1

“Passive” institutional investors and audit quality:

Empirical evidence from the Russell index reconstitution

Ting Dong

Stockholm School of Economics, Department of Accounting

P.O. Box 6501, SE-113 83 Stockholm, Sweden

Ting.dong@phdstudent.hhs.se

Florian Eugster

Stockholm School of Economics, Department of Accounting

P.O. Box 6501, SE-113 83 Stockholm, Sweden

Florian.eugster@hhs.se

December 2017

JEL Classification: M41, G10, G30

Keywords: Auditing, Passive Ownership, Russel index

Acknowledgements: The authors wish to thank Jason Chao for the Russel inclusion data. Florian

Eugster acknowledges the support of Handelsbanken Wallander stipendium. Part of this research was

conducted, while one author was a visiting scholar at Rotman School of Management, University of

Toronto. We also acknowledge fruitful comments from Mariassunta Giannetti, Henrik Nilsson, Kenth

Skogsvik, Martin Walker and participants in the FIRE workshop at the Stockholm School of Economics,

2017 EAA Annual Congress, 2016 Nordic Accounting Conference, and 2016 Swiss Economist Abroad

Conference.

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“Passive” institutional investors and audit quality:

Empirical evidence from the Russell index reconstitution

Abstract

Recent studies in Finance show that passive institutional investors play an active role in improving firms’

corporate governance (e.g., Appel, Gormley & Keim, 2016). In this paper, we examine whether the so-

called passive institutional investors affect audit quality, a key aspect of the corporate governance

mechanism. We exploit the yearly Russell index reassignment, which provides us with an ideal setting

to study the causal relation between passive institutional investors (i.e. index trackers) and firms’ audit

quality. Around the Russell 1000 and Russell 2000 index cutoff line, firms are arguably randomly

assigned, but there is a discontinuity in passive ownership around the index thresholds due to the

discontinuity in market-weight based index-weights assigned by Russell. By examining firms closely

surrounding this cutoff line, our instrumental variable approach provides evidence that passive mutual

funds do have a significantly positive effect on audit quality. Contrary to common understanding in the

literature regarding the non-activism of passive investors as shareholders, our results suggest that

passive investors seem to exert favorable influence on firms’ audit quality, which results in (1) higher

audit fee payment, (2) lower discretionary accruals, (3) lower probability of misstatement, and (4) lower

cost of equity capital. Cross-sectional results reveal that our results are most pronounced in firms where

(1) CFOs have high incentives to conduct earnings management, (2) the company has more short-term

investors, (3) the level of corporate governance is low, and (4) the level of information complexity is

high.

JEL Classification: M41, G10, G30

Keywords: Auditing, Passive Ownership, Russel index

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“Because the funds' holdings tend to be long term in nature (in the case of index funds, we're essentially

permanent shareholders), it's crucial that we demand the highest standards of stewardship from the

companies in which our funds invest.”

-Vanguard, 2016 1

1. Introduction

In this paper, we investigate whether passive institutional investors such as index funds have any

influence on firms’ auditing quality. Agency theory (Jensen & Meckling, 1976) postulates that the

demand for external auditing stems from the conflict of interest between the managers and investors.

Hence ownership structure, which affects the severity of this conflict, should have considerable impact

on firm’s demand for auditing services (Chow, 1982). Several prior studies show the positive impact of

institutional ownership on corporate choice and performance. 2 However, one type of institutional

investors, namely the passive investors, has been largely ignored in the literature until very recently.

The investment strategy of passive investors is to mirror the components of a benchmark index (such as

the S&P500 or Russel 1000 or 2000 index) on a market capitalization basis, aiming at delivering the

returns of the market or benchmark index. Such investment style is usually characterized with low

turnover, very low cost and minimal tracking error. Index funds, being the most visible type of passive

funds, hold nearly all stocks in the market index that is benchmarked against (Appel et al., 2016). The

massive scale of passive investment is hard to be ignored nowadays. For example, Fichtner, Heemskerk

and Garcia-Bernardo (2017) reports that, if one adds up the ownership of the largest three institutions

dominating the passive index fund industry (namely BlackRock, Vanguard, and State Street), together

they constitute the largest shareholder in 88 percent of the S&P 500 firms. By holding large blocks of a

company’s share, passive institutions have the real power to implement strategic changes (Azar,

Schmalz, & Tecu, 2017). Besides, passive owners are the “permanent” holders of a firm, they might

1 See Vanguard (2016): https://www.vanguardinvestments.se/portal/site/institutional/se/en/investments/proxy-

voting-engagement-efforts 2 See for example, Lee & Park (2009) on institutional ownership and firm value, Aghion, Reenen & Zingales,

(2013) on innovation, Mitra & Cready (2005) on the inverse relation between institutional ownership and accrual

management, and Velury & Jenkins (2006), which document positive association between institutional

ownership and the quality of earnings. We acknowledge, however, that some studies find contradictory evidence,

showing that some type of institutional owners such as transient investors induce myopic firm choice (for

example R&D expenses in Bushee (2001)).

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have strong motivation to be engaged owners.3 Consistent with the above reasoning, several latest

studies in Finance present evidence that, contrary to common belief, passive investors do play an active

role in improving firm governance (Appel et al., 2016) and transparency (Boone & White, 2015).

In this paper, we attempt to investigate whether such positive influence is manifest in firms’ audit

quality, which is a major part of the corporate governance mechanism. We use (1) audit fee, (2)

discretionary accruals, (3) misstatement risk, and (4) cost of equity capital to proxy for audit quality.

We also acknowledge the inherent endogeneity problem existing in most of the research on the relation

between ownership structure and demand for audit services. To identify the causal impact of passive

investors on audit quality, in this paper we follow Appel et al. (2016) and exploit an exogenous variation

in passive ownership that occurs around the cutoff line between two widely-used market benchmarks,

the Russel 1000 and Russell 2000 indices. Using a sample of firms in the Russell 1000/2000 indices

during years 2000 to 2006, we find that, first, passive ownership is significantly and positively associated

with audit fee payment, suggesting that passive investors generate higher demand for audit quality,

which drives up audit fees. Second, we find a significantly negative association between passive

ownership and (1) absolute value of discretionary accruals (Kothari, Leone, & Wasley, 2005) and (2)

misstatement risk (Dechow, Ge, Larson, & Sloan, 2011), indicating high quality audits translate into

less earnings management and lower likelihood of misstatement. Third, we find that passive ownership

is significantly and negatively associated with firm’s cost of equity capital.

We further explore four main topics in cross-sectional variations in the demand for audit quality due

to agency costs: (1) CFO compensation incentives, (2) pressure from short-term investors, (3) Board

independence, as well as (4) information complexity of the firms.

First, we find that the demand for higher audit quality is more pronounced for firms where the CFO

has above the median incentives to conduct earnings management due to their compensation structure

3 Mike Scott in the article “Passive investment, active ownership” (Financial Times, April 6, 2014) cited the

following from John Wilcox (Chairman of Sodali, a New York-based investment consultancy, and former head

of corporate governance at TIAA(_CREF), one of the world’s largest pension funds): “Having a passive

investment strategy has nothing to do with your behaviour as an owner…Being a “permanent” owner is not an

excuse not to engage, it is a reason to engage.” (Link: https://www.ft.com/content/7c5f8d60-ba91-11e3-b391-

00144feabdc0)

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(Bergstresser & Philippon, 2006).4 Additionally, we find that the demand for auditing quality is more

pronounced if the firm has a younger less experienced CFO.5

Second, we find that the demand for audit services of passive institutional investors is higher when

the firm has more short-term investors (proxied as transient investors as in Bushee 2001) that likely

demand short-term pressure on their earnings. We find that the demand for audit quality is more

pronounced for firms with a larger short-term investor base.

Third, we find evidence showing that passive institutional investors demand better audit quality

when concurrent corporate governance is low (proxied by a low fraction of outside independent board

members). Forth, our evidence seems to also suggest that passive investors’ impact on audit quality is

more pronounced when the information complexity (proxied by intangible asset intensity) is high.

Overall, this paper contributes to the literature in three ways. Firstly, the paper adds to the emerging

line of research that studies the “activism” of passive institutional investors using the Russell 1000/2000

index reconstitution setup. Current findings suggest that passive investors do influence firms’

governance choices 6 and improve firms’ long-term performance (Appel et al., 2016) as well as

disclosure quality and transparency (Boone & White, 2015). In the same vein, we focus our attention on

the activism of passive owners, a special group of institutional investors that have been largely ignored

in the auditing literature. We look at the effect of passive mutual fund ownership on firms’ audit quality.

Our results suggest that passive institutional owners do exert positive influence on firms’ audit quality.

Secondly, this paper adds to the broad literature that investigates the effect of institutional investors

on firm behavior. Institutional investors vary greatly in their investment style and preferences (Gillan &

Starks, 2000; Boone & White, 2015), and they are believed to influence firms through “voice” and “exit”

(Edmans, 2009). While active institutional investors could use both channels to influence corporate

4 We are using the incentive ratio as calculated in Bergstresser & Philippon (2006). The incentive ratio measures

the increase in personal compensation, when the company’s share price increases by one dollar. 5 Interestingly, we do not find the same pattern for CEOs. 6 Schmidt & Fahlenbrach (2017) argue that passive investors exert positive influence only on low-cost governance

activities such as removal of poison pills, establishment of equal voting rights, or an increase in board

independence. In terms of high-cost governance activities, however, passive investors may not have the capacity

for continuous monitoring.

