The Role of Law, Corruption and Culture in Investment Fund Manager Fees

Keywords: Managerial Compensation, Incentive Contracts, Private Equity, Law and Finance JEL Codes: G23, G24, G28, K22, K34

This paper considers an international sample of venture capital and private equity funds to assess the role of law, corruption and culture in setting fund manager fees in terms of their fixed management fees, carried interest performance fees, clawbacks of fees and cash versus share distributions of fees. The data highlight a role of legal conditions in shaping fees paid to fund managers. In countries with better legal conditions, fixed fees are lower, carried interest fees are higher, clawbacks are less likely, and share distributions are more likely. These findings suggest legal conditions help to align the interests of managers and shareholders. More specifically, we examine which element of legal conditions matter most, and discover that corruption levels play a pronounced role in shaping fund manager fee contracts. We also show that cultural forces such as Hofstede`s measures of power distance and uncertainty avoidance likewise play a role in influencing fees.

Introduction

Over the course of the mid-2007-2009 financial crisis, fees paid to financial managers have been rigorously scrutinized. There are numerous examples that have become notorious, largely associated with government bailouts and subsequent bonus payments. For example, Sir Fred Goodwin, the former CEO of the Bank of Scotland, received fees that led The Economist to refer to him as "dishonorable". 1 There are blogs that cite a deathwatch for Sir Fred Goodwin, 2 just as there were numerous reports of death threats for AIG employees after their bonus payments subsequent to government bailouts. 3 Credit Suisse approved in April 2010 very controversial bonus payments. 4 Similar examples are extremely widespread that it is hard to not make the mental connection between regulation, corruption, ethics and fees in the financial community.

Likewise, there has been a significant and growing concern in the venture capital and private equity industries worldwide has been the presence of corruption in influencing fund manager activities. For example, the law firm S.J. Berwin noted in their Private Equity Comment 5 (March 2010) that: Private equity funds, as "active" owners of international businesses, can also be a deep pocketed, high profile target for prosecutors looking for someone to bring to book in the wake of a corruption investigation involving a portfolio company. Furthermore, the 2009 Report on Progress on the UNPRI showed that 46% of asset owners and 36.2% of investment managers who had signed up to the Principles cited bribery and corruption as "Environmental, Social and Governance" issues" which they addressed when engaging with service providers. It makes good business sense, therefore, for managers to understand the legal issues in every country in which the fund does business, and to take active steps to ensure that responsible business practices are adopted throughout the portfolio. S.J. Berwin further commented that the private equity industry worldwide would suffer from the longer term effects from the crackdown on corruption for many years to come. S.J. Berwin expressed particular concern with international private equity transactions and exposure to firms linked to governments and corruption:

The case of Vetco Gray UK (which was acquired along with its German parent by a consortium of three private equity firms) is a salutary lesson. Here it was discovered that 1 http://www.economist.com/research/articlesBySubject/displayStory.cfm?story_ID=13235025&subj ectid=987105 2 http://seekingalpha.com/article/78111-royal-bank-of-scotland-ceo-deathwatch 3 http://en.wikipedia.org/wiki/American_International_Group 4 http://www.swissinfo.ch/eng/business/Credit_Suisse_avoids_shareholder_bonus_revolt.html?cid=8 785404 5 http://www.sjberwin.com/latestpublicationdetails.aspx?title=privateequitycomment bribes had been paid to Nigerian government officials in relation to oil exploration projects. This resulted not only in a record fine by the US Department of Justice of $12 million for Vetco Gray UK (which had collectively authorised the payments with several of its affiliates) but also the imposition of an independent monitor (at the company's expense) and further investigation of the company's activities in other countries, which became binding on any future purchaser of the company... Naturally, any areas identified as high-risk countries or industries should be treated with care, as should business dealings with statelinked enterprises and supranational bodies.

This wave of media coverage and public outrage against fund manager fees in recent years suggests a need to better understand the determinants of fund manager fees.

Fund manager fees comprise many components, including fixed fees, performance based fees, clawbacks, and cash versus share payments.

The fees contracts for fund managers of venture capital and private equity managers are no exception, and thus provide a useful context in which to examine the role of law, corruption and culture in setting fund manager fees. Venture capital and private equity funds are typically set up as limited partnerships whereby the institutional investors are the limited partners and the fund manager is the general partner [START_REF] Cumming | Venture capitalist value-added activities, fundraising and drawdowns[END_REF]. Institutional investors include pension funds [START_REF] Jeng | The determinants of venture capital fundraising: evidence across countries[END_REF]Mayer et al., 2004) (which are most common across countries), insurance companies, banks and endowments, etc. Private investment funds typically have a finite life of 10-13 years. This life-span enables the fund time to select appropriate investees and carry out such investments to fruition. A typical investment in an entrepreneurial firm can take from 2-7 years from first investment to the exit date. Entrepreneurial firms typically lack income, revenue and/or cash flows to pay interest on debt and dividends on equity; hence, returns to institutional investors are in the form of capital gains upon exit (such as an IPO or acquisition for successful entrepreneurial firms, or a write-off for unsuccessful firms).

Private fund managers are compensated with a two-part fee. The first part is a fixed fee which is commonly 1-3% of the fund's assets in the U.S. (Gompers and Lerner, 1999a, b), and paid per year. This enables an appropriate annual salary for the fund managers and enables the fund managers to meet overhead costs over the life-span of the fund, particularly in times prior to the realization of investments in the investee firms. The second component is the performance fee, or carried interest, which is commonly 20% of the profits earned by successful fund investments (Gompers and Lerner, 1999a,b). Fixed fees are higher and performance fees are lower among younger funds, which is consistent with a learning model whereby risk adverse fund managers are more likely to prefer more certain compensation when their abilities are unknown to themselves (Gompers andLerner, 1999a, 1999b). Fund managers may face clawbacks from their fees, which means that institutional investors in funds can reduce fees in the event of poor performance.

Institutional investors into funds can state in limited partnership contracts that payment terms come in the form of cash or share distributions.

In this paper we compare and contrast the role of fund manager characteristics and market conditions to the legal and institutional setting in which a fund is based to understand the determinants of fund manager fees. We expect market conditions and fund manager characteristics to be important in setting fees, as these factors would be important in any labor market context. In respect of legal and institutional differences, we compare and contrast the role of legal conditions versus cultural conditions in a country to ascertain the importance of country-specific factors on fees. We expect countries with superior legal settings to affect fees in a way that better aligns the interests of fund managers with their investors, as shown in prior work with a sample of 50 venture capital funds worldwide [START_REF] Cumming | Legality and Fund Manager Compensation[END_REF]. We extend prior work in two important dimensions. First, we obtain a much larger and more recent sample to assess the robustness of prior findings.

Second, and perhaps more importantly, we explore for the first time the effect of the specific features of a country's legal and institutional setting on fees, including different components of legal conditions (specific indices from La [START_REF] Porta | Law and finance[END_REF] as well as cultural dimensions on fees (Hofstede's cultural indices).

Based on a sample of 123 venture capital and private equity funds around the world, we find that in countries with better legal conditions, fixed fees are lower, carried interest fees are higher, clawbacks are less likely, and share distributions are more likely.

These findings support the idea that legal conditions help to align the interests of managers and shareholders. We extend our empirical analyses to ascertain what specific legal conditions matter across countries. We find that corruption levels play a pronounced role in shaping fund manager fee contracts across countries. For example, corruption is the only significant legal determinant of setting fixed fees such that fixed fees are lower in less corrupt countries. We also show that cultural forces such as Hofstede`s measures of power distance and uncertainty avoidance likewise play a role in influencing fees. Overall, it is noteworthy that laws and culture are much more significant in determining fees than fund manager characteristics and/or market conditions.

Our paper is related to a growing the literature on law and finance associated with financial intermediation. Prior work has shown that fees depend on legal conditions [START_REF] Cumming | Legality and Fund Manager Compensation[END_REF] but the dearth of data in that work with 50 observations led to inconclusive statements about what specifically matters in terms of specific attributes of a legal system that affects fees. Other related evidence has shown legal systems affect venture capital financial contracts with entrepreneurs and investment performance [START_REF] Lerner | Does legal enforcement affect financial transactions? The contractual channel in private equity[END_REF][START_REF] Hege | Determinants of venture capital performance: Europe and the United States, Revue Finance[END_REF][START_REF] Cumming | Legality and Fund Manager Compensation[END_REF], as do cultural factors across countries [START_REF] Hazarika | Success in Global Venture Capital Investing: Do Institutional and Cultural Differences Matter? Working Paper[END_REF]. Our paper contributes to the literature by examining how specific legal and cultural differences across countries matter for fee structures.

This paper is organized as follows. Section 2 considers the institutional context and develops hypotheses pertaining to the determinants of fees. The data and summary statistics are presented in section 3. Empirical tests follow in section 4. Concluding remarks are provided in the last section.

Hypotheses

In subsection 2.1 we first briefly outline predictions in regards to the relation between legal conditions and managerial compensation. Thereafter in subsection 2.2 we discuss the importance of certain control variables.

