Pop), that compares fund supply and fund diversity in a country Table provides an overview of the development of the mutual fund industry around the world An analysis of the importance of the mutual fund industry in GDP or MFpc reveals that it is most developed in the USA but is at an earlier stage in China In European countries like France, Austria and Switzerland, the mutual fund industry represents a large proportion of GDP and presents a large per capita value In Asia, the countries with a more developed industry are Singapore and Korea By weighting the size of the mutual fund industry against the size of securities markets, a correction is made for countries whose securities markets are small in relation to GDP From this point of view, Austria has the largest industry and Malaysia the smallest Note that a large value of this ratio might be indicative of a high level of investment in C 2007 The Author Journal compilation C 2007 Blackwell Publishing Ltd 150 Sofia B Ramos Table The development of the mutual fund industry around the world This table presents descriptive statistics on indicators of the development mutual fund industry: (1) MF/GDP – The ratio of the value of the mutual fund industry to the GDP (in %); (2) MFpc – The mutual fund value per capita (in USD); (3) MF/SBM – The ratio of the value of the mutual fund industry to the value of bond and stock markets (in %) and (4) N Funds/Pop – The total number funds per million inhabitants The last rows present descriptive statistics For two countries there is no data on the size of stock and bond markets Data sources are in Appendix A MF/GDP MFpc MF/SBM N Funds/Pop Countries Austria Belgium China Finland France Germany India Italy Japan Korea Malaysia Poland Portugal Singapore Spain Switzerland Taiwan Thailand UK USA 32.4% 27.2% 2.0% 27.2% 47.7% 14.0% 5.3% 27.1% 8.3% 22.2% 9.9% 5.7% 20.6% 37.2% 28.5% 40.7% 10.9% 7.3% 30.3% 60.4% 10, 184 7, 996 29 8, 181 13, 888 3, 969 31 6, 656 2, 458 3, 054 415 366 2, 892 5, 804 6, 320 16, 699 2, 166 177 9, 226 21, 773 32.9% 10.2% NA 17.4% 26.5% 11.5% 6.7% 15.5% 3.3% 17.0% 4.1% 10.1% 16.9% 17.9% 17.6% 14.4% NA 6.5% 18.2% 20.9% 330.62 121.48 0.13 144.39 95.85 14.66 1.35 18.88 20.62 105.08 75.57 3.22 22.23 173.93 65.20 121.65 21.13 7.97 70.16 62.84 Mean Standard Deviation Max Min Median 23.2% 15.7% 60.4% 2.0% 24.7% 6, 114 5, 984 21, 773 29 4, 886 14.9% 7.6% 32.9% 3.3% 16.2% 73.85 80.12 330.62 0.13 64.02 foreign securities markets, which in turn is dependent on the level of barriers to capital movements The supply of funds per inhabitant is largest in Austria, Singapore, Switzerland, Belgium and Finland, countries with smaller populations, while China and India (overpopulated countries) have the lowest values The mutual fund industry is also compared with the development of financial markets Indicators are Private Credit by Deposit Money Banks and Other Financial Institutions to GDP (Private Credit/GDP), the ratio of Stock Market Capitalisation to GDP (SMC/GDP), the ratio of Private Bond Market Capitalisation to the GDP (Private Bond/GDP) and the ratio of Public Bond Market Capitalisation to the GDP (Public Bond/GDP) and stock and bond market capitalisation to GDP (SBM/GDP) All ratios are from Beck et al (2000) database on international development of financial markets C 2007 The Author Journal compilation C 2007 Blackwell Publishing Ltd The Size and Structure of the World Mutual Fund Industry 151 Table The development of the world mutual fund industry and financial markets This table presents the correlation between the indicators of the development of mutual fund industry and the development of financial markets Columns are: (1) MF/GDP – The ratio of the value of the mutual fund industry to the GDP (in %); (2) MFpc – The mutual fund value per capita (in USD); (3) MF/SBM – The ratio of the value of the mutual fund industry to the value of bond and stock markets (in %) and (4) N Funds/Pop -The total number funds per million inhabitants Rows are: Private credit by deposit money banks and other financial institutions to the GDP (Private Credit/GDP), the ratio of stock market capitalisation to the GDP (SMC/GDP); the ratio of private bond market capitalisation to the GDP (Private Bond/GDP); the ratio of public bond market capitalisation to the GDP (Public Bond/GDP) and the ratio of stock and bond market capitalisation to GDP (SBM/GDP) Data sources are in Appendix A MF/GDP Panel A: Mutual Fund Industry Development MF/GDP 1.00 MFpc 0.94 MF/SBM 0.73 N Funds/Pop 0.51 Panel B: Financial Market Development Private Credit/GDP 0.62 SMC/GDP 0.49 Private Bond/ GDP 0.61 Public Bond/ GDP −0.06 SBM/GDP 0.52 MFpc MF/SBM N Funds/Pop 0.94 1.00 0.62 0.44 0.73 0.62 1.00 0.69 0.51 0.44 0.69 1.00 0.60 0.52 0.62 −0.01 0.57 0.29 −0.10 0.26 −0.26 −0.10 0.13 0.24 0.11 −0.16 0.11 Table reports the correlation between mutual fund industry and the financial indicators Correlations show that larger values of MF/GDP and MFpc are associated with credit and securities markets development Thus, countries where credit markets and stock markets are more developed also have a more developed mutual fund industry