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outcomes, passive institutional investors’ influencing ability is limited primarily to “voice” since they

are “forced” to hold the stocks to mimic the benchmark portfolio. Our findings, which is consistent with

some of the empirical evidence in previous literature, 7 provides further evidence that passive

institutional investors’ voicing power do place substantial influence on firm choices, such as a higher

demand for higher audit quality. Our evidence is also in accordance with some practitioners who claim

that passive investors are in fact activists for a significant portion of their portfolio.8

Thirdly, this paper provides new insights into the strand of literature that investigates the drivers of

client’s demand for audit quality. Explanations for the clients’ demand for high audit quality mostly

focus on agency conflicts: the incentives to reduce agency costs drive the demand for audit quality.

Studies usually explore the variation in agency costs and have found consistent evidence supporting that

firms with higher agency costs tend to demand higher quality auditing (for e.g., Khalil, Magnan & Cohen,

2008; Blouin, Grein & Rountree, 2007). Firms that are more capable of meeting their demand for audit

quality - those who have better governance system - are also found to exhibit higher audit quality (for

e.g., Engel, Hayes, & Wang, 2010; Cassell, Giroux, Myers, & Omer, 2012). But since audit quality,

governance system, and agency cost proxies are all choice variables, it is very hard to establish causal

inference between these variables (DeFond & Zhang, 2014). Our paper contributes to the literature by

addressing the endogeneity problem using the Russell index setting, and unlike the above-mentioned

7 For example, Carleton, Nelson & Weisbach (1998) study the process of private negotiations between pension

fund TIAA-CREF and 45 of its portfolio firms on corporate governance issues. TIAA-CREF was the single

largest pension fund and held one percent of the U.S. equity market during the study period. The pension fund

was also one of the leaders in the trend of passive investing: approximately 80% of TIAA-CREF’s fund was

placed in passively managed index funds. The study finds that, through private negotiations, TIAA-CREF was

able to reach agreements with targeted companies more than 95% of the time, and at least 87% of the targets

took actions accordingly. Guercio & Hawkins (1999) examine the impact and motivation of pension funds

through shareholder proposals. They find evidence suggesting that the investment strategy of a fund may play

an important role. For example, a heavily indexed fund might adopt activism tactics to boost the performance

of the overall market, i.e., index funds may aim to pursue tactics from a fund value maximization perspective.

Both papers provide evidence that many institutions, albeit their passive investment style, often take active role

to influence their portfolio firms. 8 For example, John Bogle, Founder and retired CEO of The Vanguard Group, stated in an interview with

Morningstar on Oct. 15, 2010 that “...This is what we call the Wall Street Rule, if you don’t like the

management sell the stock. We can’t in index fund, cannot sell a stock. So the only weapon we have, if we don’t

like the management, is to get a new management or to force the management to reform.” The whole interview

can be found at: http://www.morningstar.com/videos/359002/bogle-index-funds-power-in-corporate-

governance.aspx

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papers, our work looks at another type of owners, namely, passive investors, who are playing an

increasingly important role in the capital market.

2. Related Literature and Hypotheses Development

Agency theory predicts that equity holders, especially outsiders, demand for information about a

firm due to conflicts of interest and information asymmetry (Jensen & Meckling, 1976; Shleifer &

Vishny, 1986). Financial reports that truthfully reflect a firm’s fundamentals are vital sources of

information for shareholders. Since managers’ interest might deviate from the shareholders’, high

quality audits have become an important element in the corporate governance sphere. Firms have

incentive to employ high quality auditors to help control agency conflicts (Hope, Langli, & Thomas,

2012) and signal high quality financial reporting quality (Tendeloo & Vanstraelen, 2008; Lennox &

Pittman, 2011; Kausar, Shroff, & White 2016). Information from the financial report is also crucial for

evaluating management performance and therefore for managerial compensation (Indjejikian & Matějka,

2009).

Ample studies show that ownership structure affects the demand for audit service.9 Institutional

investors are powerful and sophisticated participants on the stock market (Walther, 1997; Chakravarty,

2001), and many have high incentives to monitor the firm. Previous research provides empirical

evidence on institutional owners’ monitoring behavior and the effect on corporate governance, firm

performance, and earnings quality, etc. In terms of auditing, the literature is relatively scarce. Kane and

Velury (2004) provide evidence that institutional investors demand high quality audit, which is proxied

by larger audit firm size in their study. Velury, Reisch, & O'Reilly (2003) find evidence indicating that

institutional owners demand higher auditing quality by influencing firms’ choice of auditors

(appointment of industry specialists). Mitra, Hossain, & Deis (2007) examines the relation between

ownership structure and audit fees. They find that diffused (less than 5%) institutional stock ownership

is positively associated with audit fees, whereas institutional block holder ownership (more than 5%

individual shareholding) has a negative association with audit fees. Furthermore, Lim, Ding, &

9 With regard to how managerial ownership affects the demand for auditing, see for example Chow (1982),

Niemi (2005), O’Sullivan (2000), Gul & Tsui (2001) and Lennox (2005).

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Charoenwong (2013) show that institutional owners’ monitoring efforts help to mitigate the audit

independence issue arising from non-audit services.

However, the above-mentioned literature in auditing seem to have ignored the fact that institutional

investors vary greatly in terms of their monitoring incentives. Elyasiani and Jia (2010) describe three

types of monitoring roles that institutional investors play: (1) active monitoring, (2) passive monitoring,

and (3) siding with managers to exploit smaller shareholders. They argue that institutions in each of the

three categories have different levels of incentive to monitor a firm. In line with their reasoning, we also

posit that it is not appropriate to group institutional owners as one homogeneous type10 in the study of

institutional investors’ influence on audit quality. More specifically, we focus on one class of investors,

namely passive investors, and investigate their impact on audit quality.

The incentive and behavior of passive institutional investors is intriguing to us due to the growing

importance of passive investing in the capital market as well as the controversial views towards it in

both academia and practice. Passive investing has gained tremendous popularity in recent years. Bogle

(2016) claims that passive index funds occupy 34% of the total equity mutual fund market in the U.S.

by year 2015. In the same year, global ETF assets exceeded $2.97 trillion, surpassing hedge fund assets

by $2 billion.11 Fichtner et al. (2017) reports that, when combined, the largest three money managers

dominating index fund industry constitute the single largest shareholder in at least 40% of all listed

companies in the U.S. in 2015, with mean ownership of 17.6%.

While passive investing is quietly changing the ownership structure of U.S. listed firms, an

important question to ask is whether they exacerbate or mitigate agency conflicts. More specifically for

our paper, what is the impact of these powerful, yet relatively new, investors on audit quality? Extant

research (such as, for example, Beasley & Petroni, 2001; Beasley & Salterio, 2001; Engel et al., 2010;

Cassell et al., 2012;) find consistent results showing that strong governance is positively associated with

10 In Accounting research, some recent papers studying the relation between institutional ownership and financial

reporting quality also investigate the impact of different types of institutional holdings separately. See, for

example, Burns, Kedia, & Lipson (2010) and Ramalingegowda & Yu (2012). 11 Source: http://www.etf.com/sections/features-and-news/global-etf-assets-surpass-hedge-fund-assets

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the demand for higher auditing quality, but the influence of passive investors on auditing has not yet

been examined.

Elyasiani and Jia (2010) classify passive indexers into the “passive monitoring” category, arguing

that their ownership should not (or only weakly) correlate with firm performance. They suggest that

only institutional investors who adopt active investing style have the incentive to monitor and hence

improve firm performance. The Economist (2015)12 also expressed a negative view: “…But the past 15

years have cast a shadow over the public company…Governance has been weakened by the rise of

passive index funds, which means that many firms’ largest shareholders are software programs…Index

funds and ETFs mimic the market’s movements, and typically take little interest in how firms are run.”

However, there are also compelling reasons that passive investors are more motivated than others

to be engaged owners. Passive investors do not have much choice on the stocks to hold, i.e., they are

unable or are reluctant to “vote with their feet” - hence they have higher motivation to be engaged

owners and adopt activist positions (Carleton et al., 1998). Since their stakes in each firm are substantial

(Cremers & Petajisto, 2009), they could either intervene firm policy through using their large voting

blocs (Appel et al., 2016) or by private negotiations (Carleton et al.,1998, Booraem, 2014) to improve

governance and related matters. Since indexers’ strategy of passively tracking a certain index at minimal

cost hampers their ability at trading on private information (Gillan & Starks, 2000; Parrino, Sias, &

Starks, 2003), Boone & White (2015) argue that passive indexers, in order to minimize their monitoring

and transaction costs, demand higher disclosure quality which lessens information asymmetry (Diamond

& Verrecchia, 1991) and reduces the costs of information gathering (Keim, 1999). 13 Using an

instrumental variable approach, Appel et al. (2016) finds a causal relation between passive mutual fund

ownership and corporate governance elements such as (1) more independent directors, (2) removal of

takeover defenses and (3), more equal voting rights through their large voting blocs. Iliev & Lowry

12 Issue Feb. 7th, 2015. Article titled “Capitalism's unlikely heroes; Shareholder activism”.

https://www.economist.com/news/leaders/21642169-why-activist-investors-are-good-public-company-

capitalisms-unlikely-heroes 13 With respect to the demand for disclosure and audit quality, Dunn & Mayhew (2004) find evidence showing

that the choice of higher quality auditing serves as a signaling mechanism to investors that the firm intends to

provide enhanced disclosure. Lee, Stokes, Taylor, & Walter, (2003, p. 378) also provide empirical evidence

confirming that “the demand for high quality auditors reflects the extent of direct accounting disclosure”.