Law, Culture and Fund Manager Compensation

Private fund managers are financial intermediaries between institutional investors and entrepreneurial firms. Institutional investors do not have the time and specialized skill set to carry out due diligence in screening potential private entrepreneurial firms in which to invest; institutional investors also do not have the time and skills to efficiently monitor and add value to the investee entrepreneurial firms. The pronounced risks, information asymmetries and agency problems associated with investments in small, illiquid, and hightech entrepreneurial firms is a primary explanation for the existence of private investment funds with specialized skill sets to mitigate such problems [START_REF] Sahlman | The structure and governance of venture capital organizations[END_REF]Gompers and Lerner, 1999a,b).

We expect countries with superior legal settings to affect fees in a way that better aligns the interests of fund managers with their investors. Legal conditions can be measured in a variety of ways, such as the many indices developed by La [START_REF] Porta | Law and finance[END_REF] and others. The traditional La [START_REF] Porta | Law and finance[END_REF] indices include efficiency of judicial system, rule of law, corruption, risk of expropriation, risk of contract repudiation, and shareholder rights. A weighted average of these indices was adopted by [START_REF] Berkowitz | Economic development, legality, and the transplant effect[END_REF], and referred to as the Legality Index. It is natural to expect these indices to matter for cross-country determinants in fees, not because these indices were developed for limited partnerships, but rather because they affect the uncertainty faced by fund managers in carrying out their investments in those countries and as such their expected incomes.

Similarly, as fee contracts are the outcome of bargaining between fund managers and their institutional investors, and bargaining depends on culture in different countries, we may expect cultural measures developed by Hofstede to matter in setting fees. These cultural dimensions are as follows:6

Power Distance Index (PDI) that is the extent to which the less powerful members of organizations and institutions (like the family) accept and expect that power is distributed unequally. This represents inequality (more versus less), but defined from below, not from above. It suggests that a society's level of inequality is endorsed by the followers as much as by the leaders. Power and inequality, of course, are extremely fundamental facts of any society and anybody with some international experience will be aware that 'all societies are unequal, but some are more unequal than others'. Individualism (IDV) on the one side versus its opposite, collectivism, that is the degree to which individuals are integrated into groups. On the individualist side we find societies in which the ties between individuals are loose: everyone is expected to look after him/herself and his/her immediate family. On the collectivist side, we find societies in which people from birth onwards are integrated into strong, cohesive in-groups, often extended families (with uncles, aunts and grandparents) which continue protecting them in exchange for unquestioning loyalty. The word 'collectivism' in this sense has no political meaning: it refers to the group, not to the state. Again, the issue addressed by this dimension is an extremely fundamental one, regarding all societies in the world.

Masculinity (MAS)

versus its opposite, femininity, refers to the distribution of roles between the genders which is another fundamental issue for any society to which a range of solutions are found. The IBM studies revealed that (a) women's values differ less among societies than men's values; (b) men's values from one country to another contain a dimension from very assertive and competitive and maximally different from women's values on the one side, to modest and caring and similar to women's values on the other.

The assertive pole has been called 'masculine' and the modest, caring pole 'feminine'. The women in feminine countries have the same modest, caring values as the men; in the masculine countries they are somewhat assertive and competitive, but not as much as the men, so that these countries show a gap between men's values and women's values.

Uncertainty Avoidance Index (UAI) deals with a society's tolerance for uncertainty and Consistent with [START_REF] Acemoglu | Information accumulation in development[END_REF], we conjecture that fund managers operating in legal conditions of poor quality will be more inclined to accept higher fixed fees and lower performance fees. At a general level, information asymmetries are more pronounced in countries with poor legal conditions, and therefore less developed countries are less likely to employ incentive contracts for managers and entrepreneurs [START_REF] Acemoglu | Information accumulation in development[END_REF]. Specifically in the venture capital context, prior empirical work is consistent with the view that countries with weaker legal conditions (based on the La Porta et al., 1998, indices) face more uncertain exit markets whereby it is more difficult to obtain a capital gain and generate fund returns [START_REF] Lerner | Does legal enforcement affect financial transactions? The contractual channel in private equity[END_REF][START_REF] Cumming | Venture capitalist value-added activities, fundraising and drawdowns[END_REF].

As such, we expect risk adverse fund managers to prefer higher fixed fees in exchange for a lower performance fees in order to garner a more certain income stream in countries with weaker legal conditions. Similarly, we expect cultural attributes in a country to affect fees, as Power Distance, Individualism and Masculinity relate to disparity in fees or lower fixed fees and higher performance fees, while uncertainity avoidance is associated with higher fixed fees and lower performance fees.

H1:

Fixed management fee percentages will be in higher in countries with a weaker Legality Index, and in countries with less Power Distance, Individualism and Masculinity, and higher Uncertainty Avoidance.

H2:

Carried interest performance fee percentages will be higher in countries with a higher Legality Index, and in countries with more Power Distance, Individualism and Masculinity, and lower Uncertainty Avoidance.

While fund managers benefit from higher fixed fees and lower performance fees in countries with poor legal conditions, institutional investors nevertheless face a particularly pronounced risk of lower profits among funds in countries with poor laws. Institutional investors can lower the downside costs of low returns with the mechanism of a "clawback".

A clawback means institutional investors reduce the compensation paid to fund managers in the event of poor performance. A fund usually distributes cash and other proceeds to the fund manager and other investors upon each liquidating event. The problem of excess distributions may occur when earlier liquidations are profitable, and later ones are not. This will be further exacerbated if the fund manager accelerates the sale of profitable investments and holds off the liquidation of bad investments. The clawback allows the investors to recover excess distributions upon liquidation of the fund. 7 We therefore expect clawbacks to be more frequently employed in countries with poorer legal conditions. We likewise expect clawbacks to be more common in countries with greater Power Distance as it directly reflects bargaining power amongst fund managers and institutional investors.

H3:

Clawbacks of fund manager fees in the event of poor performance are more common in countries with a weaker Legality Index and greater Power Distance.

We further expect legal conditions to influence the mode of distribution of fund profits to institutional investors in terms of cash versus share distributions. Poor legal conditions increase the financial risk of share positions in entrepreneurial firms; therefore, all else being equal, the greater the uncertainty created by a lower quality legal environment, the greater the probability of a cash-only distribution policy in the setup of a private fund.

H4:

The weaker the legal environment, the greater the probability of covenants mandating cash-only distributions from fund managers to institutional investors.

7

See http://vcexperts.com/vce/library/encyclopedia/glossary_view.asp?glossary_id=188 for a formal definition of clawbacks: "A clawback obligation represents the general partner's promise that, over the life of the fund, the managers will not receive a greater share of the fund's distributions than they bargained for. Generally, this means that the general partner may not keep distributions representing more than a specified percentage (e.g., 20%) of the fund's cumulative profits, if any. When triggered, the clawback will require that the general partner return to the fund's limited partners an amount equal to what is determined to be "excess" distributions."

Finally, in an international context private investment funds can be set up offshore, and doing so typically has significant tax advantages. In the US, share distributions are common as the institutional investor can decide when it is the best time to realize capital gains (There are other reasons for share distributions, see e.g., Gompers andLerner, 1999b, 1997). Since offshore funds are by their very nature tax lowering entities, the timing of realization of capital gains is a less pronounced concern among institutional investors of offshore funds, and therefore the need for share distributions is less pronounced for offshore funds. Furthermore, aside from concerns relating to taxation, offshore funds commonly comprise of various types of institutional investors, such as pension funds, insurance companies, banks, and endowments from a diverse set of countries. Institutional investors from a diverse set of countries typically face non-harmonized legal impediments to acquiring and selling shares in entrepreneurial firms transferred to them from the fund manager. Overall, therefore, offshore funds are expected to mandate cash-only distributions.

H5:

Offshore funds are more likely to mandate cash-only distributions from fund managers to institutional investors.

Control Variables for Analysing Managerial Compensation across Countries

Fund manager compensation quite plausibly is influenced by a variety of factors pertaining to economic conditions, institutional investor and fund manager characteristics, including education and experience as well as fund factors such as stage and industry focus, among other factors. We briefly discuss each of these factors in this subsection.

First, in regards to economic conditions, where the demand for fund managers exceeds supply, fund managers are more likely to be compensated better. For instance, in the boom periods a phenomenon of "money chasing deals" (Gompers and Lerner, 2000) typically results, whereby fund managers are short in supply relative to the institutional investors wanting to contribute to the asset class [START_REF] Kanniainen | Start-up investment with scarce venture capital support[END_REF]). As such, fund managers are more likely to have higher fixed fees and carried interest percentages, and less likely to face clawbacks, in times of boom economic conditions (i.e., in countries with stronger economic environments and at times of better stock market performance).

Second, apart from overall legal quality conditions, specific legal environments pertaining to legal origin might influence fee structures (La [START_REF] Porta | Legal determinants of external finance[END_REF][START_REF] Porta | Law and finance[END_REF].

Cultural differences across regions may also be closely to legal origin variables.