Although positive, correlation is lower for the indicators MF/SBM and N Funds/Pop, indicating that cross-country differences in those indicators are less likely to be related to cross-country differences on the general development of financial markets Asset structure Mutual funds are also categorised according to the asset class Categories include bond, equity, mixed assets, money market, real estate and other mutual funds Asset shares are computed aggregating funds by each asset type in each country Table presents shares of asset classes in the country industries The distribution of asset types within countries varies greatly; however, on average bond and equity funds are always the most important classes of assets Bond mutual funds have an average weight of 27% in our sample Investors in Thailand and Taiwan seem to prefer these funds as bond funds present market shares of 70%, which is well above the average, while in China and Korea the average weight is smaller at below 7% Equity mutual funds have an average weight of 28% In the UK and the USA, equity funds represent more than 50% of the industry assets, while in China and Portugal they C 2007 The Author Journal compilation C 2007 Blackwell Publishing Ltd 152 Sofia B Ramos Table Weights of asset classes in the mutual fund industry This table presents the relative weight of each asset class in a country’s mutual fund industry in Panel A Panel B reports the descriptive statistics for the sample and Panel C reports the correlations with the indicators of the development of the mutual fund industry: (1) MF/GDP – The ratio of the value of the mutual fund industry to the GDP (in %); (2) MFpc – The mutual fund value per capita (in USD); (3) MF/SBM – The ratio of the value of the mutual fund industry to the value of bond and stock markets (in %) and (4) N Funds/Pop -The total number funds per million inhabitants Data is from Lipper Hindsight Asset Classes Bond Equity Panel A: Countries Austria Belgium China Finland France Germany India Italy Japan Korea Malaysia Poland Portugal Singapore Spain Switzerland Taiwan Thailand UK USA 50% 15% 2% 21% 11% 21% 27% 35% 45% 7% 22% 33% 31% 17% 18% 29% 71% 52% 16% 16% 21% 28% 5% 36% 23% 38% 34% 20% 39% 12% 52% 13% 7% 34% 11% 40% 20% 16% 70% 51% 14% 31% 34% 13% 10% 8% 9% 22% 7% 10% 14% 40% 18% 21% 15% 16% 8% 14% 10% 8% 10% 3% 56% 28% 46% 10% 30% 23% 0% 27% 11% 14% 30% 1% 29% 8% 0% 13% 1% 24% 1% 0% 0% 0% 0% 22% 0% 0% 3% 0% 0% 0% 10% 17% 2% 1% 1% 0% 3% 1% 3% 22% 3% 2% 10% 1% 0% 0% 5% 44% 1% 0% 4% 11% 24% 7% 1% 6% 0% 0% Panel B: Descriptive Average Standard Deviation Max Min Median statistics 27% 17% 71% 2% 22% 28% 17% 70% 5% 26% 16% 9% 40% 7% 14% 18% 16% 56% 0% 13% 3% 6% 22% 0% 0% 7% 11% 44% 0% 3% −0.24 −0.24 −0.11 −0.08 0.03 0.01 0.33 −0.21 0.01 −0.10 0.00 −0.01 0.13 −0.02 0.09 0.21 Panel C: Correlations MF/GDP −0.30 MFpc −0.22 MF/SBM −0.18 N Funds/Pop −0.01 C 2007 The Author Journal compilation C 0.32 0.40 −0.13 0.12 2007 Blackwell Publishing Ltd Mixed assets Money market Real estate Others The Size and Structure of the World Mutual Fund Industry 153 have an average weight of 5% and 7% Otten and Schweitzer (2002) also report that in Anglo-Saxon countries equity funds tend to dominate the market, reporting a figure of 50% for the USA in 1997 In 2005, equity funds represent 51% for the USA in contrast with 28% and 27% of European and Asian countries respectively Funds that mix several kinds of assets have an average weight of 16% But this figure is higher in countries like Poland (30%) and smaller in Japan, Germany, the USA, Taiwan, and India (less than 10%) Money market funds represent a share of 18% on average in a country’s mutual fund industry, and are very important in China and France with market shares around 50% In contrast, the importance of money market mutual funds is very small in Japan, Taiwan, the UK and Singapore, with less than 3% Real estate mutual funds have an average weight of 3% although the weight is heavier in countries like Germany, Portugal and Singapore Finally, Panel C analyses the correlation with industry development indicators More developed industries tend to have a higher share of equity mutual funds and a lower share of bond and mixed assets funds Geographic focus Lipper identifies the geographic focus of the mutual fund, which can be a country, a region or even the ‘world’ like global funds Table reports the percentage of net assets invested geographically The table is divided into two panels, one reporting when the geographic focus is an individual country (Panel A) and the second when it is a region (Panel B) Columns add up to 100% Funds in China and India focus exclusively on their home country Funds in countries like Korea, Malaysia, Poland, Taiwan and Thailand invest more than 80% of their assets in their home market Many companies cannot have large holdings in financial assets abroad or are even constrained by their governments from investing outside the country On the contrary, the industry in Austria, Belgium, Finland, France, Germany, Italy and Singapore invest only a small fraction of their assets in the home country Except for countries