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(2015) look at the voting behavior of mutual funds. They find that index funds are actually active voters,

a result consistent with index funds paying substantial attention in corporate governance.

Anecdotal evidence also echoes the above research. For example, an article named “Why Vanguard

and BlackRock could beat Peltz and Icahn” in Fortune14 (June 11, 2015) stated that, “Huge index-fund

asset managers are growing more willing to use their clout to influence boards and CEOs. [ …] Until

recently conventional wisdom held that indexers had zero interest in influencing their portfolio

companies. But now they’ve realized that while active managers can sell a stock they don’t like, passive

managers can’t; if it’s part of the index (say, the S&P 500), they have to own it. Passive managers have

only one way to improve their returns: influence their portfolio companies……Passive managers are

the new activists” (boldness added by the authors). Blackrock, the largest money manager with 4.7

trillion US dollar under management (reported in 2015 by Fortune15), stated in their 2014 Global

Corporate Governance and Engagement Principles that, “Where company reporting and disclosure is

inadequate or the approach taken is inconsistent with our view of what is in the best interests of

shareholders, we will engage with the company and/or use our vote to encourage a change in practice

(boldness added by the authors).”

In terms of the demand for high quality auditing by passive investors, we also find support in proxy

voting guidelines of the largest three money managers, BlackRock, Vanguard and State Street. For

instance, each of the largest three money managers explicitly puts auditor independence, audit

committee member independence and non-audit fee consideration on their checklist. Vanguard even

states that they “will evaluate on a case-by-case basis instances in which the audit firm has a substantial

non-audit relationship with the company (regardless of its size relative to the audit fee) to determine

whether independence has been compromised” (boldness added by the authors). Besides, auditor

ratification voting is also mentioned in both Vanguard and BlackRock’s guideline as a means to enhance

auditor independence and quality.16

14 Source: http://fortune.com/2015/06/11/vanguard-blackrock-could-beat-peltz-icahn/ 15 Source: http://www.etf.com/sections/features-and-news/global-etf-assets-surpass-hedge-fund-assets 16 Sources of the proxy voting guidelines can be found:

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Motivated by Appel et al. (2016) and the above reasoning, we posit that if passive investors do

indeed demand for “the highest standards of stewardship from the companies”, as claimed by

Vanguard,17 then high quality audit that facilitates truthful representation of the financial statements

might be a salient part of passive investors’ focus on good governance. Accordingly, we propose the

following hypothesis:

Hypothesis 1. Passive institutional investors have a positive impact on firms’ audit quality.

The seminal work of Chow (1982) provided first evidence that the severity of agency conflicts

affects the demand for external auditing. Parkash & Venable (1993) further argue that, since the

perceived quality of external auditing may be impaired by the purchase of nonaudit services from the

same audit firm (due to reduced independence), firms have incentive to vary the purchase of nonaudit

services depending on the expected agency costs in order to enhance the perceived level of exteral audit

quality. They showed evidence that agency cost proxies were able to significantly explain cross-

sectional differences in the demand for recurring nonaudit services. Sound corporate governance

mechanisms are considered to alleviate agency conflicts in many ways, and one of them is through

improving the production process of financial information, thereby reduce information asymmetry.

Consistent with this line of argument, extant research show that the demand for external auditing is

affected by certain corporate governance arrangements. For example, using a sample of UK listed

companies and audit fee as proxy for audit quality, O’Sullivan (2000) shows that the proportion of non-

executive directors is significant and positively associated with audit quality, suggesting that non-

executive directors tend to regard high quality auditing as a means for monitoring. Knechel & Willekens

(2006) also find evidence from Belgium listed firms that companies with an audit committee and higher

proportion of non-executive directors have on average higher audit fees.

a. BlackRock, https://www.blackrock.com/corporate/en-br/literature/fact-sheet/blk-responsible-investment-

guidelines-us.pdf;

b. Vanguard, https://about.vanguard.com/vanguard-proxy-voting/voting-guidelines/;

c. State Street, https://www.ssga.com/investment-topics/environmental-social-governance/2016/Proxy-Voting-

and-Engagement-Guidelines-US-20160301.pdf 17 Source: https://about.vanguard.com/vanguard-proxy-voting/update-on-voting/

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Based on the above discussion, we posit that passive investors, who are motivated to monitor their

portfolio firms, would put more attention on firms that exhibit higher agency cost. Hence we propose

our second hypothesis:

Hypothesis 2. Passive investors’ demand for audit quality is more pronounced when the firm has

higher agency costs.

3. Identification Strategy and research design

The key challenge in establishing causal inference on the relation between passive ownership and

audit quality is to identify an exogenous instrument. Firms that hire good quality auditors, presenting

true and fair financial reports and adopt sound governance policy might attract institutional owners.

Omitted variables might also confound the results of an OLS regression. Hence we follow Appel et al.

(2016) and use the Russell index assignment as an exogenous shock to passive mutual fund ownership.

In this section we describe our identification strategy and data in detail.

3.1. Russell 1000/2000 index construction

Our identification strategy relies on a special mechanism that Russell assigns stocks into the Russell

1000 and Russell 2000 index. Russell Investments use the last traded price on the final trading day of

May each year to calculate each stock’s total market capitalization. The largest 1000 stocks comprise

the Russell 1000 index, and the next 2000 stocks comprise the Russell 2000 index. The final

reconstitution date is the last Friday of June. After this date, firms stay within its allocated index for a

year except for certain events such as merger, acquisition and delisting. The cutoff line between Russell

1000 and 2000 each year depends on the size of the 1000th largest stock, which varies year by year.

Thereby, every year, the largest 1000th stock on the US market is classified in Russell 1000 index,

whereas the 1001st stock will be grouped into the Russell 2000 index. Several papers have shown that

there is no discontinuity in end-of-May market capitalization at the Russell 1000/2000 cutoff point

(Boone & White, 2015; Appel et al., 2016; Chang, Hong, & Liskovich, 2015). This guarantees that

fundamental characteristics of firms around the cutoff point are continuous and the assignment around

the threshold is somewhat random. Similar to Appel et al. (2016), we restrict our sample to a bandwidth

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close to this threshold, which resembles a randomized sample in line with the regression discontinuity

research design.

We obtained the member firms of the Russell 1000 and 2000 index for the year 2000 to 2006 from

Russell Investments. The sample starts from year 2000 because this was the year when Audit Analytics

started to have a relatively good coverage of US firms. Our sample ends in 2006 because after this year

Russell introduced a banding policy which does not use market capitalization strictly for reconstituting

the indices, and this change makes our identification strategy invalid. We exclude firms in the finance

industry and utility companies, due to their special business model and its reflection in the accounting.

Russell also assigns a portfolio weight to each stock once the index membership list is reconstituted,

and uses this portfolio weight to rank all stocks within an index. The weight is determined by the stock’s

float adjusted market capitalization, which is different from the market capitalization used for

determining index assignment. The float adjusted market capitalization excludes the value of those low

investible shares (e.g., shares owned by block holders, employees stock ownership plan, another Russell

index member, or the government). As described by Boone & White (2015), there is a large discontinuity

in portfolio weight for stocks around the Russell 1000/2000 cutoff line. They report that the top 20

Russell 2000 firms receive portfolio weight, on average, 46 times greater than stocks in the bottom of

Russell 1000. In Figure 1 we graph the discontinuity in index weight which clearly shows the shift in

index weight from the top of Russell 2000 to the bottom of Russell 1000 firms.

[Insert Figure 1 here]

3.2. Passive mutual fund ownership

In this section, we describe why the index weighting mechanism described above is important for

index fund investment. Index funds are mutual funds that track the performance of a specific index. In

the attempt to replicate the target index, index fund invests all, or substantially all of its assets in the

stocks that make up the index. For the same purpose, each stock in the fund portfolio is value weighted

according to the stock’s index weight. In contrast to active fund managers selecting stocks into their

portfolio, index funds passively invest in (nearly) all stocks in a market index that they benchmark

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against. Since index fund is the most visible type of passive funds (Appel et al., 2016), we follow Appel

et al. (2016) and use index fund ownership to proxy for passive institutional ownership in our main test.

We note, however, that not all passive investors are index funds.18 Passive investing could also be in the

form of allocating a stock portfolio across various index funds.19 Also, as Appel et al. (2016) pointed,

using index fund to proxy for passive ownership cannot capture the total number of passive ownership

stake, because “passive investment by some institutions, like pension funds, are not reported in the S12

mutual fund database”. But due to the fact that index funds follow the most visible passive investment

style, and there is no other superior way to measure passive ownership stake, we follow the literature

and resort to the S12 mutual fund file.

Because of the value-weighting mechanism that Russell implements, stocks that are ranked at the

bottom of the Russell 1000 index receive significantly less attention from index funds than those at the

top of the Russell 2000 index. The largest 1000th stock in its May capitalization would be ranked at the

bottom of the Russell 1000 index with small index weights and hence is of minor importance to a fund

that is benchmarked to the Russell 1000 index. In stark contrast, the largest 1001st stock would most

likely have a top ranking in the Russell 2000 index, attracting much more index fund attention due to its

heavy index weight. The total amount of institutional assets benchmarked to each index also matters.