Third, partnership profits from limited partnership funds (carried interest) may be taxed at the capital gains tax rate or deemed as business income and taxed at the income tax rate (unlike venture capital firms set up as corporations) [START_REF] Fleishcher | The missing preferred return[END_REF]. As such, we control for the difference between income tax and capital gains tax rates for limited partnership funds.

Fourth, fund managers that have more education are more likely to receive higher fixed and performance fees, and less likely to face clawbacks. Fund managers with more relevant work experience are more likely to have lower fixed fees but higher carried interest percentages (consistent with the signalling model as discussed in Gompers and Lerner , 1999).

Fifth, fund characteristics such as fund size, stage focus and industry focus can affect fees (Gompers and Lerner, 1999a). Larger funds are more likely to have smaller fixed fees simply because the fixed compensation would be excessive. Funds focused on investing in earlier stages of development and in more high tech industries are more likely to have higher performance fees to incentivise the fund managers and align their interests with that of the institutional investors (since agency problems and information asymmetries are more pronounced among funds focused in early stage and high-tech investments).

Sixth, the type of institutional investor (bank, government, pension fund, etc.) and their respective risk tolerance levels could influence the pay structure of the fund managers in terms of fixed versus managerial fees (for reasons analogous to research in Mayer et al., 2004, and[START_REF] Lerner | Does legal enforcement affect financial transactions? The contractual channel in private equity[END_REF]. As well, the identity of the institutional investors could of course affect the probability of use of clawbacks and the mode of distributions in terms of cash versus shares.

In the empirical analyses of the hypotheses outlined in subsection 2.1, these and other control variables identified in this subsection are used. The data and summary statistics are described in the next section. Thereafter in section 4 multivariate empirical tests are provided. A discussion of limitations, alternative explanation, future research and concluding remarks follows after section 4.

Data

Methods and survey instrument

The data used in this study come mainly from a survey conducted over the period December 2009 and March 2010. The aim of our study is therefore to present a new set of international data corresponding to other countries in the world. The data on fund structure, their size and their investments are mostly available on the financial databases. Otherwise, details of the fees structure of general partners, the terms of recoveries and the profit distribution policy used by the fund (cash against shares) are not publicly revealed by all funds in some countries. On the other hand, most of the agreements used to govern the relationship between managers and investors in the fund are generally written in different languages, so it was necessary to obtain the data by use of surveys and interviews that allow collecting pertinent information. Funds publications on their websites were however used to verify and enhance data obtained by survey and interviews.

We integrate in our sample all investment funds without distinction between private equity funds or venture capital funds. The data collected can be classified into six different groups, which are summarized in Table 1:

-The data related to the compensation of the management partners: % of management fees, % of carried interest performance fees, the application of the clawbacks clause, the distribution of cash to institutional investors instead of shares.

-The legal conditions of the country measured by the legality index (La [START_REF] Porta | Legal determinants of external finance[END_REF][START_REF] Porta | Law and finance[END_REF]. The legality index is derived from a principal components analysis of the covariance matrix from the five observed legality variables (Berkowitz, Pistor, and Richard 2000).

-The country's economic conditions: the GNP per capita, the MSCI Index, the Industry market / book ratio calculated according to the sectors targeted by investment funds.

- [Insert Table 1 About Here]

Potential sample selection bias

To summarize, the different sources used in collecting data from the study are as follows:

-The database Thomson One Banker allowed us to collect specific information about funds as its size, creation date and the target firms of the funds...

-The Datastream database allowed us to collect economic information about each country of the sample: the GNP, the MSCI, the Industry Market to Book ratio.

-A questionnaire was sent to officers, general partners and members of management funds. Some respondents preferred a telephone interview. The questionnaire allowed gathering non-published information about general partners (such as their training level, their professional experience, their compensation)

Those potential interviewees were identified from various sources such as:

(1) The Kompass database for the case of French fund managers

(2) The database Thomson One banker to collect the email addresses of fund management teams internationally.

(3) The websites of investment funds.

The survey was sent to approximately 2,500 investment funds in the world by using software for online survey (WysuForms). It has mainly sought the partnership of managers of such funds in the survey with the promise that their results will be communicated to the end of the study. We have verified that a single response is validated for each investment fund. Furthermore, there is a limit in the method of collecting survey data in particular selection bias in the sample. Knowing that this bias is possible, we believe that, after detailed analysis of responses obtained confirm we prove that this bias is avoided in this type of study.

One limitation to obtaining data through a survey is the possibility of sample selection bias. While we acknowledge that this is a possibility, we believe from a detailed analysis of the responses received and the data obtained from the responses that this concern does not arise in this exercise. First, survey data were gathered for a final sample of 123 funds in 23 countries. We are aware that the seminal work carried out by [START_REF] Gompers | The use of covenants: an empirical analysis of venture capital partnership agreements[END_REF] utilized a sample of 140 contracts used to establish funds, obtained from institutional investors (two fund of funds and one endowment). Litvak (2004b) Second, a broad array of respondents replied to the survey. For example, the data show the median respondent fund size of US$70.900.000 and the average being US$144.667.818 (minimum US$263.377,5; maximum US$930.000.000), indicating that respondents were of a variety of fund sizes and of typical size for a sample of non-US countries. The possibility of sample selection bias is further reduced by the presence of both onshore and offshore funds within the final sample, the presence of funds organized not only in both common law and civil law jurisdictions, but also within jurisdictions in legal systems with English, French, Scandinavian and German based legal systems, and also the presence of funds situated in countries where English is not the primary language.

Finally, a sufficient number of variables regarding both fund and fund manager organization and the relevant features of the fund asset size, fund vintage, investor composition, investment strategy, industry composition of fund investments and governance structures, more specifically the specific covenants provided in the terms within the agreements that govern the relationship between fund investors and fund manager, were collected to minimize the risk of response bias. We also sought information on the method of calculating management fees, the treatment of other fees such as consulting and monitoring fees, and profit sharing and distribution terms. We unfortunatelyrealize that we cannot absolutely rule out the possibility of a response bias as the data we have collected here is unique.

Summary statistics

The summary statistics are presented in Table 3. In the data the average performance fee is 18.01%, and the median performance fee is 20%. The average fixed fee is 2.32%, and the median fixed fee is 2.5%. Thirty four of the 123 funds imposed clawbacks against fund managers in the event of poor performance; the degree of these clawbacks was most often 20% of the fund manager fees. Eighty seven of the 123 funds mandated cash-only distributions.

The level of the legality index for each country is indicated in Table 2, and for all countries together in Table 3. The legality index is a weighted average of the legal index variables introduced by La Porta et al. (1997,1998), as defined by [START_REF] Berkowitz | Economic development, legality, and the transplant effect[END_REF]. Each of the components of the legality index is highly pertinent to venture finance, and comprise the efficiency of the judicial system, the rule of law, corruption, risk of expropriation, risk of contract repudiation, and shareholder rights. It is calculated as 4 shows a strong positive correlation (0.51) between the legality index and performance fees and a strong negative correlation (-0.62) between the legality index and fixed fees.

[Insert Tables 2-4 About Here]

Eighteen per cent of the funds in the data are outbound offshore funds, and 8% are inbound offshore funds. As indicated in Table 1, an outbound offshore fund is one that obtains its capital from investors from a certain jurisdiction but fund investments are made primarily in assets other than in the jurisdiction of the fund and the fund investors. With reference to US jurisdictional boundaries, a fund will be considered to be an outbound offshore fund if it obtains capital from US investors, but it invests outside the US. An inbound offshore fund is one that obtains its capital from investors from various jurisdictions but fund investments are made primarily in assets in a certain jurisdiction.

With reference to US jurisdictional boundaries, an inbound offshore fund will be a fund located offshore that invests primarily in assets within the US yet obtains its capital from non-US investors.

As these distinctions appear to be important in practice for private investment fund management in an international setting, we control for these variables in our empirical analyses.

A majority of funds are managed by MBA graduates, and a typical fund manager has relevant work experience of about 15.5 years. Science and law graduates exist among some of the funds in the data, and some fund managers had partial training (i.e. some nondegree courses) in law and/or sciences (and this partial training is reflected in our data by recording the proportionate number of years of training). We control for the specific training of the fund managers in our empirical tests.

Seventy-two of 123 funds were set up as limited partnerships, and the remainder were set up as limited liability companies or trusts (see e.g. Cumming and Walz 2004;[START_REF] Cumming | Venture capitalist value-added activities, fundraising and drawdowns[END_REF] and accompanying text on limited partnerships versus other types of fund structures). Thirty one per cent had a pure venture capital (earlystage) focus, and most had a significant exposure to high-tech industries (as reflected by the industry market/book ratio of the investee firms in which the fund had invested). The range across each of the different funds and countries for these and the other variables is detailed in Table 2.

Econometric Tests

Econometric Methods

We analyse four different dependent variables in this section: fixed fees (Table 5),

Carried interest performance fees (Table 6), clawbacks (Table 7) and cash-only distributions (Table 8). The various right-hand-side explanatory variables were identified in section 3 and defined in Table 1. For each dependent variable we provide 7 alternative sets of regressions to show robustness (for a total of 28 different models, consecutively numbered across Tables 5678). Tables 5 and 6 make use of standard OLS regression methods corrected for heteroskedasticity with White's estimator (1980). The dependent variable in Tables 5 and6 are bounded below by zero and above by one; we considered different methods of estimating fractions [START_REF] Bierens | Modeling fractions[END_REF], but did not find any material differences to the conclusions drawn. Alternative specifications are available upon request.

