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Institutional investors and corporate financial policies

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Institutional investors and corporate financial policies
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Language:
English
Creator:
Scott, Ricky William
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University of South Florida
Place of Publication:
Tampa, Fla
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Subjects

Subjects / Keywords:
Dividends
Investment Policy
Liquidity
Payout Policy
R&d
Share Repurchases
Dissertations, Academic -- Economics, Finance -- Doctoral -- USF   ( lcsh )
Genre:
bibliography   ( marcgt )
non-fiction   ( marcgt )

Notes

Abstract:
ABSTRACT: Institutional investors influence corporate payout and research and development (R&D) investment policies. Higher payouts are encouraged by institutional investors, especially in firms with high free cash flow and poor investment opportunities. They also positively influence stock repurchases, particularly in firms with high information asymmetry. The substitution of stock repurchases for dividends as a percentage of total payout is encouraged by institutional investors. Institutional owners persuade firm management to increase research and development (R&D) investment overall and specifically in firms with higher stock liquidity, higher information asymmetry, lower free cash flow, and better investment opportunities. Institutional investors decrease agency costs in payout and R&D investment policy decisions.
Thesis:
Disseration (Ph.D.)--University of South Florida, 2011.
Bibliography:
Includes bibliographical references.
System Details:
Mode of access: World Wide Web.
System Details:
System requirements: World Wide Web browser and PDF reader.
Statement of Responsibility:
by Ricky William Scott.
General Note:
Title from PDF of title page.
General Note:
Document formatted into pages; contains 140 pages.
General Note:
Includes vita.

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ABSTRACT: Institutional investors influence corporate payout and research and development (R&D) investment policies. Higher payouts are encouraged by institutional investors, especially in firms with high free cash flow and poor investment opportunities. They also positively influence stock repurchases, particularly in firms with high information asymmetry. The substitution of stock repurchases for dividends as a percentage of total payout is encouraged by institutional investors. Institutional owners persuade firm management to increase research and development (R&D) investment overall and specifically in firms with higher stock liquidity, higher information asymmetry, lower free cash flow, and better investment opportunities. Institutional investors decrease agency costs in payout and R&D investment policy decisions.
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Advisor:
Pantzalis Sutton, Christos Ninon .
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Investment Policy
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Share Repurchases
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Institutional Investors and Corporate Financial Policies by Ricky W. Scott A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Finance College of Business University of South Florida Co-Major Professor: Christos Pantzalis, Ph.D. Co-Major Professor: Ninon Sutton, Ph.D. Delroy Hunter, Ph.D. Jianping Qi, Ph.D. Date of Approval: March 31, 2011 Keywords: Payout policy, Share repurchases, Dividends, R &D, Investment policy, Managerial myopia, Liquidity, Her ding Copyright 2011, Ricky W. Scott

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Dedication I dedicate this dissertation to my wife, Elisabeth, who has supported me in this life-changing endeavor. If I was not confident of our eternal bond, I could have never undertaken the most difficult project of my life. I would like to thank my beautiful daughters Sarah an d Samantha for interrupting my work on a regular basis for questions, ki sses, hugs and just to tell me about their day. My desire to demonstrate to you t hat you shouldn’t give up on your dreams just because they are difficult to achi eve has led me to reach goals I otherwise would have not been able to attain.

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Acknowledgments I thank the members of my Dissertation Committee: Dr. D elroy Hunter, Dr. Jianping Qi, and especially my Dissertation Co-Chairs Dr Christos Pantzalis and Dr. Ninon Sutton for their advice, patience, guidance and encouragement. I am also thankful for the support and counsel I received fro m Dr. Scott Besley, Dr. Patrick Kelly, Dr. Ziwei Xu, Dr. Donald Flagg, Amy Du nkel, and Dr. Dror Parnes.

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i Table of Contents List of Tables iii Abstract iv 1 Institutional Investors and Payout Policy 1 1.1 Introduction 1 1.2 Literature Review and Hypotheses 7 1.2.1 Literature Review 7 1.2.2 Hypotheses 21 1.3 Data, Methods, and Summary Statistics 25 1.3.1 Data and Methods 25 1.3.2 Summary Statistics and Data Correlations 28 1.4 The Effect of Institutional Owners on Total Payouts 33 1.4.1 Do Institutional Owners Influence Total Payouts? 33 1.4.2 Are Potential Agency Problems a Factor? 36 1.5 The Effect of Institutional Owners on Stock Repurchases 4 2 1.5.1 Do Institutional Owners Influence Stock Repurchases? 42 1.5.2 Is Information Asymmetry a Factor? 44 1.6 The Effect of Institutional Owners on Payout Compositio n 48 1.7 Conclusion 52 2 Institutional Investors and R&D Investment 54 2.1 Introduction 54 2.2 Literature Review and Hypotheses 63 2.2.1 Literature Review 63 2.2.2 Hypotheses 68 2.3 Data, Methods and Summary Statistics 73 2.3.1 Data and Methods 73 2.3.2 Summary Statistics and Data Correlations 77 2.4 The Effect of Institutional Owners on R&D Investment 81 2.4.1 Does Increased R&D Lead to Lower Earnings? 81 2.4.2 Do Institutional Owners Influence R&D Investment? 82 2.4.3 Is Stock Liquidity a Factor? 87 2.4.4 Is Information Asymmetry a Factor? 90