Chang et al. (2015) report that, in year 2005, over $200 billion of assets were benchmarked against the

Russell 2000 index, whereas only $90 billion were benchmarked against the Russell 1000 index. Hence

it is quite obvious that index assignment has a heavy impact on passive ownership for firms closely

around the Russell 1000/2000 threshold. Figure 2 presents the index fund ownership for the 500 top

stocks in Russell 2000 and the 500 bottom stocks in the Russell 1000 index. Consistent with the

discontinuity in index weight at the index threshold, the distinct jump of index fund ownership stake at

point 0 illustrates the importance of index assignment.

[Insert Figure 2 here]

18 For more explanation about passive investing, see http://www.etf.com/sections/index-investor-corner/24096-

swedroe-how-to-define-passive-investment.html?nopaging=1 19 For an example of such portfolio, see https://www.thebalance.com/passive-investing-and-index-funds-

2388593

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3.3. Identification strategy

Our identification strategy follows that of Appel et al. (2016). The Russell 1000/2000 index

assignment mechanism provides us with an exogenous instrument for examining the relation between

passive ownership and audit quality. The arbitrary rule by which Russell Investment constitutes the

Russell 1000 and Russell 2000 indices produces an exogenous variation in passive ownership stake that

is arguably not correlated with firm characteristics. Stocks closely around the threshold resembles a

random sample, where those included in the Russell 2000 index receive significantly heavier “treatment”

from passive investors. Therefore, we restrict our sample to a close band around the index cutoff line

and use the inclusion into Russell 2000 index (an indicator equals to 1) to instrument for index fund

ownership. By doing so, we attempt to exclude other endogenous factors that could correlate both with

index fund ownership and audit quality from our analysis. More specifically, we estimate the causal

effect of index fund ownership on audit quality from the following (second stage) Equation:

𝑌𝑖𝑡 = 𝛼 + β𝑃𝑀𝐹𝑂𝑖𝑡 + ∑ 𝜃𝑛2𝑛=1 (MV5𝑖𝑡)𝑛 + 𝛾MV6𝑖𝑡 + 𝛿𝑋𝑖𝑡 + 𝜌𝑡 + 𝜖𝑖𝑡 (1)

where:

𝑌𝑖𝑡 : the audit quality measure for firm 𝑖 in year 𝑡. (LN_AF, Abs_Kothari, F-Score, COEC)

𝑃𝑀𝐹𝑂𝑖𝑡: Passive Mutual Fund (index fund) ownership.

MV5𝑖𝑡: Firm 𝑖’s end-of-May market capitalization in year 𝑡.

MV6𝑖𝑡: Firm 𝑖’s end-of-June float adjusted market capitalization in year 𝑡.

𝑋𝑖𝑡: A set of control variables for firm 𝑖 in year 𝑡.

𝜌𝑡: Year indicator.

Our main proxy for audit quality 𝑌𝑖𝑡 is the natural log of audit fees (LN_AF). We posit that passive

investors’ positive impact on corporate governance is the driving force for demanding higher audit

quality, and such demand may drive up audit fees (Eilifsen, Knechel, & Wallage, 2001; O’Sullivan,

2000; Niemi, 2005; Cassell et al., 2012). According to Defond & Zhang (2014), audit fee is also one of

the main measures of audit quality from the input perspective. We acknowledge that audit risk can also

16

be a source of higher audit fees. But since the market value of our sample firms are continuous, and the

Russell index assignment is rather arbitrary, we do not see why there should be a significant shift in

audit risk among our sample firms across the index threshold. Considering that our sample is close to

random, we regard the audit risk argument not applicable to our case. Nevertheless, we employ a vector

of control variables (𝑋𝑖𝑡) to take into account confounding factors. These variables include: natural log

of end-of-year total assets (LN_TA), number of geographic segments (NGS), number of business

segments (NBS), leverage (LEV), inventory scaled by total assets (INVT_SCALED), receivables scaled

by total assets (RECEIVABLES_SCALED), return on assets (ROA), indicator variable equal to one if the

firm reports special item (SPECIAL_ITEM), indicator variable equal to one if the firm reports net income

(LOSS_NI). In essence, our model is an extended audit fee model from the Simunic (1980) framework.

We add index fund ownership as our key variable into the equation and take care of the endogeneity

problem by the IV approach, described two paragraphs below.

Defond & Zhang (2014) give a comprehensive summary of audit quality measures from several

perspectives in literature. We therefore also employ two more audit quality proxies from different

perspectives to test our hypotheses. From financial reporting quality perspective, we employ the absolute

value of discretionary accruals from the Kothari model (Kothari et al., 2005) and F-score (Dechow et

al., 2011).20 From investors’ perception perspective, we use cost of equity capital as the proxy for audit

quality. In order to maximize the sample size we use the composite measure of Hou et al. (2012) that

does not require analyst forecasts to be calculated.21 The reasoning behind is the assumption that higher

quality audit curbs earnings management, and that cost of capital reduces when perceived audit quality

is high (Chen, Chen, Lobo, & Wang, 2011). We also consider the fact that the Russell reconstitution

takes place in June each year, hence the “treatment effect” may take some time to become observable.

20 We used the full Compustat universe to calibrate the Kothari model and to calculate the discretionary accruals. 21 We thank the authors for sharing their implied cost of equity measures. Basically, the composite measure of

Hou et al. (2012) is the average outcome measure of five different approaches to calculate the implied cost of

equity capital. They use the Gebhardt, Lee, & Swaminathan (2001), the Claus and Thomas (2001), the Ohlson

and Juettner-Nauroth (2005), Easton (2004) and the Gordon and Gordon (1997) model to calculate the implied

cost of equity capital for all the firms. Instead of using biased analyst forecasts they estimate the earnings

forecast with a cross-sectional prediction model.

17

Therefore, we the also take the year t+1 value of each of the audit quality proxies as our dependent

variable and present the results.

Hay, Knechel, & Wong (2006) argue that research on the determinants of audit fee following the

Simunic (1980) framework sometimes generate mixed results possibly due to missing demand factors

(and other factors). They highlight the importance of addressing the endogeneity problem in the demand

side of audit fees research, which was also emphasized by DeFond & Zhang (2014). To isolate an

exogenous variation in index fund ownership, we instrument index fund ownership (𝑃𝑀𝐹𝑂𝑖𝑡) by index

assignment. More specifically, we estimate the following (first stage) equation and use the estimated

value of 𝑃𝑀𝐹𝑂𝑖𝑡̂ in Equation (1):

𝑃𝑀𝐹𝑂𝑖𝑡 = 𝑎 + b𝑅2000𝑖𝑡 + ∑ 𝜏𝑛2𝑛=1 (MV5𝑖𝑡)𝑛 + 𝜑MV6𝑖𝑡 + 𝜔𝑋𝑖𝑡 + 𝜌𝑡 + 𝜖𝑖𝑡 (2),

Where 𝑅2000𝑖𝑡 is an indicator variable equals to one when a sample firm is assigned into Russell 2000

index and zero if the firm is a Russell 1000 firm in year 𝑡. In both stages, we include firms’ end-of-May

market capitalization (MV5it), end-of-June float adjusted market capitalization (MV6𝑖𝑡 ) as control

variables. The reason is that, within a certain index, the effect of passive ownership on audit quality

might be endogenous: market value as well as float adjusted market value could correlate with both

passive ownership and audit quality. But after conditioning on these two variables, the exclusion

restriction seems quite reasonable: being included in the top of Russell 2000 significantly increases

passive ownership, but there is no obvious reason that index inclusion could affect audit quality in a

significant way given that our sample is restricted to a close band around the index threshold.

By examining audit fees, financial reporting quality and cost of capital, we aim to provide

comprehensive evidence to test our Hypothesis 1 from both the input side (audit fees) and the output

side (the reporting quality as well as cost of capital).

For the second Hypothesis, we conduct our analysis by splitting our sample by four agency cost

proxies: (1) CFO incentives to conduct earnings management due to compensation structure, (2)

proportion of transient institutional investors, (3) corporate governance level and (4), the information

complexity of a firm proxied by intangible asset intensity.

18

3.4. Data sources

Following Appel et al. (2016), we use passive mutual fund (index fund) holdings data as the main

measure for passive institutional investor ownership for the main tests. Mutual fund holdings data is

from Thomson Reuters S12 file. We use the complimentary method by Iliev & Lowry (2015) and Appel

et al. (2016) to extract passively managed funds. Hence a fund is identified as a passively managed index

fund if the fund name includes one of the following strings: “Index, Idx, Indx, Ind_ (where _ indicates

a space), Russell, S & P, S and P, S&P, SandP, SP, DOW, Dow, DJ, MSCI, Bloomberg, KBW,

NASDAQ, NYSE, STOXX, FTSE, Wilshire, Morningstar, 100, 400, 500, 600, 900, 1000, 1500, 2000,

and 5000”. In this paper, we denote index ownership calculated by this approach as PMF_A% from

here onwards.