The dependent variables in Tables 7 and8 are binary variables; as such, we make use of simple logit regressions and again correct for heterockedasticity. Marginal effects for the logit models were computed using Data Analysis and Statistical Software (Stata). The results are also robust to inclusion / exclusion of most of the countries in the data and potential outliers. 8

We use logs of the right-hand-side variables (except the dummy variables) to reduce the weight of outlier observations and account for diminishing effects. Again, the results are quite robust and alternative specifications are available upon request. In section 5 we describe various other elements of the data, including alternative definitions of variables and variables not employed for reasons of conciseness.

[Insert Tables 5678About Here]

Regression Results

The regression results in Tables 5 and6 indicate that legal conditions by far have the most statistically and economically significant effect on fixed fees and performance compensation. Table 5 indicates fixed fees are significantly lower in countries with stronger legal conditions, and this result is robust to any of the different specifications in 8 One exception to the robustness of results to exclusion of countries is that where the number of observations is significantly reduced by excluding countries, some of the results reported are not robust. Excluding countries with just one fund did not affect the primary results reported.

Models 1-5, among others not presented. This strongly supports H1 outlined above in subsection 2.1. Further, Table 5 indicates legality is the most economically and statistically significant variable in explaining performance fees, strongly in support of H2. In regards to the economic significance, based on the legal numbers indicated in Some of the control variables are significant in Tables 5 and6 in ways that are expected (as described in subsection 2.2). Larger funds have lower fixed fees (Table 5).

Ph.D.'s are more likely to have higher performance fees (Table 6, but this effect is significant only in Model 11). The presence of government investors gives rise to larger fixed fees, and funds focused in high-tech industries (with higher market/book ratios) are more likely to have smaller fixed fees. Managers with extensive work experience are more likely to have higher carried interest (this effect is statistically significant but not very robust on model 10, 11 and 13). The other variables, however, are generally insignificant and/or not robust. For example, differences between income and capital gains tax rates do not affect fixed fees versus management fees. Overall, therefore, the most robust variable is legality for explaining international differences in fixed and performance fees.

The model 6 and 7 show that the corruption, a component of the legality index has a negative and significant effect on fixed fees. Countries with high corruption index have low management fees. However, almost all the components of the Legality index have a significant effect on the level of carried interest. The efficiency of Judiciary System induces higher carried interest but the risk of expropriation and corruption reduce the preference of fund managers to receive variable compensation depending on funds 'performance.

Introducing the cultural dimensions of Hofstede in model 7, shows that Power Distance Index and Individualism have a negative and significant effect on the level of management fees. Anxiety about the unknown, measured by Uncertainty Avoidance Index, induces general partners to require higher management fees (significant result in model 7 of table 5).

As in Tables 5 and6, Table 7 indicates that the legal environment is the most statistically and economically significant determinant of clawbacks among private investment funds across countries, strongly supporting H3 (subsection 2.1). In terms of the economic significance, a reduction in the quality of legal conditions increases the probability of clawbacks by approximately 33% for a move from Brazil than Germany, and approximately 50% for a move from Brazil than Switzerland.

It is important to stress the asymmetric relation between legality and fund manager compensation. Fund managers have higher fixed fees and lower incentive fees in countries with weak legal conditions (Tables 5 and6). But in regards to penalty clauses, fund managers in countries with weak legal conditions are more likely to face the downside risk of a clawback on their fees (Table 7). The intuition underlying this asymmetric result is possible explained by the fact that risk adverse fund managers trade off a higher fixed fee for a lower performance fee when legal conditions are weak, while risk adverse institutional investors are more likely to require clawbacks to protect against downside risk in countries with poor laws.

The only other significant and robust variable in Table 7 for clawbacks is the MSCI index (model 20). In better market conditions across time, fund managers are less likely to face clawbacks. This is consistent with results in previous work (See e.g. Gompers andLerner, 1999a,b, 2000 for empirical work; [START_REF] Kanniainen | Start-up investment with scarce venture capital support[END_REF] for theoretical work) which shows demand and supply conditions affect fees. When there is money chasing deals (excess capital flowing from institutional investors to private investment funds for the given inelastic supply of private investment fund managers), fund managers receive more favourable deal terms from their institutional investors.

In regards to the economic significance of legality in Tables 5 and6, note that when GNP per capita is included alongside the legality index, the statistical significance of legality is not affected but the economic significance is affected. The reason for this change is the high correlation between legality and GNP per capita (see Table 4). Given the bias introduced by simultaneously including both legality and GNP per capita, we feel much more confident with the economic significance associated with only including the legality index without the GNP per capita variable. Note as well in Table 7 that the simultaneous inclusion of GNP per capita and legality gives rise to a statistically insignificant relation between legality and the probability of use of clawbacks. Again, this is due to the high correlation between legality and GNP per capita (Table 4).

Model 21 of table 7, shows that the Power Distance Index is the only cultural dimension that significantly affect clawbacks. When the acceptance of inequality of power is emphasized in a given country, the probability of use of clawbacks will be higher. Much more significantly in Table 8, however, is the result that institutional investors mandate cash-only distributions for off-shore funds. The estimates coefficients for legality in Models 22-27 provides very strong support for H5 outlined in subsection 2.1.

In regards to the economic significance, institutional investors are approximately 77.78% more likely to require cash-only distributions when the fund is established as an offshore fund. As conjectured and explained in subsection 2.1, this finding is consistent with the view that share distributions for tax reasons are less meaningful since offshore funds are already tax pass through entities. Furthermore, institutional investors in an offshore fund are commonly from a diverse set of countries, and they typically face non-harmonized legal impediments to selling shares in entrepreneurial firms transferred to them from the fund manager. Hence, it is much more efficient for liquidity reasons to have cash-only distributions among offshore fund structures.

The other significant and robust variable in Table 8 for Cash versus Share Distributions is the Legality index. In terms of the economic significance, a reduction in the quality of legal conditions increases the probability that institutional investors mandate cash-only distributions. On the other hand, better market conditions illustrated by a higher GNP per Capita in model 22 or a higher MSCI index in model 24, 25 and 27 have a positive effect on Cash Distributions. Institutional investors will rather prefer to receive cash and invest it in the market since there is a favorable economic situation.

Conclusion

There has been a growing concern over fee structures since the financial crisis.

International law firms such as S.J. Berwin have been highlighting the role of corruption and law in setting fees and governance in the private equity industry: "Private equity funds that use agents, advisers or consultants to conduct business on their behalf without proper due diligence, training or monitoring, and business partners that lack transparency in their books and records should also place the fund on alert, as should unusual or unclear sales timings, transactions or payment routes, and any non-standard contractual terms."

Based on a sample of 123 venture capital and private equity funds around the world, we find that in countries with better legal conditions, fixed fees are lower, carried interest fees are higher, clawbacks are less likely, and share distributions are more likely.

These findings support the idea that legal conditions help to align the interests of managers and shareholders. We extend our empirical analyses to ascertain what specific legal conditions matter across countries. We find that corruption levels play a pronounced role in shaping fund manager fee contracts across countries. For example, corruption is the only significant legal determinant of setting fixed fees such that fixed fees are lower in less corrupt countries. We also show that cultural forces such as Hofstede`s measures of power distance and uncertainty avoidance likewise play a role in influencing fees. Overall, it is noteworthy that laws and culture are much more significant in determining fees than fund manager characteristics and/or market conditions.

Consistent with the theoretical work [START_REF] Acemoglu | Information accumulation in development[END_REF], risk adverse private fund managers substitute fixed compensation for incentive compensation as legal conditions worsen. The data in fact indicated that legal and cultural conditions by far have the most statistically and economically significant effect on compensation, even in comparison to the role of the managers education and experience, as well as a variety of other fund characteristics including fund size and industry and stage focus.

While risk adverse fund managers trade-off performance fees for fixed fees in countries with poor laws, risk adverse institutional investors are also more likely to require clawbacks of fund manager fees in the event of poor fund performance. In other words, there is an asymmetry in fund manager compensation in relation to legal conditions: fund managers have higher fixed fees and lower incentive fees in countries with weak legal conditions, but fund managers in countries with weak legal conditions are also more likely to face the downside risk of a clawback on their fees.

That legal conditions affect the payment conditions of fund managers and institutional investors across countries has a number of implications for future research.

Legal and cultural conditions influence fund manager compensation, which in turn could have implications for fund investment selection, returns and the development of private equity markets across countries. The comparative importance for law versus culture in compensation contracts versus the role of law directly in other aspects of fund management could be a fruitful avenue for future research.

Table 1. Definition of Variables

This table defines the variables considered in this paper. Summary statistics are presented in Tables 2 and3.

Variable Description

Compensation Variables

Fixed Management Fee % The fund managers' fixed fee as a percentage of the funds raised from the institutional investors.