like Poland, the UK and Switzerland, European funds show a low level of home bias, which maybe explained by the fact they share the euro currency On the other hand, Asian countries have a high level of home bias, the exception are economically open countries such as Singapore and Japan The USA also has a low level of home bias Panel B highlights regional preferences For instance, USA mutual funds invest 38% domestically and more than 50% in North America Funds in European countries tend to invest substantially in Europe, except for Poland, the UK and Switzerland where, as previously reported, they tend to invest domestically In Asia, only Singapore diversifies substantially in other Asian countries Industries located in small countries like Austria, Belgium or Singapore make substantial investments globally but Japan also invests substantially in global funds The correlation between the degree of home bias and the diversification in own region with the development of the industry (not reported in the table) shows that less developed industries tend to invest more domestically (average correlation −0.55), while Otten and Bams (2002) advocate that the importance of bond funds in Europe is due to a different equity culture, a strong presence of banks and a different pension system C 2007 The Author Journal compilation C 2007 Blackwell Publishing Ltd 166 Sofia B Ramos Table 10 Country determinants of the mutual fund industry This table presents country values for the determinants of the mutual fund industry Appendix A provides a description of the variables along with their data sources Last rows present descriptive statistics Investor CIFAR IT index IT enf protection Banking restrict Age GDPpc Education Austria Belgium China Finland France Germany India Italy Japan Korea Malaysia Poland Portugal Singapore Spain Switzerland Taiwan Thailand UK USA 47.36 47.43 NA 48.82 44.87 46.83 30.61 39.73 46.86 33.55 38.54 NA 39.03 44.95 39.35 49.96 40.4 29.67 47.01 47.61 62 68 NA 83 78 67 61 66 71 68 79 NA 56 79 72 80 58 66 85 76 3.19 2.80 4.62 2.61 3.07 2.48 4.53 3.87 2.84 4.09 4.47 3.82 3.56 2.44 3.45 3.02 4.70 4.73 2.26 2.62 NA 1 0 1 NA 1 1 1 1.3 2.3 3.5 1.8 1.5 1.3 2.5 2.5 3.3 2.3 2.5 2.5 2.3 1.8 1.3 2.3 1.3 1956 1947 2001 1987 1964 1949 1964 1983 1965 1992 1959 1992 1986 1959 1958 1931 1984 1995 1934 1924 31,265 30,509 5,559 28,949 28,637 28,654 3,072 27,692 29,392 19,019 9,573 12,008 17,859 27,316 23,242 33,636 25,168 8,176 29,483 39,732 0.96 0.99 0.84 0.99 0.97 0.96 0.61 0.95 0.94 0.97 0.83 0.96 0.97 0.91 0.97 0.96 Average Standard Deviation Max Min 42.37 6.3 71 3.46 0.84 0.67 0.49 2.22 0.68 1967 22,947 23 10,284 0.93 0.09 49.96 29.67 85 56 4.73 2.26 3.5 1.3 2001 39,732 1924 3,072 0.99 0.61 0.86 0.99 0.97 The mutual fund industry is also closely related to the banking industry In continental Europe, banks are commonly portfolio managers, the primary promoters and the distributors of funds But there are countries where banks are not allowed to enter the securities business The entry barriers might create inefficiencies and hinder industry development Previous results have shown that restrictive banking regulations tend to have undeveloped financial systems (Demirg¨uc¸ -Kunt and Levine, 2000) Similarly to Khorana et al (2005), a variable is used to indicate whether a bank faces a restriction to enter the other financial activities like securities activities, and thereby offering competition to mutual fund management companies (Banking restrict) This variable is from Barth et al (2001) The higher the value, the higher the level of restrictions faced by banks Banking restrictions are less common in European countries where the average value is 1.8, and more common in the USA and Asia C 2007 The Author Journal compilation C 2007 Blackwell Publishing Ltd The Size and Structure of the World Mutual Fund Industry 167 Other variables Finally, variables like industry age, education, and GDP per capita are also considered Countries with an older industry history are likely to have larger industries, as investors need time to become familiarised with financial innovations Industry age (Age) is the year the first mutual fund was sold according to Khorana et al (2005); a negative coefficient is expected, as younger industries should be less developed The investment in financial assets is also dependent on the financial wealth of investors; it is natural than richer countries have more money invested in financial assets Demirg¨uc¸ -Kunt and Levine (2000) find that financial systems on average are more developed in richer countries and Khorana et al (2005) also find that the mutual fund industry is larger in developed countries The level of a country’s development is measured by the Gross Domestic Product per capita (GDPpc) According to previous studies, the sign of the coefficient should be positive The degree to which new products are embraced is dependent on investors’ financial knowledge, which in turn is dependent on investors’ education and sophistication The general education level of a country is measured by the United Nations Development Report Education Index The industry first