Because the Russell reconstitution happens in June, we use each fund’s end-of-September holding

to proxy for passive investor ownership stake. Before May 2004, funds were only required to report

twice a year regarding their holdings information, although some funds did voluntary quarterly report

to the SEC. Therefore, we populate missing September holdings by taking the average of the reported

holdings before and after this missing date, as long as the gap is within one year. If the gap is over one

year, Thomson Reuters S12 file description stated that it is very likely that the fund does not hold any

shares of the firm or the fund has undergone significant changes. Our method arrives at passive mutual

fund ownership percentage level similar to the Appel et al. (2016) paper.

For robustness check, we use two more measures to proxy for passive ownership. First, we follow

Petajisto (2013), where the author sorts all-equity mutual fund into various categories of active

management. According to the data provided by Petajisto (2013), we extract “index fund” and

“enhanced index fund” from his data22 and use as our measure for passive ownership (denoted as

PMF_P%). For the second measure, following Appel et al (2016), we use the total ownership stake of

the three largest passive institutional investors during our sample period to proxy for passive institutional

ownership as a robustness check. These are Vanguard, State Street, and Barclays Bank. To extract this

22 Data available at http://www.petajisto.net/data.html

19

data, we use the 13F file from WRDS. In similar way as above, we populate missing September holding.

We denote passive ownership calculated by this approach as PMF_Big3%. Our audit fee data is from

the Audit Analytics database. Accounting numbers are from the Compustat Annual file, and market

capitalization information is from CRSP file.

We initially could identify around 2,800 firms’ PERMCO each year in the Russell 3000 (Russell

1000 and Russell 2000 combined) firm list from year 2000 to 2006, as shown in Table 1 Column (2).

We then merge the Russell list with CRSP, S12, 13F, COMPUSTAT and Audit Analytics (AA). Finally,

we require each firm year observation to have non-missing values for all the test variables needed, and

we delete firms in the financial and utility industries, thereby we arrive at around 1,650 firms each year

on average (see Column (4) of Table 1) from the Russell 3000 firm list. The relatively low number in

year 2000 is due to the relatively poor coverage of firms by Audit Analytics in that year.

We restrict our main test sample to the bottom 500 firms of the original Russell 1000 list and the

top 500 firms of the original Russell 2000 index member list, as determined by the portfolio weights

assigned by Russell for each index. For the audit fee tests, we are eventually left with 4,031 firm year

observations (Column (5) of Table 1). And for the abnormal accrual, F-score and Cost of equity capital

tests, we have 3,356 firm year observations.

[Insert Table 1 here]

For all estimations, we include year fixed effects to take care of time trend, and we winsorize all

continuous variables at 1 and 99 percent by year.

4. Descriptive statistics and empirical results

4.1 Summary statistics

In Table 2 we report summary statistics for our main sample. We have 4,031 firm year observations

in our Audit Fee test sample (the primary test), which includes firms in the ± 500 bandwidth across the

Russell 1000/2000 cutoff line from 2000 to 2006. Out of the 4,031 observations, there are 2,008

observations from the Russell 1000 index, and 2,023 from the Russell 2000 index. Table 2 shows the

summary statistics of the full sample. The median value of index fund ownership (denoted as PMF_A%)

20

is 2.59 percentage points in our sample, which is very close to that in Appel et al. (2016) which reports

2.6 percentage point. The average value in our whole sample is 2.52 percentage point whereas Appel et

al. (2016) reports 3 percentage points. This deviation is perhaps due to the fact that the sample period in

Appel et al. (2016) starts from 1998, and they use a smaller bandwidth (±250) as their main test sample.

Our sample is limited to the years 2000 - 2006, so we choose a wider bandwidth to obtain more

observations. We nevertheless present test results also at ±250 bandwidth for robustness check.

The descriptive statistics also shows the difference between Russell 1000 and Russell 2000 firms

(untabulated for brevity). Russell 1000 is obviously comprised of larger firms, in terms of market value

(MV5), total assets (LN_TA), and return on assets (ROA). Consistent with the larger company size,

Russell 1000 firms also pay higher audit fees. The average value of LN_AF (natural log of audit fees)

for Russell 1000 firms is 13.87, while it is 13.48 for Russell 2000 firms. But despite the larger size,

Russell 1000 firms exhibit less index fund ownership (2.27 percentage points on average for PMF_A%,

for example) than Russell 2000 firms (2.77 percentage points on average for PMF_A%), which is in line

with our expectation.

4.2 Empirical results

4.2.1 First stage regression

To demonstrate that index assignment has important implication for index fund ownership stake

among firms around the cut-off line, we run the first stage regression as in equation (2).

The model is estimated over the period from year 2000 to 2006, using all three measures of passive

ownership: PMF_A%, PMF_P%, and PMF_Big3%. Table 3 shows the test results.23 We start with our

main sample using the ± 500 bandwidth, reported in Columns (1), (3), and (5). The estimated coefficient

on the indicator for Russell 2000 firms (R2000) is statistically significant at 1% level and is not sensitive

to different polynomial orders.24 Overall, the explanatory power for our first stage regression is between

0.17-and 0.24 depending on the specification. Columns (2), (4), and (6) demonstrate that the estimated

23 For the sake of brevity, we do not tabulate the control variables. 24 For brevity, we do not tabulate the test results controlling for only the first order polynomial on MV5, but the

results are consistent and statistically significant.

21

relation between R2000 index inclusion and passive ownership is robust to different choice of bandwidth

by replicating the tests for bandwidth of ±250, and the coefficient on R2000 is largely the same. Column

(6), for example, shows that, on average, firms at the top 250 band of Russell 2000 has the largest three

passive institutions’ ownership that is 1.40 percentage points higher than those in the bottom 250 band

of Russell 1000 index. Since the average value of PMF_Big3% in the whole sample is 5.78 percentage

points with standard deviation of 3.93 percentage points, the estimation from our first stage regression

indicates that index assignment does indeed have significant impact on passive ownership. Our first

stage result is also consistent with Appel et al. (2016). We would also like to point out that, following

Appel et al. (2016), we also carried out tests on active mutual fund ownership and unclassified mutual

fund ownership as additional checks. However, similar to Appel et al. (2016), we do not find evidence

of a shift in ownership stake for those two types of mutual funds at the index cut-off line, consistent

with active fund ownership not being affected by index inclusion/exclusion. For brevity, we do not

tabulate those tests.

Now we turn to second stage estimations for our first hypothesis: the impact of index fund ownership

on audit quality, proxied by (1) audit fee, (2) discretionary accruals and F-score, and (3) cost of equity

capital.

4.2.2 Audit fees

As discussed in our empirical framework, the first audit quality variable that we assess is audit fee

payment (LN_AF and F1_LN_AF).25 We note that, from this section of the paper, we present our main

results only using PMF_A% as proxy for passive ownership for the sake of brevity.26 First, we start with

a basic OLS regression to test if there is an association between audit fees and index fund ownership. In

Column (1) of Table 4, we regress current year audit fees (LN_AF) on passive ownership, market

capitalization, and other control variables (𝑋𝑖𝑡). The result indicates a positive relation between audit

fees and passive ownership, but is not statistically significant at conventional level. This can be due to

25 We use the prefix F1 for our independent variables in t+1. 26 We have nevertheless conducted each set of tests using PMF_P% and PMF_Big3% (untabulated), and all

results consistently point to the same direction.

22

a biased estimate using OLS given the endogeneity issue. We next move on to our main instrumental

variable approach in Column (2) and (3). We start with the main sample of the ±500 bandwidth around

the threshold. In Column (2) of Table 4, we present test result with both first and second order

polynomial on end-of-May market capitalization and base the test on the full sample with ±500

bandwidth. Our result shows that, 1 percentage increase in index fund ownership increases audit fee by

10 percentage points, which is statistically significant at 10% level. The magnitude of economic

significance is also sizable: one standard deviation increases in index fund ownership (PMF_A%)

increases the standard deviation of audit fee by 18%. In Column (3) we reduce the sample to ±250

bandwidth and the results seem to be stronger, consistent with index assignment having stronger impact

where it is closer to the cutoff line. We show that the impact of passive ownership on audit fee in the

year forward (t+1) from Column (4) to (6). In Column (4) we run the OLS test, showing no statistically

significant effect of passive ownership on audit fee. But Columns (5) and (6) using the IV approach

suggest that the positive influence of passive owners is persistent into year t+1. Equally importantly, the

first stage F-statistic as well as the Stock and Yogo statistic(s) both support the validity of our IV

approach. Overall, our estimations seem to indicate that index fund ownership causes firms to pay more

for their audit services.

In untabulated tables, we also test whether non-audit fees have significant correlation with passive

ownership. We do not find any evidence that index ownership cause changes in non-audit fees. This

indicates that passive investors may also help to enhance auditor independence.

Some may argue that the inherent audit risk in top Russell 2000 list firms might be higher, which

result in higher audit fees. To alleviate such concern, we have controlled for a full set of audit fee

determinants widely seen in the audit fee literature. Second, given that our sample is arguably random

across the cutoff line, especially when the bandwidth is as small as ±250, there should not be systematic

difference in audit risk among firms in our sample. Nevertheless, we next conduct our analysis using

financial reporting quality, measured by absolute value of discretionary accruals and F-score, to further

examine the effect of passive ownership on audit quality. If the positive effect of passive ownership on

23

audit fee is driven by higher risk, as some may argue, then we would expect to observe positive

association between passive ownership and earnings management or misstatement risk (F-score).