Carried Interest Performance Fee %

The fund managers' carried interest performance fees as a percentage of the profits earned by the fund.

Clawbacks

A dummy variable equal to one if the fund allows for clawbacks against the fund managers but not any of the fund investors. A clawback enables the fund investors to lower the fee received by the fund manager in the event of poor performance of the fund.

Cash Distributions A dummy variable equal to one if the fund managers are required to distribute cash to the institutional investors instead of shares (for realized capital gains from investments in entrepreneurial firms).

Legal and Market Conditions

Country Legality Index Weighted average of following factors (based on [START_REF] Berkowitz | Economic development, legality, and the transplant effect[END_REF]: efficiency of judicial system, rule of law, corruption, risk of expropriation, risk of contract repudiation, shareholder rights (as per La [START_REF] Porta | Legal determinants of external finance[END_REF][START_REF] Porta | Law and finance[END_REF]. Where the weighted average is not available, especially for less developed countries, an approximate index is derived by multiplying the country's GNP per population with a constant variable obtained by carrying out a regression of the legality indices available. Higher numbers indicate 'better' legal systems. The log of this variable is used in the empirics to account for a diminishing effect with larger numbers.

Legal Origin Dummy variables equal to 1 for a fund organized in countries of different legal origin, including English, French, German and Scandinavian.

Hofstede Cultural Dimensions

Hofstede's study demonstrated that there are national and regional cultural groupings that affect the behavior of societies and organizations, and that these are persistent across time. Hofstede has found five dimensions of culture in his study of national work related values. The dimensions are: Small vs. large power distance, Individualism vs. Collectivism, Masculinity vs. Femininity, Weak vs. strong uncertainty avoidance and Long vs. short term orientation.

GNP per Capita

The GNP per Capita of the country in which the fund is formed. The log of this variable is used.

MSCI Index

The country-specific MSCI Index taken for the year prior to that when fund raising commenced . The year prior to fund raising is deemed to be most relevant as decisions to invest in private equity by institutional investors will be based on available economic indicators. The log of (1+MSCI) is used in the empirics to account for a diminishing effect with larger numbers.

Vintage Year of Fund

The year fund raising commences Outbound offshore A dummy variable equal to 1 for a fund located offshore that obtains its capital from investors from a certain jurisdiction but fund investments are made primarily in assets other than in the jurisdiction of the fund and the fund investors. With reference to United States jurisdictional boundaries, a fund will be considered to be an outbound offshore fund if it obtains capital from United States investors, but it invests outside the United States.

Inbound offshore A dummy variable equal to 1 for a fund located offshore that obtains its capital from investors from various jurisdictions but fund investments are made primarily in assets in a certain jurisdiction. With reference to United States jurisdictional boundaries, an inbound offshore fund will be a fund located offshore which invests primarily in assets within the United States yet obtains its capital from non-United States investors. Average number of years relevant work experience of principal fund managers at the time of fund raising. The log of this variable is used in the empirics to account for a diminishing effect with larger numbers.

Fund Characteristics

Funds Raised

The fund size, or amount of funds raised in US Dollar. Where the amount is provided in a local currency, an exchange rate as at December 2003 is used for conversion of such amounts into US Dollar equivalents. The log of this variable is used in the empirics to account for a diminishing effect with larger numbers.

Bank Institutional Investors

The proportion of banks as the fund's institutional investors

Government Investors

The proportion of government agencies or ministries as institutional investors Limited Partnership Funds A dummy variable equal to 1 for the fund being organized as a limited partnership.

Industry Market / Book

The industry market/book ratio of the industries for which the fund has invested in. The industry market/book ratio of 5 general categories, Biotechnology and Medical, Communications and Internet, Computers and Electronics, Manufacturing and others, is obtained by averaging the total book value of specific industries falling within the general categories. The log of this variable is used in the empirics to account for a diminishing effect with larger numbers.

Early Stage Investee Focus

A dummy variable equal to 1 for funds which indicate a focus on financing provided to firms in their early / expansion stages of development (not late stages or buyout stages). More specific stages of focus were not tractable due to international differences in the definition of stage focus, as well as style drift that is often observed among different stages of development.

  ambiguity; it ultimately refers to man's search for Truth. It indicates to what extent a culture programs its members to feel either uncomfortable or comfortable in unstructured situations. Unstructured situations are novel, unknown, surprising, different from usual. Uncertainty avoiding cultures try to minimize the possibility of such situations by strict laws and rules, safety and security measures, and on the philosophical and religious level by a belief in absolute Truth; 'there can only be one Truth and we have it'. People in uncertainty avoiding countries are also more emotional, and motivated by inner nervous energy. The opposite type, uncertainty accepting cultures, are more tolerant of opinions different from what they are used to; they try to have as few rules as possible, and on the philosophical and religious level they are relativist and allow many currents to flow side by side. People within these cultures are more phlegmatic and contemplative, and not expected by their environment to express emotions.

  The characteristics of institutional investors: the proportion of banks institutional investors, government investors... -The characteristics of the fund: funds size, funds organization (Partnership, Liability Company ...), companies target by funds... -Characteristics associated to the fund managers specially their training level (the proportion of MBA, CFA or PhD trained fund managers) and relevant experience.

  0.381* (Efficiency of the Judiciary) + 0.5778* (Rule of Law) + 0.5031* (Corruption) + 0.3468* (Risk of Expropriation) + 0.3842* (Risk of Contract Repudiation)The legality index is an appropriate focus of our analysis, in view of the fact that the components of the legality index are very highly correlated, and to focus on a subset of indices within the component of legality to avoid the collinearity problem might tend to have the appearance of data mining. Moreover, as we have a relatively small number of observations, a focus on a weighted average legal index suitably mitigates the possibility of incorrect statistical inferences with outlier observations and the inclusion or exclusion of certain countries in the data. A higher legality index indicates better substantive legal content pertaining to investing, the quality and likelihood of enforcement. Higher numbers indicate 'better' legal systems across each of the factors. Consistent with Hypothesis 1 and Hypothesis 2, Table

  has data from 38 funds in the US, andMetrick and Yasuda (2006) have data from 203 funds in the US. We believe however that by obtaining data from funds situated both in and outside the US, and by having access to data regarding contracts entered into by 123 different fund managers in 23 countries, response bias is mitigated as much as possible. Similarly,[START_REF] Lerner | Does legal enforcement affect financial transactions? The contractual channel in private equity[END_REF] study of the relation between legality and venture capital contracts with entrepreneurs is based on data from 28 fund managers. Limitations in our sample size from each country from which we derived data, as well as the limited information about venture capital and private equity funds around the world, however, makes reliable statistical comparisons of our sample relative to the population of funds intractable. Our sample of respondent funds includes twenty-one funds from France, fourteen funds from the US, twelve funds from the UK, eleven funds from the Netherlands and, eight funds from Malaysia, six funds from Germany, five funds each from Australia, Finland and South Africa, four funds each from Brazil and the Netherlands Antilles, three funds each from the Philippines Belgium, Canada, India, Italy,Spain and Switzerland, two funds each from the Cayman Islands and Mexico, and one fund each from the New Zealand Singapore and

Luxembourg (see Table

2

). The number of respondents, and representation of funds from both developed and emerging private equity markets, makes a response bias even less likely.

Table 2

 2

	, the data

These results are robust to the inclusion and/or exclusion of controls for a variety of factors including market conditions, institutional investor and fund manager characteristics, including education and experience as well as fund factors such as stage and industry focus, among other control variables available shown explicitly in the tables (among other considered in the new detailed international dataset, but excluded for reasons of conciseness).

Table 8

 8 analyses the relation between legality and payment terms to a fund's institutional investors in terms of cash versus share distributions from realized investments in entrepreneurial firms. The Logit regression indicates a robust relation between legality and cash distributions in Model 23. The other models 22, 25 and 26 are less significant but showed the same relation (These results confirm the hypothesis H4 in subsection 2.1): cash only distributions are more likely in countries with weak legal conditions.

  Tax DifferenceA variable equal to, for top marginal tax rates, (Income Tax Rate -Capital Gains Tax Rate) * (Limited Partnership Dummy Variable), for partnerships for which carried interest is taxed at the capital gains rate, and fixed management fees are taxed at the income tax rate.Percentage of principal fund managers with investment making decisions who have obtained a PhD in a non-science based discipline. Where managers have some extent of advanced Ph.D. studies, that fraction of training is also reflected in the data.

	Fund Manager Characteristics
	Percentage of Legally Trained Fund Managers	Percentage of principal fund managers with investment making decisions who are legally trained, or are qualified as lawyers. Where managers have some extent of legal training, that fraction of the extent of legal training is also reflected in the data.
	Percentage of MBA/CFA Trained Fund Managers	Percentage of principal fund managers with investment making decisions who have obtained an MBA or CFA qualifications. Where managers have some extent of such training, that fraction is also reflected in the data.
	Percentage of PhD	Percentage of principal fund managers with investment making decisions who have obtained a PhD in
	(Science) Trained Fund	a science based discipline. Where managers have some extent of formal scientific training, that
	Managers	fraction of training is also reflected in the data.
	Percentage of PhD (Non-
	Science) Trained Fund
	Managers
	Average # Years of
	Relevant Work Experience
	of Principal Fund
	Managers

Table 2 . Summary of the Data This

 2 table summarizes the different characteristics of the funds forming the sample. Funds are classified by country in which they were formed and countries are grouped in two categories: Civil Law and Common Law. The average values of each variable per country are reported (except for Clawbacks and Cash distribution, where the number of funds is reported).