started in the USA in 1924 Asian countries have a younger industry On average, investors are richer in the USA, followed by Europe and then Asia Also European countries present higher education levels than in Asian countries Table 11 reports the results for the correlations In general, countries with better investor protection also have lower insider trading, and are wealthier countries with an older mutual fund industry Countries with better investor protection present lower average market shares but also a large number of firms and new firms per million population and higher total charges Countries where insider trading is more common also have a high percentage of new funds and more banking restrictions If insider trading laws are enforced it tends to produce on average lower annual charges Correlation is also high between some competition indicators New firms/Pop and New funds/Pop (0.97) In addition, countries where a large percentage of existing funds are new and with higher market shares are also the ones where the industry is younger, and with a lower GDPpc Because some of the variables are highly correlated they cannot be included in the same regression Empirical Results This section makes an empirical study on the determinants of the development of the mutual fund industry using univariate and multivariate regressions Univariate analysis This section analyses the determinants of the mutual fund industry using a univariate regression Table 12 reports the summary results All the models are estimated using Other indicators such as the Human Development Index, the number of personal computers per thousand inhabitants and the number of internet users per thousand inhabitants were also considered All these measures were highly correlated with GDPpc, and they were not included in the research C 2007 The Author Journal compilation C 2007 Blackwell Publishing Ltd C 2007 The Author Journal compilation C Investor protection CIFAR IT index IT enf Av mk sh CR5 N firms/Pop New funds/N funds New funds/N firms New funds/Pop New firms/N firms New firms/Pop Banking restrict Annual c Total c Age GDPpc Education 10 11 12 13 14 15 16 17 18 1.00 −0.05 −0.85 0.38 −0.64 0.18 0.58 −0.47 −0.41 0.29 0.22 0.51 −0.34 −0.07 0.58 −0.62 0.89 0.46 1.00 −0.18 0.04 −0.14 −0.33 −0.27 0.00 0.06 −0.02 0.03 −0.18 0.15 0.14 0.40 0.14 −0.06 0.04 1.00 −0.41 0.48 −0.14 −0.47 0.54 0.18 −0.33 −0.03 −0.38 0.52 0.17 −0.52 0.65 −0.80 −0.51 1.00 −0.17 −0.15 0.02 −0.20 0.20 −0.06 0.26 −0.04 −0.03 −0.54 −0.17 −0.15 0.44 0.39 1.00 0.42 1.00 −0.35 0.20 1.00 0.30 −0.01 −0.05 1.00 0.03 −0.16 −0.19 0.48 1.00 −0.28 0.11 0.78 0.23 0.35 −0.20 0.04 −0.04 0.26 0.02 −0.31 0.21 0.97 0.05 −0.13 0.30 −0.26 −0.59 0.27 0.08 0.34 0.25 −0.12 0.07 −0.27 −0.17 0.10 0.32 −0.42 −0.53 0.61 0.09 −0.35 0.58 0.27 −0.63 0.00 0.50 −0.64 −0.10 −0.07 0.39 0.25 −0.38 0.12 11 12 13 14 15 16 1.00 −0.02 1.00 0.82 0.01 1.00 −0.47 −0.07 −0.55 1.00 −0.20 −0.07 −0.11 −0.05 1.00 0.13 0.11 0.33 −0.36 0.41 1.00 −0.14 0.11 −0.25 0.42 0.41 −0.37 1.00 0.38 −0.10 0.43 −0.37 −0.33 0.29 −0.63 0.31 0.02 0.23 −0.40 0.04 0.20 −0.04 10 1.00 0.60 17 1.00 18 This table presents correlation between country determinants of the mutual fund industry Appendix A provides a description of the variables along with their data sources Table 11 Correlation between country determinants of the mutual fund industry 168 Sofia B Ramos 2007 Blackwell Publishing Ltd C 2007 The Author Journal compilation C Investor protection CIFAR IT index IT enf Av mk sh CR5 N firms/Pop Banking restric New firms/N firms New firms/Pop New funds/N firms New funds/N Funds New funds/Pop Annual c Total c Age GDPpc Education Variables R2 0.33 0.00 0.41 0.05 0.32 0.02 0.22 0.18 0.08 0.14 0.00 0.31 0.17 0.14 0.07 0.40 0.60 0.22 coeff 0.01∗∗ −0.00 −0.12∗∗∗ 0.07 −5.16∗∗∗ −0.12 0.02∗∗ −0.10∗ −0.69 0.17 −0.00 −3.72∗∗ 0.01∗ −0.23 0.13 −0.00∗∗∗ 0.00∗∗∗ 0.69∗ MF/GDP 629.69∗∗∗ −62.03 −4447.73∗∗∗ 3532.69 −212030.84∗∗∗ −2621.98 596.65∗∗ −3351.63 −19028.76 6490.28 −341.50 −149328.71∗∗∗ 275.94 −10007.92∗ 4320.25 −186.11∗∗∗ 0.47∗∗∗ 23842.99∗ coeff MFpc 0.44 0.03 0.39 0.08 0.37 0.01 0.22 0.14 0.04 0.15 0.03 0.34 0.09 0.18 0.05 0.50 0.65 0.20 R2 0.00 0.00 −0.04∗ −0.01 −1.91∗ −0.08 0.01∗∗∗ −0.07∗∗ −0.02 0.12∗∗∗ −0.00 −0.98 0.01∗∗∗ −0.03 0.07 −0.00 0.00∗∗ 0.30 coeff MF/SBM 0.14 0.00 0.16 0.00 0.21 0.03 0.36 0.30 0.00 0.37 0.00 0.08 0.42 0.01 0.08 0.04 0.30 0.13 R2 5.82∗ −0.05 −39.56∗ −5.66 −1776.14 66.28 15.23∗∗∗ −62.99∗∗ −77.58 207.58∗∗∗ 1.94 206.12 11.82∗∗∗ −60.43 80.27 −1.12 0.00∗∗ 254.05 coeff N Funds/Pop 0.21 0.00 0.17 0.00 0.14 0.02 0.80 0.28 0.00 0.86 0.01 0.00 0.88 0.04 0.10 0.10 0.22 0.08 R2 This table displays results of a univariate OLS regression on mutual fund industry development indicators Dependent variables are: (1) MF/GDP- The ratio of the value of the mutual fund industry to the GDP (in %); (2) MFpc – The mutual fund value per capita (in USD); (3) MF/SBM- The ratio of the value of the mutual fund industry to the value of bond and stock markets (in %) and (4) N Funds/Pop – The total number funds per million inhabitant Appendix A provides a description of the variables along with their data sources