4.2.3 Discretionary accruals and misstatement risk (F-score)

In Table 5 we report the estimation from our discretionary accruals and F-score tests. First we test

the current year absolute value of performance matched accrual model from Kothari et al. (2005) in

Columns (1) to (2), with sample size from the ±500 bandwidth and the ±250 bandwidth respectively.

The negative coefficient on PMF_A% in Column (1) suggests that higher index fund ownership reduces

the level of absolute discretionary accruals, indicating higher earnings quality. Results stay statistically

significant in the same direction and magnitude for the ±250 bandwidth in Column (2). For brevity, we

only show the results which control for both first and second order polynomial on end-of-May market

capitalization, and we suppress the coefficients on all control variables.27 Columns (3) and (4) presents

the same tests for F-score, and in the same vein, we find that passive ownership reduces misstatement

risk with rather large impact. In Columns (5) to (8) we use the one year ahead (t+1) value of discretionary

accruals and F-score as dependent variable. Results indicate that, consistent with the audit fee test,

passive owners’ positive impact on financial reporting quality is not transient. To this end, we also

provide extra validity to the audit fee results by showing that the driving force of audit fee is not higher

audit risk, but rather higher demand for better audit quality.

[Insert Table 5 here]

4.2.4 Cost of equity capital

High quality audits enhance the credibility of financial statements, which may then translate into

lower level of investors’ pricing of information risk, and reduce cost of equity capital (Khurana &

Raman, 2004; Chen et al., 2011). In this section, we use cost of equity capital (by Hou et al. 2012) to

proxy for audit quality from the perspective of investors’ perception. We find that passive ownership is

negatively associated with cost of equity capital at the 1%-significance level (tabulated in Table 6), both

27 Following prior literature (e.g., Cornett, Marcus & Tehranian (2008), Chung, Firth & Kim (2002), Hope,

Thomas & Vyas (2013), Core, Hail & Verdi (2015)), we do not control for the number of segments in the tests

for accruals, F-score and cost of capital.

24

for the ±500 bandwidth and the ±250 bandwidth. The effect is also more pronounced when we use the

sample with the narrower ±250 bandwidth. And the effect seems to persist also into the year ahead,

which are demonstrated in Columns (3) and (4).

[Insert Table 6 here]

Taken together, our results in Tables 4, 5 and 6 seem to indicate that passive owners are the driving

force of the demand for higher audit quality. The effect is consistent across the input perspective and

output perspective of audit quality measures, giving strong support to our first hypothesis. In the next

section we turn to test hypothesis 2 and examine whether the passive investors have stronger impact on

audit quality where agency cost is higher.

4.2.5 Cross-sectional analysis based on agency cost

For testing H2, we split our sample by four broad indicators of agency costs, and conduct basically

the same tests as for H1. First, we posit that passive investors might demand for higher quality auditing

if the CEO/CFO of a firm have high incentive to engage in earnings management. We use this measure

for sample split because compensation plan is one of the most important governance issues that passive

institutions focus on,28 and Bergstresser & Philippon (2006) show that firms’ earnings management

behavior is related the increase in CEO’s stock-based compensation.

Our second split variable for agency cost is the proportion of transient institutional investors.

Transient investors might myopically price firms and pressure managers into a short-term focus (Bushee,

2001), a strategy in strak contrast to that of passive investors. Bushee (1998) find evidence suggesting

that transient institutional owners may encourage myopic investment behavior to meet short-term

earnings goals. Koh (2007) further finds evidence suggesting that transient ownership is associated with

aggressive earnings management for meeting/beating earnings benchmarks. Since our study focuses on

passive investors who seem to have quite the opposite goals than transient investors, we attempt to

28 Appel et al. (2016) analysed the proxy-voting policies of the largest passive investors during 2003-2006. They

summarised four major governance issues that were the focus of passive investors. In particular, “passive

investors (1) supported greater board independence, (2) opposed takeover defences, (3) opposed unequal voting

rights, as occurs when firms maintain a dual class share structure, and (4) supported compensation plans that

align management's interests with shareholders and avoid excessive awards.” (Appel et al., (2016), p. 123.)

25

examine whether passive investors’ impact on audit quality differs when we split the sample based on

the proportion of transient owners.

Thirdly, we split our sample based on the level of corporate governance of a firm, proxied by

whether the firm has an independent board. Board independence seems to be the primary issue that

passive investors focus on (see footnote 28), and several studies find that board independence is

negatively associated with earnings management (for example, Klein (2002), Osma (2008)). Therefore

we posit that passive investors are more concerned when the company’s board is less independent, and

they accordingly demand higher quality audit to enhance the credibility of the financial reports.

Our fourth proxy for agency cost is the information complexity of a firm, proxied by the intensity

of intangible assets to total assets. Intangible assets are associated with more complex information, and

their value is more uncertain to value due to the lack of active market benchmarks. Hence high level of

intangibles might hamper the assimilation of information to the market (Gu & Wang, 2005). Passive

investors aim to minimize their monitoring and transaction costs; therefore, they may be more willing

put focus on firms that have higher level of intangible assets.

Our cross-sectional tests for H2 is presented in Table 7. For the sake of brevity, we conduct this part

of the tests using only audit fee as the proxy for audit quality. First, consistent with our predictions we

find that passive investors’ impact on audit quality is more pronounced for firms where the CFO has

above the median incentives to conduct earnings management due to their compensation. Interestingly,

we do not find the same pattern for CEOs. Even more interesting is the fact that the coefficient for the

one year ahead audit fee is larger than the concurrent, which suggests a timing effect in the demand.

Second, we find that passive institutional investors’ effect on audit quality is higher when the firm

has more short-term investors (proxied by the proportion of transient investors as in Bushee (2001)) that

likely demand short-term pressure on firm earnings. Moreover, passive institutional investors seem to

demand better audit quality when concurrent corporate governance level is lower, which is proxied by

less independent board. Finally, we also observe that passive investors’ impact on audit fee is more

pronounced for the group of firms that have above median intangible assets.

26

Overall, our cross-sectional tests in this section seem to provide evidence suggesting that passive

investors’ monitoring incentive depends on the agency cost of a firm, consistent with (1) institutional

investors vary their monitoring behavior according to firm characteristic and (2), passive investors are

active owners who pay attention to the audit quality of their portfolio firms. Hence Hypothesis 2 is

supported.

[Insert Table 7 here]

4.2.6 Robustness checks

In the previous tests, we have in fact already integrated a large extent of robustness checks. Our

model specifications include two levels of polynomial order on end-of-May market capitalization, and

our sample bandwidth varies from ±500 to ±250. We also use two other measures of passive institutional

ownership for all the tests (PMF_P% and PMF_Big3%). The results are largely consistent with the tests

presented in this paper.

5. Conclusion

In this paper we find causal evidence suggesting that passive institutional investors have a positive

impact on audit quality. We focus on passive investors because, albeit their importance to the capital

market in recent years, little attention has been paid to them in Accounting and Auditing research. Our

setting allows us to use an instrumental variable approach to draw causal inference in the relation

between ownership structure and audit quality, where endogeneity has been the inherent limitation.

Contrary to common belief that passive investors are “lazy investors” lacking motivation to monitor

managers, our findings provide further evidence showing that passive investors do have an active role

in improving audit quality. Although we are not able to pin down the exact channel through which

passive investors influence the auditing process, previous literature suggests that they exercise their

power through active voting and engagement with management.

Our work might suffer from three limitations. First, since our sample period is restricted to the period

from 2000 to 2006, it is hard to generalize our conclusion to the current capital market. However, with

the increased importance of passive investing and the growth in ETF in recent years (Sullivan & Xiong,

27

2012), we expect that passive investors are playing an even more crucial role in the corporate governance

sphere. Second, due to the Russell Index setting, our sample is confined to an arguably narrow

bandwidth around the threshold and hence we warn the readers to be careful while generalizing the

result to a wider range of U.S. midcap firms. Third, we acknowledge that our measures of audit quality

are proxies for a latent variable. However, we try to provide comprehensive evidence by employing both

output and input measures following DeFond and Zhang (2014). Finally, we provide ample cross-

sectional evidence that suggest that there is a causal intervention of passive institutional investors on the

audit process.

28

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33

Appendix A - Figures

Figure 1: Index weight discontinuity around the Russell 1000/2000 cut-off point

This graph presents index weight for firms around the Russell 1000/2000 cutoff point (±500 bandwidth), and all firms are

ranked according to their float adjusted market capitalization. To the left of the point “0” are firms that belong to the Russell

2000 index, whereas to the right of the point are the firms in the Russell 1000 index. We group firms within the ±500 band into

100 bins, and each dot on the graph represent the average value of firms’ index weight in each bin.

0

.00

05

.00

1.0

015

.00

2

Ind

ex W

eig

ht

-500 0 500R2000 R1000

34

Figure 2: Index fund ownership discontinuity around the Russell 1000/2000 cut-off point

This graph presents index fund ownership for firms around the Russell 1000/2000 cutoff point (±500 bandwidth), and all firms

are ranked according to their float adjusted market capitalization. To the left of the point “0” are firms that belong to the Russell

2000 index, whereas to the right of the point are the firms in the Russell 1000 index. We group firms within the ±500 band into

100 bins, and each dot on the graph represent the average value of firms’ index fund ownership in each bin.