	Table 2.a Summary of the Data (Civil Law)

Table 2 .b Summary of the Data ( Common Law)

 2

		Total	Australia	Canada	Cayman Islands	India	Malaysia	New Zealand	Singapore	South Africa	U.K	U.S.A
	Number of funds	54	5	3	2	3	8	1	1	5	12	14
	Compensation
	Management fees (%)	2.30	1.90	2.00	2.50	3.67	2.25	2.32	2.00	2.19	2.08	2.08
	Carried Interest (%)	17.96	20.00	20.00	12.50	18.67	19.50	20.09	20.00	11.50	19.67	17.64
	Clawbacks	18	1	1	0	2	5	1	0.00	2.00	2	4
	Cash Distribution	40	4	2	1	3	3	4	1.00	3.00	9	10
	Legal and Market Conditions
	Legality Index	18.84	20.44	21.13	20.41	12.8	16.67	21.67	19.53	14.51	20.41	20.85
	GNP per Capita	17868.93	29222.88	22602.23	18060.00	670.00	5176.54	25058.18	20981.60	3331.82	25108.51	28477.50
	MSCI Index	409.67	681.77	534.28	0.14	281.80	63.72	278.23	935.16	177.00	783.85	360.73
	Vintage Year of fund	2001.34	2001.60	2002.33	1999.00	2004.67	2002.00	1999.73	1999.00	2002.80	2002.00	2000.29
	Outbound Offshore	0.14	0.00	0.33	0.00	0.00	0.25	0.09	0.00	0.20	0.33	0.21
	Inbound Offshore	0.12	0.00	0.00	0.50	0.33	0.00	0.09	0.00	0.00	0.25	0.07
	Fund Manager Characteristics
	Proportion of MBA/CFA (%)	78.38	66.67	64.00	90.00	100.00	80.00	61.82	100.00	79.40	66.08	75.79
	Proportion of Ph.D (%)	9.04	10.40	3.33	2.50	2.00	7.13	7.36	33.33	1.60	14.58	8.21
	Proportion of Legally trained	8.74	15.67	13.00	2.50	33.33	3.13	10.91	0.00	0.20	4.50	4.17
	manager (%)
	Years Relevant Work	15.90	19.00	15.33	17.00	11.00	15.81	12.18	25.00	12.80	14.25	16.64
	Experience
	Funds Raised	1.51E+08	2.45E+08	5.66E+07	2.45E+08	4.90E+07	3.40E+07	3.93E+07	5.30E+08	3.88E+07	1.63E+08	1.10E+08
	Bank Institutional Investors	0.11	0.11	0.19	0.09	0.03	0.18	0.18	0.04	0.08	0.09	0.11
	Government Investors	0.13	0.00	0.00	0.05	0.34	0.24	0.21	0.02	0.27	0.13	0.06
	Pension Investors	0.20	0.67	0.40	0.05	0.02	0.17	0.11	0.00	0.22	0.16	0.19
	Endowment Investors	0.04	0.08	0.15	0.05	0.00	0.00	0.03	0.00	0.00	0.06	0.07
	limited Partnership Funds	0.59	0.40	0.67	1.00	0.33	0.13	0.27	1.00	0.40	0.83	0.86
	Industry Market/Book	4.52	4.43	2.95	4.37	10.22	3.72	3.39	4.21	3.92	3.99	3.96
	Early-stage Investee Focus	0.38	0.20	0.67	0.00	0.67	0.13	0.27	1.00	0.20	0.33	0.29

Table 4 .

 4 CorrelationsThis table presents correlation across the variables defined in Table1.

		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)	(14)	(15)	(16)	(17)	(18)	(19)	(20)	(21)	(22)	(23)	(24)	(25)
	(1) Management Fees	1.00
	(%)
	(2) Carried Interest	-0.28	1.00
	(%)
	(3) Clawbacks	0.25	-0.09	1.00
	(4) Cash	-0.05	-0.10	0.00	1.00
	Distributions
	(5) legality Index	-0.62	0.51	-0.27	-0.16	1.00
	(6) English Legal	-0.12	-0.04	0.06	-0.06 -0.10	1.00
	Origin
	(7) French Legal	-0.03	0.02	0.05	0.02	0.08	-0.71	1.00
	Origin
	(8) German Legal	0.05	0.11	-0.10	-0.09	0.17	-0.26	-0.22	1.00
	Origin
	(9) Scandinavian	0.05	0.11	-0.04	0.04	0.16	-0.19	-0.16	-0.06	1.00
	Legal Origin
	(10) GNP per Capita	-0.42	0.42	-0.21	0.18	0.75	-0.21	0.06	0.23	0.20	1.00
	(11) MSCI Index	0.18	-0.28	-0.04	0.10	-0.23	-0.10	-0.10	-0.04	-0.03	-0.14	1.00
	(12) Year of fund	0.03	-0.04	0.08	0.18	-0.14	0.17	-0.29	-0.03	0.15	0.14	0.17	1.00
	formation
	(13) Outbound	-0.26	-0.12	-0.12	0.19	0.08	0.08	-0.07	0.05	0.02	-0.01	-0.07	0.06	1.00
	Offshore
	(14) Inbound Offshore	0.03	0.05	0.04	0.18	-0.03	0.18	-0.09	-0.08	-0.06	-0.12	0.00	0.06	0.13	1.00
	(15) Proportion of	-0.01	-0.09	-0.10	-0.17 -0.09	0.35	-0.38	0.04	-0.03	-0.28	0.12	0.00	0.12	0.14	1.00
	MBA
	(16) Proportion of	0.17	0.07	0.21	-0.01 -0.11	-0.12	0.18	-0.09	-0.05	-0.01	-0.03	0.10	-0.22	-0.14	-0.19	1.00
	Ph.D
	(17) Legally trained	0.08	-0.01	0.01	0.07	-0.10	-0.21	0.29	-0.07	-0.11	0.02	-0.08	-0.11	-0.02	0.08	-0.30	0.01	1.00
	Managers
	(18) Work Experience	0.06	-0.15	0.02	0.06	-0.05	0.22	-0.19	-0.02	-0.05	-0.02	0.12	0.14	-0.09	0.03	-0.08	-0.08	-0.02	1.00
	(19) funds Raised	-0.16	-0.02	-0.08	0.19	0.10	-0.14	0.10	-0.02	-0.05	0.16	0.11	-0.04	0.02	-0.04	-0.03	-0.12	-0.03	0.12	1.00
	(20) Bank Investors	-0.07	0.12	-0.11	-0.04	0.07	-0.27	0.23	0.22	-0.14	0.09	-0.02	-0.20	-0.14	-0.13	-0.11	-0.11	0.08	-0.01	0.16	1.00
	(21) Government	0.21	-0.12	0.22	-0.10 -0.17	0.11	-0.06	-0.07	0.01	-0.30	-0.03	0.07	0.09	0.08	0.02	0.17	-0.06	-0.09	-0.21	-0.22	1.00
	Investors
	(22) Pension Investors	0.03	-0.09	-0.06	0.25	-0.04	0.13	-0.11	-0.20	0.03	0.08	0.14	0.02	-0.13	-0.12	-0.08	-0.03	-0.02	0.07	0.09	-0.10	-0.21	1.00
	(23) Endowment	-0.11	0.06	-0.04	0.14	0.21	-0.09	0.07	-0.10	0.26	0.25	-0.07	-0.04	0.15	0.03	-0.14	-0.09	0.03	-0.02	0.13	-0.05	-0.13	0.23	1.00
	Investors
	(24) Limited	-0.13	-0.03	-0.07	0.22	0.11	0.04	-0.10	-0.02	0.09	0.21	0.08	0.08	0.15	0.05	-0.08	-0.07	0.06	0.05	0.04	-0.08	-0.14	0.14	0.21	1.00
	Partnership Fund
	(25) Industry	0.06	0.02	0.09	0.00	-0.07	0.11	-0.12	0.04	0.01	-0.07	-0.10	0.03	0.07	-0.07	0.05	0.41	-0.06	-0.14	-0.10	-0.06	0.36	-0.08	-0.03	-0.10	1.00
	Market/Book
	(26) Early-Stage fund	0.10	0.19	-0.02	0.16	0.00	-0.05	-0.04	0.08	0.04	0.11	-0.08	0.07	0.04	-0.12	-0.13	0.15	0.05	-0.17	-0.01	0.12	-0.06	-0.07	0.03	0.06	0.13
	focus

Table 5 .

 5 Regression Analyses of Management FeesThis table presents OLS regressions of the fixed management fee % for the private investment fund managers. The sample comprises 123 funds from 23 countries in Africa, Australasia, Europe, and North and

	South America.