All regressions are run with an intercept Columns present the coefficients and R squares (R2 ) Superscripts ∗ , ∗∗ , ∗∗∗ denote statistical significance at 10%, 5%, and 1% levels respectively Table 12 Univariate regression of the determinants of the mutual fund industry The Size and Structure of the World Mutual Fund Industry 2007 Blackwell Publishing Ltd 169 170 Sofia B Ramos ordinary least squares with an intercept although this is not reported The table reports the estimated coefficients, their statistical significance and the R-square The results confirm the importance of legal variables, although their effect is stronger for MF/GDP and MFpc Better investor protection and better insider trading protection are associated with more developed industries Despite feeling more protected, investors still look for the services of financial intermediaries However, accounting standards are not found to be statistically significant for any of the variables The analysis of the impact of industry concentration shows that countries with higher average market share have less developed industries However, industry development is not associated with large market shares In contrast, there is a strong relation between industry competition variables and industry development The number of competing firms per million population is positively related with industry development, and banking restrictions have a negative effect on the size of the industry Industry contestability also affects industry development A large number of new funds and new firms per million inhabitants positively affect MF/SBM These variables are even more significant for general fund supply (N funds/Pop) where the R-square is higher than 0.8 Countries with a large percentage of new funds are the ones where the industry is less developed However, these indicators correlate strongly with industry age and countries with younger industries are also likely to have a less developed industry Surprisingly, there is no evidence of a relation between charges and industry development Age and Education are important in explaining the weight of the mutual funds to the GDP and MFpc, but they are not relevant for the other indicators Not surprisingly, GDPpc is a common explanatory variable to all dependent variables; wealthier populations can invest more in financial assets, and hence the industry is larger Overall, coefficient signs are in line with our expectations, although sometimes they are not statistically different from zero (e.g CIFAR, IT enf , CR5, charges, New firms/N firms, New funds/N firms) Multivariate analysis This section analyses the determinants of the mutual fund industry using a multivariate regression The empirical analysis is guided by concerns about the lack of sample observations comparative to a large number of explanatory variables, the existence of missing observations and correlation between explanatory variables 10 The model selection is guided by these concerns and the preliminary results of the univariate analysis Development of the mutual fund industry Table 13 reports a summary of the results of the multivariate regression All regressions were estimated with an intercept The table presents coefficients and the statistical significance of the coefficients as well as the adjusted R-square and the number of observations Panel A and B presents the results for MF/GDP and MFpc as dependent variables Both present similar determinants so they are analysed together The economic development of a country and age are statistically significant variables and the coefficient 10 For instance, N firms/Pop is highly correlated with New funds/Pop and New firms/Pop, therefore N firms/Pop is used C 2007 The Author Journal compilation C 2007 Blackwell Publishing Ltd C 2007 The Author Journal compilation C 2007 Blackwell Publishing Ltd Adj R square Observations GDPpc Age N firms/Pop Investor protection Av mk sh Intercept 0.274 18 (3) 0.406 20 7.426 (0.010) (4) 3.165 (0.241) (5) 209796.590 (0.120) 394.392 (0.080) (6) 0.566 20 0.405 20 0.593 20 0.483 18 0.009 (0.155) −0.004 −0.002 −111.928 (0.012) (0.236) (0.091) 0.000 0.000 (0.001) (0.004) 0.003 (0.556) 5.741 −0.034 (0.078) (0.570) (2) −2.122 (0.322) 0.005 0.008 (0.549) (0.218) −0.002 −0.003 (0.238) (0.093) 4.474 (0.266) 0.007 (0.389) (1) Panel A: MF/GDP (8) (9) (10) (11) 0.455 18 0.439 18 0.511 20 0.611 20 0.677 20 220856.351 186420.254 260468.202 −4439.738 169178.844 (0.118) (0.211) (0.025) (0.051) (0.073) 326.940 244.677 (0.224) (0.400) −79764.291 −81456.231 (0.448) (0.274) 132.476 143.534 276.838 113.622 (0.638) (0.616) (0.229) (0.596) −116.384 −96.291 −128.959 −86.960 (0.092) (0.192) (0.030) (0.066) 0.442 0.346 (0.000) (0.003) (7) Panel B: MFpc This table displays results of multivariate OLS regressions on mutual fund industry development indicators Dependent variables are: (1) MF/GDP- The ratio of the value of the mutual fund industry to the GDP (in %); (2) MFpc – The mutual fund value per capita (in USD); (3) MF/SBM- The ratio of the value of the mutual fund industry to the value of bond and stock markets (in %) and (4) N Funds/Pop – The total number funds