0

.01

.02

.03

.04

PM

FO

-500 0 500R2000 R1000

35

Appendix B - Tables

Table 1: Sample description

Year

Initial

Russell

3000 firm

list

Identifiable

with

PERMCO

After merge

with S12, 13F,

CRSP,

COMPUSTAT

and AA

After deleting

finance & utility

industry and

require each

observation

have non-

missing values

in audit fee and

control

variables.

Final audit fee

sample (500

bandwidth)

(1) (2) (3) (4) (5)

2000 3,000 2,787 2,098 1,297 462

2001 3,000 2,823 2,583 1,704 600

2002 3,000 2,896 2,719 1,764 626

2003 3,000 2,889 2,741 1,747 607

2004 2,998 2,886 2,741 1,692 583

2005 3,000 2,851 2,699 1,673 576

2006 2,987 2,836 2,684 1,666 577

Total 20,985 19,968 18,265 11,543 4,031

This table presents the process that we generate our final sample. Column (1) shows the number of firms that are initially

included in the Russell 3000 list of firms from year 2000 to 2006. Out of this initial sample, we identify 19,968 firms by

PERMCO (shown in Column (2)). Then we merge the list of firms with the S12, 13F, CRSP, COMPUSTAT, and Audit

Analytics database, and arrive at Column (3). We further delete firms in the finance and utility industry, and we delete firms

that have missing values for variables in the main (audit fee) model, the sample is then reduced to 11,543 observations

(shown in Column (4)). Finally, we restrict our sample to ±500 bandwidth around the Russell 1000/2000 cutoff line, and so

the final sample is as shown in Column (5).

36

Table 2: Descriptive Statistics29

Mean Median SD p10 p25 p75 p90 Count

PMF_A% 2.522 2.589 1.881 0 0.788 3.766 4.896 4,031

PMF_B% 1.820 1.870 1.217 0 0.907 2.660 3.426 4,031

PMF_Big3% 5.775 5.857 3.929 0 3.214 8.147 10.300 4,031

LN_AF 13.676 13.695 1.048 12.269 12.948 14.404 15.039 4,031

LN_TA 7.139 7.102 0.962 5.953 6.479 7.766 8.371 4,031

NGS 2.866 2.000 2.125 1.000 1.000 4.000 6.000 4,031

NBS 2.663 2.000 1.980 1.000 1.000 4.000 5.000 4,031

LEV 0.491 0.494 0.228 0.185 0.325 0.636 0.785 4,031

INVT_SCALED 0.105 0.072 0.119 0 0.008 0.157 0.258 4,031

RECEIVABLES_SCALED 0.134 0.118 0.100 0.024 0.059 0.185 0.258 4,031

LN_REV 6.907 7.002 1.292 5.416 6.181 7.729 8.384 4,031

ROA 0.034 0.054 0.137 -0.083 0.010 0.096 0.145 4,031

SPECIAL_ITEM 0.709 1 0.454 0 0 1 1 4,031

LOSS_NI 0.212 0 0.409 0 0 0 1 4,031

MV5 14.216 14.198 0.559 13.510 13.809 14.614 14.962 4,031

Abs_kothari 0.044 0.031 0.045 0.005 0.014 0.058 0.097 3,356

F_score 0.374 0.346 0.188 0.161 0.229 0.482 0.618 3,356

COEC 4.537 4.168 2.946 1.594 2.798 5.557 7.475 3,356

We present in this table summary statistics of our main variables. Appendix C describes the definition of each variable in detail. For tests on Audit Fees, Discretionary Accruals, F-score and COEC,

the sample sizes are slightly different due to data availability for calculating each of these audit quality proxies. Hence, in this table we report most of the variables based on the Audit Fee test

sample, which include firms with none missing data within the ±500 bandwidth around the Russell 1000/2000 cutoff line from the year 2000 to 2006. And due to the above said reason, the sample

count of discretionary accrual, F-score and COEC is somewhat lower.

29 We present for each variable the mean, median, standard deviation, 10th percentile, first quartile (p25), third quartile (p75), 90th percentile, and the number of observations.

37

Table 3: First stage regression - how index assignment affects index fund ownership

(1) (2) (3) (4) (5) (6)

PMF_A% PMF_A% PMF_P% PMF_P% PMF_Big3% PMF_Big3%

R2000 0.729*** 0.740*** 0.514*** 0.457*** 1.501*** 1.396***

(6.94) (6.71) (7.31) (6.53) (6.64) (6.01)

Observations 4,031 2,009 4,031 2,009 4,031 2,009

R-squared 0.243 0.285 0.202 0.256 0.169 0.213

Year FE Yes Yes Yes Yes Yes Yes

Bandwidth 500 250 500 250 500 250

Polynomial 2 2 2 2 2 2

Robust t-statistics in parentheses *** p<0.01, ** p<0.05, * p<0.1

This table shows the first stage regression as in equation (2). More specifically, we estimate:

𝑃𝑀𝐹𝑂𝑖𝑡 = 𝑎 + b𝑅2000𝑖𝑡 + ∑ 𝜏𝑛2𝑛=1 (MV5𝑖𝑡)𝑛 + 𝜑MV6𝑖𝑡 + 𝜌𝑡 + 𝜖𝑖𝑡,

Where 𝑃𝑀𝐹𝑂𝑖𝑡 is firm 𝑖′𝑠 end-of-September index fund ownership in year t. The indicator variable 𝑅2000𝑖𝑡 is equal to 1 if

firm 𝑖 is assigned to Russell 2000 in year t. We control for firm’s end-of-May market capitalization (MV5𝑖𝑡) as well as end-of-

June float adjusted market capitalization (MV6𝑖𝑡). We present results with 3 proxies for passive ownership as dependent

variables: PMF_A% is the percentage point of index fund ownership based on the Appel et al. (2016) approach (Columns 1

and 2); PMF_P% is the percentage point of index fund ownership based on the Petajisto (2013) classification (Columns 3 and

4), and PMF_Big3% is the is the percentage point of ownership by the 3 largest passive institutional investors during the sample

period, namely Vanguard, State Street, and Barclays Bank. We show test results for both the ±500 (Columns 1, 3 5) and ±250

bandwidth (Columns 2, 4, 6) respectively. For brevity we suppress the regression coefficients on control variables and the

constant, and we only present results with both the first and second order polynomial on the end-of-May market capitalization.

38

Table 4: Index fund ownership and audit fee

(1) (2) (3) (4) (5) (6)

Concurrent Future (F1)

LN_AF LN_AF LN_AF LN_AF LN_AF LN_AF

PMF_A% 0.007 0.102* 0.118** 0.002 0.108** 0.110*

(1.23) (1.89) (1.98) (0.48) (1.98) (1.87)

LN_TA 0.314*** 0.334*** 0.338*** 0.322*** 0.343*** 0.344***

(15.18) (9.21) (6.89) (16.18) (9.25) (7.52)

NGS 0.076*** 0.075*** 0.074*** 0.074*** 0.073*** 0.074***

(14.86) (8.63) (6.10) (14.77) (8.67) (6.58)

NBS 0.041*** 0.038*** 0.026** 0.039*** 0.036*** 0.029**

(8.45) (4.35) (1.98) (8.08) (4.13) (2.33)

LEV 0.274*** 0.305*** 0.316*** 0.287*** 0.322*** 0.258**

(5.13) (3.20) (2.61) (5.37) (3.43) (2.17)

INVT_SCALED -0.129 -0.109 -0.167 -0.040 -0.018 -0.055

(-1.57) (-0.74) (-0.91) (-0.48) (-0.11) (-0.31)

RECEIVABLES_SCALED 1.142*** 1.067*** 1.149*** 1.256*** 1.174*** 1.231***

(10.45) (5.12) (4.42) (11.37) (5.47) (4.73)

LN_REV 0.167*** 0.151*** 0.158*** 0.130*** 0.112*** 0.129***

(11.31) (5.73) (4.97) (9.13) (4.15) (4.50)

ROA -0.580*** -0.572*** -0.468** -0.414*** -0.405*** -0.334*

(-4.75) (-4.05) (-2.39) (-4.22) (-3.20) (-1.77)

SPECIAL_ITEM 0.231*** 0.225*** 0.222*** 0.231*** 0.223*** 0.239***

(11.41) (8.29) (6.21) (11.17) (8.09) (6.68)

LOSS_NI 0.085*** 0.115*** 0.139** 0.074** 0.107*** 0.143***

(2.63) (2.82) (2.55) (2.46) (2.72) (2.68)

MV5 0.905 0.414 0.687 1.389** 0.846 1.987

(1.39) (0.45) (0.41) (2.02) (0.89) (1.16)

𝑀𝑉52 -0.034 -0.014 -0.021 -0.051** -0.028 -0.067

(-1.47) (-0.42) (-0.36) (-2.09) (-0.84) (-1.11)

MV6 0.095*** 0.005 -0.006 0.103*** 0.004 0.014

(2.71) (0.08) (-0.07) (3.02) (0.06) (0.15)

Constant 0.881 5.522 3.389 -2.465 2.664 -6.032

(0.19) (0.83) (0.28) (-0.50) (0.38) (-0.49)

Observations 4,031 4,031 2,009 4,031 4,031 2,009

Year FE Yes Yes Yes Yes Yes Yes

OLS/IV OLS IV IV OLS IV IV

Bandwidth 500 500 250 500 500 250

First stage F-stat 43.78 36.60 43.78 36.60

Partial R squared 0.0179 0.0263 0.0179 0.0263

Stock &Yogo minimum

eigenvalue statistic 72.97 53.59 72.97 53.59

Robust z-statistics in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Note:

This table reports estimates of our first instrumental variable estimation to identify the effect of passive mutual fund ownership

on audit fee payment (year t and year t+1) from equation (1):

𝑌 = 𝛼 + β𝑃𝑀𝐹𝑂𝑖𝑡 + ∑ 𝜃𝑛2𝑛=1 (MV5𝑖𝑡)𝑛 + 𝛾MV6𝑖𝑡 + 𝛿𝑋𝑖𝑡 + 𝜌𝑡 + 𝜖𝑖𝑡

Where 𝑌 is audit fees in this test. Variable LN_AF is firm 𝑖′𝑠 audit fee, 𝑃𝑀𝐹𝑂𝑖𝑡 is firm 𝑖′𝑠 end-of-September index fund

ownership in year t. We control for firm’s end-of-May market capitalization (MV5𝑖𝑡) as well as end-of-June float adjusted

market capitalization (MV6𝑖𝑡). We present results for sample with both ±500 and ±250 bandwidth surrounding the Russell

39

1000/2000 cutoff. For brevity we do not show the results with only the first order polynomial on end-of-May market

capitalization. In each Column of tests, we also control for firm fundamentals (𝑋𝑖𝑡), these are: LN_TA, NBS, NGS, LEV,

INVT_SCALED, RECEIVABLES_SCALED, LN_REV, ROA, SPECIAL_ITEM, LOSS_NI (see appendix for details). Sample

period is from year 2000 till 2006.

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Table 5: Effect of index fund ownership on absolute value of discretionary accruals

(1) (2) (3) (4) (5) (6) (7) (8)

Concurrent Future (F1)

Abs_kothari Abs_kothari F_score F_score Abs_kothari Abs_kothari F_score F_score

PMF_A% -0.010** -0.009** -0.045** -0.048** -0.005 -0.009** -0.037** -0.033

(-2.47) (-1.96) (-2.30) (-2.05) (-1.51) (-2.03) (-2.05) (-1.58)

Observations 3,356 1,656 3,356 1,656 3,356 1,656 3,356 1,656

Year FE Yes Yes Yes Yes Yes Yes Yes Yes

OLS/IV IV IV IV IV IV IV IV IV

Bandwidth 500 250 500 250 500 250 500 250

Polynomial 2 2 2 2 2 2 2 2

ACCT Controls Yes Yes Yes Yes Yes Yes Yes Yes

First stage F-stat 37.02 29.63 37.02 29.63 37.02 29.63 37.02 29.63

Partial R squared 0.020 0.027 0.020 0.027 0.020 0.027 0.020 0.027

Stock & Yogo minimum eigenvalue

statistic

67.04 45.14 67.04 45.14 67.04 45.14 67.04 45.14

Robust z-statistics in parentheses *** p<0.01, ** p<0.05, * p<0.1

This table reports estimates of our second instrumental variable estimation to identify the effect of passive mutual fund ownership on discretionary accruals / F-score from equation (1):

𝑌 = 𝛼 + β𝑃𝑀𝐹𝑂𝑖𝑡 + ∑ 𝜃𝑛2𝑛=1 (MV5𝑖𝑡)𝑛 + 𝛾MV6𝑖𝑡 + 𝛿𝑋𝑖𝑡 + 𝜌𝑡 + 𝜖𝑖𝑡

We use Abs_Kothari (absolute value of discretionary accruals based on the model of Kothari et al. (2005) and F_score (Dechow et al. (2011) as the outcome variable. PMF_A% is firm 𝑖′𝑠 end-of-

September index fund ownership in year t. We control for firm’s end-of-May market capitalization (MV5𝑖𝑡) as well as end-of-June float adjusted market capitalization (MV6𝑖𝑡). For brevity, in this

table we only show test results where both the first order and the second order polynomials of MV5 are included. Our sample bandwidth varies from ± 500 to ± 250 around the Russell 1000/2000

cutoff point. In each Column, we also control for firm fundamentals (𝑋𝑖𝑡), these are: LN_TA, LEV, INVT_SCALED, RECEIVABLES_SCALED, LN_REV, ROA, SPECIAL_ITEM, LOSS_NI (see

appendix for variable definition), but we suppress the coefficients for brevity. Sample period is from year 2000 till 2006.

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Table 6: Effect of index fund ownership on cost of equity capital

(1) (2) (3) (4)

Concurrent Future (F1)

COEC COEC COEC COEC

PMF_A% -0.912*** -1.128*** -0.882*** -1.055***

(-3.05) (-3.23) (-3.02) (-2.93)

Observations 3,356 1,656 3,356 1,656

Year FE Yes Yes Yes Yes

OLS/IV IV IV IV IV

Bandwidth 500 250 500 250

Polynomial 2 2 2 2

ACCT Controls Yes Yes Yes Yes

First stage F-stat 36.99 29.62 36.99 29.62

Partial R squared 0.0197 0.0268 0.0197 0.0268

Stock and Yogo minimum eigenvalue statistic 66.92 45.11 66.92 45.11

Robust z-statistics in parentheses *** p<0.01, ** p<0.05, * p<0.1

This table reports estimates of our instrumental variable estimation to identify the effect of passive mutual fund ownership on cost of equity capital from equation (1):

𝐶𝑂𝐸𝐶 = 𝛼 + β𝑃𝑀𝐹𝑂𝑖𝑡 + ∑ 𝜃𝑛2𝑛=1 (MV5𝑖𝑡)𝑛 + 𝛾MV6𝑖𝑡 + 𝛿𝑋𝑖𝑡 + 𝜌𝑡 + 𝜖𝑖𝑡

Where 𝐶𝑂𝐸𝐶 is firm 𝑖′𝑠 cost of equity capital in year t or t+1. 𝑃𝑀𝐹𝑂𝑖𝑡 is firm 𝑖′𝑠 end-of-September index fund ownership in year t. We control for firm’s end-of-May market capitalization (MV5𝑖𝑡)

as well as end-of-June float adjusted market capitalization (MV6𝑖𝑡). Our sample bandwidth varies from ± 500 to ± 250 around the Russell 1000/2000 cutoff point. For each bandwidth, we first

present test result with both first order and second order polynomial term on MV5. All tests in this table include controls for firm fundamentals (𝑋𝑖𝑡) as in Table 5 (see Appendix C for variable

definition), but we suppress the coefficients for brevity. Sample period is from year 2000 till 2006.

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Table 7: Cross-Sectional Tests

Low High Low High

(1) (2) (3) (4)

Coefficient on PMF_A% LN_AF LN_AF F1_LN_AF F1_LN_AF

Incentive Ratio CFO -0.09 0.16** 0.01 0.18**

(-0.73) (2.15) (0.05) (2.35)

Incentive Ratio CEO 0.04 0.12 0.07 0.17

(0.23) (1.34) (0.42) (1.62)

Age CFO (young | old) 0.29* 0.02 0.41** 0.02

(1.73) (0.12) (2.22) (0.12)

Age CEO (young | old) 0.06 0.05 0.12 0.09

(0.64) (0.34) (1.19) (0.61)

Short-term investor base 0.11 0.14* 0.14 0.14*

(1.05) (1.78) (1.36) (1.72)

Independent Board (no | yes) 0.22** 0.00 0.29** -0.03

(2.09) (0.01) (2.56) (-0.27)

Intangible Intensity 0.09 0.19* 0.11 0.20**

(1.29) (1.92) (1.49) (2.04)

Controls and Year fixed effects Yes Yes Yes Yes

This table presents our cross-sectional tests using current year Audit Fee (LN_AF) and one year forward Audit Fee (F1_LN_AF) as the outcome variable. We split the sample

by the yearly median value of (1) executive incentives, (2) transient ownership (3) board independence, and (4) percentage of intangible assets over total assets of a firm. The

empirical test is carried out using the same model specification as for the tests presented in Table 4. Sample period is from year 2000 till 2006.

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Appendix C – Variable Description

Variables Description

Dependent variables

LN_AF Natural log of audit fee in current year t

F1_LN_AF Natural log of audit fee in year t+1

Abs_Kothari Absolute value of discretionary accruals in year t based on the model of

Kothari et al (2005)

F_score Measure of the current year t likelihood of earnings management or

misstatement proposed by Dechow et al. (2011)

COEC Cost of equity capital as provided by Hou et al (2012)

Key explanatory variable of research

PMF_A% Percentage point of index fund ownership based on the Appel et al. (2016)

approach.

PMF_P% Percentage point of index fund ownership based on the Petajisto (2013)

classification.

PMF_Big3%

Percentage point of ownership by the 3 largest passive institutional

investors during the sample period, namely Vanguard, State Street, and

Barclays Bank.

Instrument:

R2000 Indicator variable that is 1 if the company belongs to the Russell 2000

indices

Control variables - Market Capitalization

MV5 Firms’ end-of-May market capitalization

MV6 End-of-June float adjusted market capitalization

Control variables - firm fundamentals

LN_TA Natural log of end-of-year total assets

NBS Number of business segments

NGS Number of geographic segments

LEV Leverage as measured by total liabilities to total assets

ROA Return on assets

INVT_SCALED Inventory scaled by total assets

RECEIVABLES_

SCALED Receivables scaled by total assets

SPECIAL_ITEM Indicator variable equal to one if the firm reports special item

LOSS_NI Indicator variable equal to one if the firm reports net income

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