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ii 2.4.5 Are Potential Agency Problems a Factor? 94 2.5 Conclusion 99 List of References 102 Appendices 114 Appendix A: Payout Robustness Tests 115 Appendix B: R&D Robustness Tests 124 Appendix C: Difference GMM Methodology 130 About the Author End Page

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iii List of Tables Table 1 1: Variable Definitions Payouts 31 Table 1 2: Summary Statistics 32 Table 1 3: Correlations 32 Table 1 4: Institutional Ownership and Payouts 39 Table 1 5: Institutional Ownership, Payouts and, Inv estment Opportunities 40 Table 1 6: Institutional Ownership, Payouts, and Fre e Cash Flow 41 Table 1 7: Institutional Ownership and Stock Repurcha ses 46 Table 1 8: Institutional Ownership, Repurchases, and F irm Life-cycle 47 Table 1 9: Institutional Ownership and Payout Compo sition 51 Table 2 1: Variable Definitions R&D 79 Table 2 2: Summary Statistics 80 Table 2 3: Correlations 80 Table 2 4: Institutional Ownership and R&D 86 Table 2 5: Institutional Ownership, R&D, and Stock Li quidity 89 Table 2 6: Institutional Ownership, R&D, and Firm L ife-cycle 93 Table 2 7: Institutional Ownership, R&D, and Investm ent Opportunities 97 Table 2 8: Institutional Ownership, R&D, and Free C ash Flow 98 Table A 1: Payouts and Time Periods 117 Table A 2: Payouts, Investment Opportunities, and Fr ee Cash Flow 118 Table A 3: Payouts, Investment Opportunities and Fre e Cash Flow (GMM) 119 Table A 4: Repurchases and Time Period 120 Table A 5: Repurchases and Firm Life-cycle (GMM) 121 Table A 6: Payout Composition and Time Period 122 Table A 7: Payout Composition (GMM) 123 Table B 1: R&D Decreases 125 Table B 2: R&D and Time Period 126 Table B 3: R&D, Stock Liquidity and Firm Life-cycle (GMM) 127 Table B 4: R&D and R&D Intensity 128 Table B 5: R&D, Investment Opportunities and Free C ash Flow (GMM) 129

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iv Abstract Institutional investors influence corporate payout and research and development (R&D) investment policies. Higher payouts are encouraged by institutional investors, especially in firms with high f ree cash flow and poor investment opportunities. They also positively influence stock repurchases, particularly in firms with high information asymmetry The substitution of stock repurchases for dividends as a percentage of total payou t is encouraged by institutional investors. Institutional owners persuade fi rm management to increase research and development (R&D) investment over all and specifically in firms with higher stock liquidity, higher information asymmetry, lower free cash flow, and better investment opportunities. Instituti onal investors decrease agency costs in payout and R&D investment policy decisions.

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1 1 Institutional Investors and Payout Policy 1.1 Introduction Corporations have been using purposeful payout policies for quite some time (Lintner (1956)) despite the fact that, in theor y, payouts should have no effect on shareholder wealth, except for perhaps negati ve tax consequences (Miller and Modigliani (1961) and Poterba and Summe rs (1984)). Furthermore, repurchases and dividends are theoretically equivalent methods of payouts except where tax differentials favor one method over t he other. This raises puzzling questions articulated in Black (1976) and elsewh ere as to why firms choose to make payouts, how do they decide how much to payout and which payout method to use, and what forces shape these decisi ons. One force that appears to influence the payout decisions of corporate managers is institutional investors. Institutional invest ors are organizations that pool large sums of money which they then invest in vari ous companies. Banks, insurance companies, mutual funds, investment advisors, pe nsion funds, hedge funds, and university endowments are the most common ty pes of institutional investors. Institutions have become the dominant force in corporate ownership. They owned less than 10% of all U.S. stocks in 1955, 35% in 1975, and 53% in 2000. Now, institutions own nearly 70% of the shares of U.S. corporations. The