per million inhabitants Appendix A provides a description of the variables along with their data sources All regressions are run with an intercept Columns present the coefficients P-values are below the coefficients Table 13 Determinants of the mutual fund industry: Multivariate regression The Size and Structure of the World Mutual Fund Industry 171 C 2007 The Author Journal compilation C 2007 Blackwell Publishing Ltd Adj.R square Observations Banking restrict GDPpc Age N firms/Pop Investor protection Av mk sh Intercept 0.760 18 15.699 (0.000) 62.163 (0.415) −1.058 (0.578) (12) 0.804 20 −365.239 0.505 14.769 (0.000) 27.183 (0.146) (13) 0.775 20 15.187 (0.000) −0.025 (0.952) 66.809 (0.935) (14) (15) 0.776 20 0.000 (0.837) 15.005 (0.000) 13.636 (0.533) Panel C: N Funds/Pop 0.775 20 −0.355 (0.983) 15.199 (0.000) 18.399 (0.666) (16) Table 13 Continued 0.252 17 0.009 (0.057) 0.082 (0.526) 0.001 (0.808) (17) 0.195 17 0.034 18 0.000 (0.678) −1.029 (0.665) 0.006 (0.163) −0.193 (0.930) 0.001 (0.794) 0.009 (0.073) 0.000 (0.901) (19) (18) 0.342 18 −1.849 0.130 0.008 (0.029) 0.001 (0.359) −1.567 (0.399) (20) Panel D: MF/SBM 0.272 18 0.009 (0.015) 0.000 (0.929) 0.245 (0.871) (21) 0.369 18 0.000 (0.148) 0.007 (0.065) 0.061 (0.131) (22) 172 Sofia B Ramos The Size and Structure of the World Mutual Fund Industry 173 is as hypothesised Wealthier countries with an older industry have more developed industries These results are similar to those of Khorana et al (2005) who also find these variables statistically significant in their study Investor protection, although statistically significant in the univariate analysis, has a lower explanatory power than the latter two variables The same happens for the competition and concentration variables None exceeds the explanatory power of GDPpc and Age Panel C and D report results for N Funds/Pop and MF/SBM as dependent variables, respectively Interestingly, the results emphasise industry competition Countries where the industry shows a greater development comparatively with the domestic securities market and where fund availability per million is higher also have more firms per million inhabitants For these two indicators, Age and GDPpc are statistically irrelevant explanatory variables Therefore, the existence of fewer entry barriers in the industry and firm competition seem to contribute positively to industry development The bond and equity sector of the mutual fund industry: As shown previously, countries present a different asset structure where more developed industries seem to give more weight to equity funds This sub-section analyses the determinants of bond and equity fund sectors of the mutual fund industry To measure the development of the bond and equity sector of the mutual fund industry, the following ratios are used: the ratio of the value of bond mutual funds to the Gross Domestic Product (Bond MF/GDP); the ratio of the value of the equity mutual funds to the Gross Domestic Product (Equity MF/GDP); the ratio of the value of bond mutual funds to the bond market capitalisation (Bond MF/BMC); the ratio of the value of the equity mutual fund to the stock market capitalisation (Equity MF/SMC) 11 Bond sector of the mutual fund industry: Table 14 reports the summary of the results for the bond sector of the mutual fund industry The dependent variables are Bond˙MF/GDP and Bond MF/BMC Competition indicators seem the most important determinants of the bond market’s development, mainly the ratio of companies per million which has a positive and statistically significant coefficient GDPpc is only important to explain Bond MF/GDP On the same line Khorana et al (2005) also find that industry age and investor protection were not significant variables for bond mutual funds Equity sector of mutual fund industry: Table 15 reports the results for a multivariate regression that analyses the determinants of equity weight for the mutual fund industry The results differ a little according to the indicator For Equity MF/GDP the main determinants are Age and GDPpc, which are also the main determinants of MF/GDP However, when analysing the weight of the industry on stock market development, competition and concentration are important variables Countries with a larger average market share and with more firms per million inhabitants have a large equity sector 11 In addition, the ratio of the bond and money market mutual fund industry to the Gross Domestic Product was analysed; the ratio of equity and balanced mutual funds to the Gross Domestic Product; the ratio of the bond and money market mutual fund industry to the bond market capitalisation; the ratio of equity and balanced mutual funds to the stock market capitalisation The results were qualitatively unchanged C 2007 The Author Journal compilation C 2007 Blackwell Publishing Ltd 174 Sofia B Ramos Table 14 Determinants of the bond mutual fund industry This table displays results of a OLS multivariate regression explaining the of the weight of bond mutual funds on the industry across countries Dependent variables are: Bond MF/GDP- the ratio of the size of the bond mutual fund industry to the GDP; Bond