Error Coefficient Std. Error Coefficient Std. Error Coefficient Std. Error Coefficient Std. Error Coefficient Std. Error Coefficient Std. Error

							Table 5. (Continued)
		Model-1-	Model-2-	Model-3-	Model-4-	Model-5-	Model-6-	Model-7-
	Coefficient Std.
	Constant	6.088***	0.660	6.030***	0.739	5.744***	0.713	7.391***	0.882	22.000	24.410	6.685***	0.819	7.985***	1.049
	Legal Conditions
	legality Index		0.017	-0.145***	0.035	-0.133***	0.0173	-0.133***	0.017	-0.139***	0.016
	Efficiency of Judiciary System												0.116	0.077
	Rule of Law													-0.0539	0.086
	Corruption											-0.239***	0.050	-0.281***	0.075
	Risk of Expropriation													-0.112	0.199
	Risk of Contract Repudiation												-0.0609	0.164
	English Legal Origin							-0.660***	0.229			-0.344	0.220
	French Legal Origin							-0.482**	0.236			-0.109	0.229
	German Legal Origin							0.00385	0.296			0.300	0.277
	Scandinavian Legal Origin						-0.028	0.322			0.428	0.309
	Hofstede Cultural Dimensions
	Power Distance Index													-0.0161***	0.004
	Individualism													-0.00966**	0.004
	Masculinity													-0.00151	0.004
	Uncertainty Avoidance Index												0.0104**	0.004
	Market Conditions
	GNP per Capita			0.0173	0.109			-0.433***	0.057			-0.136*	0.081
	MSCI Index					0.0143	0.016	0.0218	0.017			0.0139	0.016
	Year of Fund Formation								-0.00808	0.012
	Outbound-Offshore Fund								-0.381***	0.132
	Inbound-Offshore Fund								0.139	0.185
	Tax Difference			0.000594	0.005

Error Coefficient Std. Error Coefficient Std. Error Coefficient Std. Error Coefficient Std. Error Coefficient Std. Error Coefficient Std. Error

	Fund Manager Characteristics
	Proportion of Ph.D Proportion of Ph.D	0.000675	0.003 0.00671	0.000622 0.013 0.00562 0.003	0.000641 0.013 0.00182	0.003 0.013	0.00162 0.00725*	0.003 0.015			-0.000455 0.00338	0.002 0.015	0.00167 -0.00834	0.003 0.012
	Proportion of Legally Proportion of Legally Trained Trained Fund Managers Proportion of MBA/CFA Fund Managers Proportion of MBA/CFA								0.00477 0.0312* -0.00118 -0.00470	0.003 0.018 0.002 0.011			0.00156 0.0281 -0.00119 -0.00194	0.003 0.019 0.002 0.011
	Work Experience of Principal Fund Managers Fund Managers Work Experience of Principal						0.0046 -0.0882*	0.010 0.053	0.0164 0.105*	0.011 0.057			0.00526 -0.132**	0.010 0.060
	Fund Characteristics
	Funds Raised Fund Characteristics	-0.0551*	0.033	-0.0564	0.035		-0.0525	0.035	-0.0269	0.036	-0.0466	0.032	-0.0424	0.033	-0.0646**	0.031
	Banks Institutional Investors Funds Raised	-0.0916	0.171	-0.113	0.178	0.0394 -0.187	0.206 0.184	-0.0685 -0.107	0.217 0.193	-0.0676	0.168	-0.0959 -0.129	0.198 0.201	-0.0624	0.146
	Government Investors Banks Institutional Investors						0.438 1.103	0.285 1.072	0.239 1.636	0.288 1.165			0.502* 1.639	0.268 1.206
	Limited Partnership Funds Government Investors						-0.443	1.483	-0.960	1.568	-0.0215	0.099	-0.241	1.633
	Industry Limited Partnership Funds	-0.0809	0.110	-0.0788	0.115		-0.0969 -0.0122	0.115 0.600	-0.187 -0.400	0.117 0.637			-0.155	0.107	-0.109	0.104
	Market/Book Early-Stage Investee Focus Industry Market/Book Early-Stage Investee Focus										0.191* -0.543 1.551***	0.516 0.104 0.540	-0.149	0.653	-0.0623	0.483
	R-squared Ajusted R 2 F-Statistic LogLikelihood R-squared Ajusted R 2 Proportion of Ph.D F-Statistic Proportion of Legally Trained Fund Managers 0.405 0.385 20.07 -96.81 0.260 0.241 -0.00026 0.011 13.95 Log Likelihood -301.1 Proportion of MBA/CFA Work Experience of Principal Fund Managers	0.405 0.374 13.17 -96.79 0.00001 -298.9 0.286 0.249 0.011 7.758	0.42 0.38 10.33 -95.21 0.297 0.248 0.00012 0.011 6.024 -298.0 0.00208 0.042	0.478 0.41 7.053 -88.8 0.331 0.237 -0.00066 0.012 3.523 0.00409 0.016 -295.0 -0.00235 0.009 0.0006 0.045	0.459 0.426 13.92 -90.99 0.345 0.305 8.663 -293.6	0.569 0.509 9.420 -76.96 0.284 0.184 0.012 2.830 0.017 -299.1 -0.00223 -0.00077 0.00370 0.009 -0.00075 0.045	-0.00117	0.520 0.468 9.932 -83.59 0.536 0.012 0.485 10.58 -272.5
	Fund Characteristics
	Standard errors in parentheses Funds Raised -0.01540 Standard errors in parentheses Banks Institutional Investors *** p<0.01, ** p<0.05, * p<0.1 *** p<0.01, ** p<0.05, * p<0.1 Government Investors	0.140	-0.00759	0.151	0.00398 -0.13388 0.41899	0.165 1.014 1.231	0.00673 -0.10134 0.35317	0.170 1.083 1.308	-0.01480	0.142	0.00094 -0.10807 0.4239*	0.165 1.087 1.283	-0.01442	0.160
	Limited Partnership Funds										-0.0315	0.436
	Industry Market/Book	0.01823	0.461	0.00209	0.478	-0.01102	0.512	-0.05047	0.545			-0.05325	0.540	0.02423	0.491
	Early-Stage Investee Focus										-0.01545	0.464
	Observations Pseudo R 2 Pseudo R 2 Chi-squared Chi-squared Log Likelihood Log Likelihood	0.2622 0.323 9.025 18.29 -68.00 -65.21		0.2666 0.495 9.665 43.84 -67.68 -52.44		0.2927 0.664 13.44 68.97 -65.79 -39.87	0.326 0.405 18.33 30.51 -63.35 -59.10		0.2653 0.381 9.471 26.85 -67.78 -60.93		0.312 0.496 16.25 73.80 -64.39 -37.46	0.147 0.132 21.84 19.21 -62.91 -63.44
	Standard errors in parentheses Standard errors in parentheses
	*** p<0.01, ** p<0.05, * p<0.1 *** p<0.01, ** p<0.05, * p<0.1

http://www.geert-hofstede.com/

Fund Manager Characteristics

Fund Manager Characteristics

logit regressions of the probability that a fund mandates cash distributions to institutional investors, such that the fund manager does not have the opportunity to distribute shares. We report the marginal effects, not the standard logit coefficients, in order to highlight economic significance (and hence do not report the constant even through the model includes a constant). The sample comprises 123 funds from 23 countries in Africa, Australasia, Europe, and North and South America.


Florin-Marian Birleanu
email: florin-marian.birleanu@gipsa-lab.grenoble-inp.fr

Alexandru Serbanescu

Jocelyn Chanussot

A TIME-DISTRIBUTED PHASE SPACE HISTOGRAM FOR DETECTING TRANSIENT SIGNALS

Keywords: Transient signals, phase space representation, recurrence plot, detection curve

Burst-type signals constitute an important class of transient signals, being used especially in the investigation of various physical environments by electric or acoustic means. An important issue in the analysis of this type of signals is their detection in time. In this paper, we propose a detection method that is based on the histogram of the phase space distributed over time. The method consists in representing the analyzed signal in phase space and, then, quantifying the recurrences of the trajectory obtained in this space. In this way, we derive a time -recurrence radius representation for the signal, that allows identification of positions and durations of the transients. Afterwards, we propose a method to obtain a detection curve starting from this representation of the signal. We also present here some results concerning the performance of our method in the presence of noise on both synthetic and real signals.

INTRODUCTION

Transient signals are generally characterized by very short durations over the observation and they often indicate abrupt changes or unstable states in the operation of the system that is being studied. In this paper, we deal with a particular case of transient signals, namely burst signals consisting in pulses or short oscillations. Such signals are encountered in the investigation of various physical environments using ultrasonic or electric signals. The general approach of such applications consists in transmitting a short signal in the environment to be investigated. This signal usually consists in a pulse (e.g. Gaussian pulse) or an oscillation (e.g. sine, chirp). The propagated signal is then recorded and analyzed, in order to detect changes from the original.