PAGE 9

2 predominance of institutional investors underscores the im portance of the relationship between institutional investors and corpora te financial policies. Institutional investors have been shown to affect corpora te governance in many areas (See Becht, Bolton, and Rell (2003)). In stitutional investors should be better corporate stewards than individual investors because they are more informed and influential. On the other hand, institu tional investors are agents that may take actions for their own benefit at the expense of their principals. One example in which institutional investors seem to have fa iled their principals as monitors is executive compensation. Institutional owner ship has grown rapidly since 1980. In the meantime, the average U.S. corporat e chief executive’s salary has grown from 42 times to 400 times an average worker’ s salary without an accompanying improvement in firm performance. 1 Institutional investors must actively monitor managemen t to influence financial policies effectively, but institutions with different characteristics have different incentive levels to expend costly effort to m onitor. Institutional investors are likely to fill one or more of three roles in mon itoring management: active monitoring, passive monitoring, or cooperating with ma nagement at the expense of other shareholders (Elyasiani and Jia (2010)). Since institutions are likely to be better informed and have larger holdings than other investors, engaging in active monitoring and positively influencing corporate governa nce is likely to lead to improved firm performance (Shleifer and Vishny (1986 )). Passive institutional owners such as index funds and many short-term traders are likely to have little effect on corporate governance or firm performance. Ch ung and Zhang (2011) 1 Bogle, John C. (2010) Restoring Faith in Financial Markets, Wall Street Journal (January 19).

PAGE 10

3 find that institutional investors gravitate to companies with pre-existing good governance to minimize monitoring costs. Cooperating wi th management to exploit other shareholders is likely when the institut ion has a business relationship (e.g. an investment banking relationship) with the firm (Cornett et al. (2007)). In this paper, I investigate empirically if i nstitutional investors monitor management and influence corporate payout policy. Pre vious theoretical and empirical work provides the basis for my investigation. Easterbrook (1984) and Jensen (1986) develop an agency -based theory which implies that higher payouts keep managers in the ca pital markets where monitoring costs are lower than those alternatively in curred by current shareholders. Therefore, payouts reduce agency costs. Age ncy-based theory recognizes that investment policies and payout policies ar e not independent. Payouts serve to prevent management from investing exce ss free cash flow in marginal or value-reducing projects. According to agency -based theory, better informed investors, such as institutions, should encoura ge higher payouts in firms that are likely to overinvest. Based on this theo ry, I test a prediction that institutional investors will encourage firms to pay out more of their free cash flow, especially in firms with high free cash flow and poor i nvestment opportunities. Barclay and Smith (1988) and Brennan and Thakor (19 90) construct an adverse selection theory which asserts that larger, better informed shareholders will prefer repurchases to dividends. In this theory, larger investors have a greater incentive to become informed and informed sha reholders know more about a repurchasing company’s true value than other in vestors. This knowledge

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4 can be used to profit at the expense of less informed sh areholders. If the firm is undervalued, informed investors will not offer their shares for repurchase. If the firm is overvalued, informed investors will offer thei r shares for repurchase. Other investors don’t know enough about the company to judge if it is undervalued or overvalued. Therefore after repurchases are completed, informed investors will own proportionally more of undervalued firms and pro portionally less of overvalued firms. In both cases, informed investors gain at the expense of other investors. Institutional investors are considered to be better inf ormed and generally have larger holdings in a firm than individual invest ors. Therefore, according to the adverse selection theory, institutional investors shou ld prefer repurchases. Additionally, repurchases should become a more advantage ous method of payout for institutional investors as the level of info rmation asymmetry between informed and uninformed investors in a firm grows. The prediction that I test based on the adverse selection theory is that higher inst itutional investor ownership leads to a higher level of repurchases, especia lly in firms with higher asymmetric information. Grullon and Michaely (2002) document a rising trend in repurchases beginning in 1982 with the adoption of safe harbor p rovisions which removed regulatory constraints against repurchases. They state that from 1980 to 2000, repurchases grew at an average annual rate of 26.1% w hile dividends grew at a 6.8% rate. As a result, share repurchases as a percentag e of total dividends increased from 13% to 113%. Fama and French (2001) also document an

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5 increase in repurchases during a similar time period. Ski nner (2008) reports that repurchases continue to increase until the end of his stud y in 2004. Because of this trend, in 1998, for the first time in history, U. S. corporations distributed more cash to shareholders through repurchases than through divi dends (Grullon and Ikenberry (2000)). Fama and French (2001) also provide evidence that th e number of firms paying dividends declined dramatically during the peri od studied. They conclude that repurchases do not explain the decline in dividend s as the primary effect of increases in the use of repurchases was to increase the payou t of dividend payers. In contrast, Grullon and Michaely (2002) find evidence consistent with a substitution effect. They argue that firms are increasin gly using funds for repurchases that would have otherwise been used for div idends. They note that their results differ from those of Fama and French’s beca use the measure of repurchases used by Fama and French includes not only rep urchase activity, but also stock options used for payment to labor and new equ ity issuance. If institutional investors prefer repurchases to divide nds as predicted by the adverse selection theory and they therefore encoura ge repurchases over dividends as a percentage of total payouts in the fir ms they own, institutional investors may be the impetus behind the increase in repu rchases as a percentage of total payout documented in Grullon and Michaely (2002). Consequently, I test a substitution hypothesis that instit utional investors encourage a higher level of repurchases as related to d ividends in the total payout composition. This conjecture expands on the adve rse selection theory by