MF/BMC- the ratio of the size of the bond mutual fund industry to the size of bond; Appendix A provides a description of the variables along with their data sources All regressions are run with intercept Columns present the coefficients P-values are below the coefficients Bond MF/GDP Intercept (1) (2) (3) (4) (5) (6) (7) −0.019 (0.538) 0.004 (0.771) 0.000 (0.031) 0.005 (0.004) −0.008 (0.736) 0.270 (0.539) 0.005 (0.004) 0.000 (0.038) 0.052 (0.013) 0.080 (0.062) 0.067 (0.079) 0.010 (0.000) 0.000 (0.426) 0.009 (0.000) 0.000 (0.442) −0.012 (0.436) 0.008 (0.000) 0.026 (0.093) 0.489 (0.253) 0.010 (0.000) −0.012 (0.420) 0.608 20 0.698 18 0.691 18 0.698 18 Av mk sh N firms/Pop GDPpc Banking restrict Adj.R square Observations Bond MF/BMC 0.005 (0.004) 0.000 (0.028) 0.009 (0.407) 0.615 20 0.622 20 0.712 18 Conclusions As more investors are relying on mutual funds as investment and retirement vehicles it becomes increasingly important to study the industry and its development determinants in detail Using a large international set of mutual funds, this paper analyses the structure and the size of the mutual fund industry around the world This paper contributes to the literature in several ways, providing a deeper look at the mutual fund industry around the world, offering new insights into its structure and documenting country and regional characteristics The main results are as follows: more developed industries are characterised by a higher share of equity mutual funds and have less home bias Countries where the industry is larger are associated with lower annual charges, consistent with economies of scale (as also found by Khorana et al., 2006) However, more developed industries impose larger initial and redemption charges It seems that countries in an early stage of development have ‘fewer entry barriers’ in order to attract investors to the industry and by making redemptions expensive, ‘increasing departure barriers’, mutual funds dissuade investors from redeeming shares, and are able to invest in a more risky portfolio thereby enhancing performance The results also reveal strong regional differences Europe presents higher levels of industry concentration than Asia and the USA In spite of concentration, Europe has the largest number of competing firms and a high level of industry contestability: the highest ratio of new funds and new companies per million population, which may be a result of the Investment Services Direct that allows EU companies to have a EU C 2007 The Author Journal compilation C 2007 Blackwell Publishing Ltd C 2007 The Author Journal compilation C 2007 Blackwell Publishing Ltd 0.338 20 0.540 20 0.564 20 −0.002 (0.002) −0.671 (0.452) 4.699 (0.002) −0.954 (0.357) −0.002 (0.973) (6) 0.444 20 0.534 18 −0.617 (0.507) 0.001 (0.748) −0.002 (0.004) 4.612 (0.003) (5) Adj.R square Observations 0.343 18 −2.309 (0.028) 0.003 (0.455) 0.115 (0.002) (4) 0.000 (0.022) −0.002 (0.011) 4.345 (0.016) 0.003 (0.304) −0.098 (0.534) 0.005 (0.136) −1.653 (0.238) (3) GDPpc Age N firms/Pop Investor protection Av mk sh Intercept (2) (1) Equity MF/GDP 0.624 20 −0.002 (0.007) 0.000 (0.082) 3.794 (0.007) (7) 0.430 18 0.114 (0.461) 0.000 (0.945) −2.672 (0.044) 0.005 (0.147) (8) 0.439 20 −2.033 (0.022) 0.006 (0.045) 0.107 (0.001) (9) 0.442 20 −1.475 (0.145) 0.006 (0.071) −0.001 (0.310) 1.590 (0.278) (10) 0.481 20 0.000 (0.143) −1.218 (0.213) 0.005 (0.159) 0.037 (0.498) (11) Equity MF/SMC 0.421 20 −0.001 (0.336) 0.000 (0.031) 1.446 (0.345) (12) 0.445 20 0.000 (0.041) −1.282 (0.204) 0.032 (0.558) (13) This table displays results of a OLS multivariate regression explaining the of the weight of equity mutual funds on the industry across countries: Equity MF/GDPthe ratio of the size of the equity mutual fund industry to the GDP; MF/SMC- the ratio of the size of the equity mutual fund industry to the size of stock markets Appendix A provides a description of the variables along with their data sources Columns present the coefficients P-values are below the coefficients Table 15 Determinants of the equity mutual fund industry The Size and Structure of the World Mutual Fund Industry 175 176 Sofia B Ramos ‘passport’ which makes the process of entering a new country easier Notwithstanding, the high level of competition is not reflected in charges, as the annual average mutual fund charge in European and Asian countries is twice that of the USA When comparing the size of the industry with the development of capital markets, GDP per capita or industry age are not so important as industry competition Larger industries have more competing firms and more contestability The results therefore have important policy implications as the existence of entry barriers in the industry seems to hinder industry development The evidence suggests that investors are better off in more developed industries as the industry optimises the risk-return trade-off and that competition in the mutual fund industry may therefore have far ranging and long lasting implications for the wealth of investors C 2007 The Author Journal compilation C 2007 Blackwell Publishing Ltd Description C 2007 The Author Journal compilation