The analysis of transient signals addresses two issues: detection (that consists in identifying the time intervals where transients are present in the anlyzed signal) and characterization (that consists in identifying the characteristic parameters of these transients (e.g. envelope, frequency band, shape, zero crossing rate, energy)). In this work, only the detection issue is addressed. Some of the transient signal detection methods (e.g. based on spectrogram, wavelets, complex-time distribution [START_REF] Lj | Time-frequency distributions with complex argument[END_REF]) [START_REF] Gottin | Detection and Localization of Transient Sources: Comparative Study of Complex-Lag Distribution Concept Versus Wavelets and Spectrogram-Based Methods[END_REF] provide the detection curve from a representation of the signal in a transformed space -that is represented, most often, by the time-frequency plane [START_REF] Papandreou-Suppappola | Applications in Time-Frequency Signal Processing[END_REF]. In this paper we propose a detection method based on a representation of the analyzed signal in the time -recurrence radius plane. We start (section 2) by discussing the idea of time-distributing the histogram of the signal and showing its potential in detecting transient signals. We continue (section 3) with improving this idea by first representing the signal in phase space and then computing the local recurrence rate, thus obtaining the timedistributed phase space histogram (TDPSH) of the signal. We present then (section 4) a method to obtain a detection curve from the TDPSH and then we show some results (section 5). We conclude the paper (section 6) by pointing out conclusions and directions for future research in this area.

TIME-DISTRIBUTED HISTOGRAM

The histogram of a signal is obtained by dividing the range of signal values in a chosen number of (non-overlapping) bins and by summing for each bin the number of samples whose values lie inside it. However, this gives only global information about the signal, without any time information. This can be solved by replacing every signal sample with the histogram value corresponding to the bin where the value of that sample lies. We obtain thus a time-distributed histogram (TDH):

T DH (ε) (t) = hist (ε) (s(t)) , ( 1
)
where s is the signal, and hist (ε) is its histogram, computed using bins of size ε.

The potential use of the TDH is illustrated in Figure 1.(a), for a test signal composed of two Gaussian pulses and an exponentially decaying sinusoidal oscillation (plus a 15dB noise). The figure illustrates the variation of T DH (ε) for some range of the parameter ε. During the time intervals where transients are present, the density of vertical black lines in the image T DH (ε) (t) increases, which allows a fairly good visual detection of these transients. If, on the other hand, the In the following section we show that extending the idea of TDH in phase space leads to a t-ε representation that has a better noise robustness, providing thus a more precise detection of transients.

TIME-DISTRIBUTED PHASE SPACE HISTOGRAM

Representing a time series (and, in particular, a signal) in phase space is discussed in various papers in literature [START_REF] Sauer | Embedology[END_REF][START_REF] Casdagli | State space reconstruction in the presence of noise[END_REF].

We will use a representation obtained by the method of delays, that consists in transforming the signal s into an ordered set of vectors (describing a trajectory in phase space) obtained by: v i = (s(t), s(t + d), ..., s(t

+ (m -1)d)) , ( 2
)
where m is the dimension of the phase space used for representing the signal and d is the time delay between two successive components of such a vector. Discussion concerning these two parameters and the way they affect the appearance of the trajectory, as well as various methods for choosing them, can be found in [START_REF] Marwan | Encounters with neighbours. Current developments of concepts based on recurrence plots and their applications[END_REF]. An intensively studied method [START_REF] Marwan | Recurrence plots for the analysis of complex systems[END_REF] to analyze this phase space trajectory is the recurrence matrix, that is computed with:

R (m,d,ε) i,j = Θ (ε -|| v i -v j ||) , (3)
where Θ is the Heaviside step function, ε is the recurrence radius, and ||•|| is a norm that we associate to the phase space. As a value of 1 in R

(m,d,ε) i,j
means that v j lies in the neighbourhood of v i , summing after j the elements of the matrix R (m,d,ε) would have for point i the signification of "the number of vectors that are in the neighbourhood of v i ". We call the function thus obtained (followed by a normalization) timedistributed phase space histogram (TDPSH). Its mathematical expression is: In the case of representing the signal in a mono-dimensional phase space (i.e. with m = 1), R (m,d,ε) i,j becomes:

T DP SH (ε) (m,d) (t) = 1 M M j=1 R (m,d,ε) i,j , (4)
R (1,d,ε) i,j = Θ (ε -|s(i) -s(j)|) . (5)
Therefore, the neighbourhood of point s(i) is an interval of length 2ε, having its center in the value s(i). Under these conditions, the similarity between T DH (ε) (t) and T DP SH We have shown so far that the time -recurrence radius representation given by the image of T DP SH (ε) (m,d) (t) that is obtained for fixed values of the parameters m and d allows a good visual detection of the transient signals. In the following section we propose a method to process this image in order to build a detection curve.

DETECTION USING TDPSH

The detection algorithm we propose involves the following steps:

• Choose from the image T DP SH (ε) (m,d) (t) the horizontal line (L) corresponding to ε = ε opt , where:

ε opt = 1 M -1 M-1 i=1 || v i+1 -vi||. (6)
(We obtained the value of ε opt empirically.)

• Rescale L, by dividing it to its maximum value: L = L/ max(L).

• Complement L: C = 1 -L.

• Compute the envelopes of C (i.e. Anv min and Anv max ) by linear interpolation of the local minima and maxima, respectively.

• Compute the median-filtered version of C: C f = f ilt med (C), using a window of size w f ilt . • Compute the detection curve so that: C det = Anv min when C f < 0.5, and C det = Anv max when C f ≥ 0.5.

• Obtain the final detection curve by a median filtering of C det (using a window of size w f ilt ):

C det = f ilt med (C det ).
As can be noticed from this description, before using the method the following three parameters must first be chosen: m, d, and w f ilt .

RESULTS

In Figure 3 we illustrate the application of the method we proposed in the previous section, for the signal in Figure 1.(a). Figure 4 shows what happens to this detection curve as the noise level increases (i.e the signal-to-noise ratio (SNR) takes values from 30 dB to -20dB).

A quantitative view on the method is offered by the receiver operating characteristic (ROC) curves shown in Figure 5. This figure shows, for comparison purposes, also the ROC curves that we obtained for a detection using the spectrogram, on the same signal. We have shown so far how our method behaves for a synthetic signal. Figure 6 shows its behavior when it comes to detecting transients in a real signal. These signals were measured during experiments conducted in collaboration with Electricité de France [START_REF] Gottin | Detection and Localization of Transient Sources: Comparative Study of Complex-Lag Distribution Concept Versus Wavelets and Spectrogram-Based Methods[END_REF].

We point out that the m and d parameters have been chosen separately in each case, based on empirical criteria. We noticed that a value of 3 (or sometimes even 2 or 1) for m is enough for detecting transients having a fairly simple shape, i.e. without highly irregular oscillations. This is because the trajectory that corresponds to such transients is usually a simple curve, without many loops and returns. The d parameter has been chosen so that the trajectory be as wide as possible. In the same time, we wanted d to be as small as possible (in order to allow a better time localization of the phase space vectors). Therefore, we have chosen d by using visual criteria. For the median filtering involved by the algorithm we used a window of length w f ilt = 30. The optimal choice for w f ilt should take into account the minimum expected duration of the transients to be detected.

CONCLUSION

We proposed in this paper a new method for transient signal detection. The method is based on representing the analyzed signal in phase space and counting the number of recurrences that correspond to each point of the trajectory. We obtained thus a bi-dimensional representation for the signal, by plotting in the time -recurrence radius plane the local recurrence rate of the trajectory. This representation, that is in fact a generalized and improved version of the time-distributed histogram of the signal, allows a good visual detection of the transients. We proposed an empirical method to perform this detection task automatically.

Further, we are planning to develop a technique for determining the optimum phase space representation parameters, as they affect the performance of our method. The optimum choice for the size of the window to be used in the filtering of the detection curve is also important. Another research direction consists in experimenting with other phase space representation techniques (e.g. singular value decomposition).

Fig. 1 .

 1 Fig. 1. (a) TDH for a test signal composed of three transients, plus a 15dB noise. (b) TDH for noise.

Fig. 2 .

 2 Fig. 2. (a) TDPSH with m = 1 and (b) TDPSH with m = 3 and d = 15, for the test signal in Figure 1.(a).

  d) (t) (for fixed values of m and d) is clear. The second has the main advantage of being less sensitive to noise than the first one.

Figure 2

 2 illustrates this, for the same test signal used in Figure 1.(a). This figure also shows that the choice of the m and d parameters is important. The bottom figure (Figure 2.(b)) shows that for m = 3 and d = 15 the representation in the time -recurrence radius plane allows a better time localization of the transients.

Fig. 3 .

 3 Fig. 3. Detection with TDPSH. (a) The test signal. (b) TDPSH computed for ε = ε opt and rescaled. (c) The final detection curve (obtained using m = 3, d = 15, and w f ilt = 30).

Fig. 4 .Fig. 5 .

 45 Fig.4. The detection curve obtained with the TDPSH method (using the same parameters as in Figure3), for a range of SNRs.

Fig. 6 .

 6 Fig. 6. Some real signals and the corresponding detection curves. m is 3 for all three figures, and d is: (a) 15, (b) 5, (c) 2. w f ilt = 30 for all three figures.