C 2007 Blackwell Publishing Ltd IT enf IT- index Investor protection Law and Legal System CIFAR Private Bond/GDP Public Bond /GDP SBM /GDP Index created by IAAT to analyze the inclusion and omission of accounting items The sum of efficiency of legal system, rule of law, corruption, risk of expropriation and risk of contract repudiation of La Porta et al (1998) All these values are scaled between and 10, a higher number representing a better judicial system, less corruption and a lower risk of expropriation and repudiation See also Khorana et al (2005) Insider trading index A higher number implies more insider trading or legal corruption Indicator variable that equals one if the country enforced insider trading laws before 1995, otherwise zero The ratio of private bond market capitalisation to the GDP The ratio of public bond market capitalisation to the GDP The ratio of stock and bond market capitalisation to GDP Indicators of Financial Market Development Private Credit/GDP The ratio of private credit by deposit money banks and other financial institutions to the GDP SMC/GDP The ratio of stock market capitalisation to the GDP Indicators of the Mutual Fund Industry Size of the mutual fund industry The sum of the net assets of all the open-end funds domiciled in the country (year 2005) MF/GDP The ratio of the size of the mutual fund industry to the GDP MFpc Mutual fund per capita The ratio of the size of the mutual fund industry per million population MF/SBM The ratio of the size of the mutual fund industry to the size of bond and stock markets N Funds/Pop Number of funds per million inhabitants Variables Appendix A Description of variables Bhattacharya and Daouk (2001) Global Competitiveness Report of World Economic Forum International Accounting and Auditing Trends, Center for Financial Analysis and Research Inc (IAAT) La Porta et al (1998) Beck et al (2000) Data on China and Taiwan is from WFE and Datastream Beck et al (2000) Beck et al (2000) Beck et al (2000) Data on China and Taiwan is from WFE and Datastream Beck et al (2000) Lipper Hindsight and World Bank Lipper Hindsight and World Bank Lipper Hindsight and World Bank Lipper Hindsight and World Bank Lipper Hindsight and World Bank Source The Size and Structure of the World Mutual Fund Industry 177 C 2007 The Author Journal compilation C 2007 Blackwell Publishing Ltd firms/Pop funds/N firms funds/N funds funds/Pop GDP GDPpc Pop SMC Other Variables Age Education Charges Annual c Total c New New New New Contestability New firms/N firms Competition N firms/Pop Banking˙restrict Concentration Av mk sh CR5 Variables Description The year the first mutual fund was sold in a country Education Index: One of the three indices on which the human development index is built It is based on the adult literacy rate and the combined primary, secondary and tertiary gross enrolment ratio Gross domestic product (Year 2004) Gross domestic product per capita Population (Year 2004) Stock market capitalisation (Year 2004) Annual charges Total charges Annual charges plus initial and redemption charges divided by five Average number of new companies to the total number of new management companies Average number of new management companies per inhabitants Average number of new funds per management company Average number of new funds to the total number of funds Average number of new funds per million inhabitants Number of management companies per million inhabitants A variable that indicates whether a bank faces or not a restriction to enter other financial activities like securities activities, and if banks can offer competition to mutual fund management companies Average market share in a country Market shares of largest five companies in terms of assets under management Appendix A Continued World Bank World Bank World Bank Beck et al (2000) Except Taiwan and China, figures are from World Federation of Exchanges and Datastream Khorana et al (2005) United Nations Development Report Lipper Hindsight Ferreira and Ramos (2006) Ferreira and Ramos (2006) Ferreira and Ramos (2006) Ferreira and Ramos (2006) Ferreira and Ramos (2006) Ferreira and Ramos (2006) Barth, Caprio and Levine (2001) Ferreira and Ramos (2006) Ferreira and Ramos (2006) Source 178 Sofia B Ramos The Size and Structure of the World Mutual Fund Industry 179 References Barber, B., Odean, T and Zheng, L., ‘Out of sight, out of mind: the effects of expenses on mutual fund flows’, Journal of Business, Vol 78, 2005, pp 2095–2120 Barth, J., Caprio, G and Levine, R., ‘The regulation and supervision of banks around the world – a new data base’, in Litan, R.E and Herring, R., eds, Integrating Emerging Markets Countries into the Global Financial System Brookings-Wharton Papers Financial Services (Brookings Institution Press, 2001), pp 183–240 Baumol, W J., Goldfeld, S M., Gordon, L A and Koehn, M F., The Economics of Mutual Funds Markets: 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Mutual Fund Industry, Entry Barriers JEL classification: G15, G23 Introduction Mutual funds are gaining importance worldwide and the mutual fund industry has registered a spectacular growth in