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6 not only predicting that institutional owners will enco urage repurchases, but that they will encourage them at the expense of dividends. My results provide support for all three of the propo sitions that I examine: agency-based theory, adverse selection theory, and the su bstitution hypothesis. I find that an increase in institutional ownership leads to a rise in a firm’s total payout in the subsequent year, especially in firms with high free cash flow and poor investment opportunities. This indicates that instit utional investors induce managers to make payouts in firms which are likely to oth erwise overinvest thus reducing agency costs. This result provides support for th e agency-based theory. I also find that changes in institutional ownership hav e a positive relationship to subsequent stock repurchase activity, especia lly in firms with high information asymmetry. It could be argued that this in dicates that institutional investors encourage higher repurchases for tax reasons, bu t that would not explain why institutions encourage repurchases more in f irms with higher information asymmetry. It appears that institutional investors are using their information advantage to profit at the expense of ot her less informed investors thus providing evidence for the adverse selection theor y. My final result indicates that higher institutional o wnership leads to a higher percentage of the total payout composition goi ng to repurchases. Consequently, this leads to a lower percentage of the t otal payout mix going to dividends. This offers support for the substitution hypo thesis and suggests that institutional owners are at least partially responsible for the increase of repurchases in relationship to dividends found in Fama and French (2001) and

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7 Grullon and Michaely (2002). The empirical results on t he relationship between institutions and dividends are not shown because, as in Gr instein and Michaely (2005), I do not find any evidence that institutional investors influence dividends. Therefore, the positive effect which I find that insti tutional owners have on total payout is entirely attributable to their positive eff ect on repurchase levels. The prevalence of institutional investors and the pote ntial impact of their superior monitoring ability highlight the importance of institutional investors to corporate payout policies. In this paper, I investigate empirically the relationship between institutional investors and payout policy. The primary contribution of this paper is that I determine that institutional investors a re a driving force behind the increased use of stock repurchases by U.S. corporations as a means of payout and as a percentage of total payout. Additionally, I find that institutional investors encourage repurchases primarily in firms in which they h ave an informational advantage and higher total payouts predominantly in firms that should increase their payouts to avoid agency problems. 1.2 Literature Review and Hypotheses 1.2.1 Literature Review Grinstein and Michaely (2005) conduct an investigation into the relationship between institutional investors and payout policy that is similar to mine. They find that low-dividend yielding stocks have h igher institutional

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8 ownership than high-dividend yielding stocks. They also f ind that dividend-paying firms have higher institutional ownership than non-di vidend-paying firms. Their results indicate that institutions do not prefer dividen ds, but that they hold firms that pay dividends to comply with prudent-man rule re gulations. They find no evidence that institutions influence dividends. They also find that institutions prefer firms that repurchase shares, especially if the y regularly repurchase. Finally, they find that institutional investors do not influence repurchases or total payouts. This final result is at odds with my findings. I can offer some explanations for the discrepancies bet ween their results and mine. One likely explanation is that my definitio n of repurchases differs from theirs. My definition is similar to that used by Fama and French (2001). I define repurchases as the dollar amount of stock repurchases minu s the dollar amount of stock issues. I reason that if a firm repurchases a doll ar’s worth of stock in the same time period as the firm issues a dollar’s worth of st ock, then the firm has not really repurchased any shares at all. I also contend that the concept of negative repurchases is not valid for the purposes of my investigation. Therefore, if the value of stock issued is more than the value of th at repurchased, I define repurchases as being equal to zero. In contrast, Grinste in and Michaely do not subtract stock issues from stock repurchases. They do not offer an explanation for this definition, but it is likely that their reason ing follows that expressed by Grullon and Michaely (2002) who argue that new equity issuance and stock options used for payment to labor should not be include d in repurchase calculations.

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9 Another possible explanation for the difference in ou r results is that when they look at total payouts they only include firms in their sample that pay dividends and I include all firms without regard to t heir payout policies. Also, the sample they analyze concludes in 1996 while my sample run s through 2005. This is important because, as previously noted; repurchases ha ve become a comparatively more important payout method over time There are also some methodology differences. My prim ary methodology uses change regressions. They use change regressions that are very similar to mine, except they use a one-digit SIC code and a time period binary variable that differentiates their sample into two time periods. I u se firm fixed effects and a dummy variable for every year. Their vector-autoregr essive (VAR) methodology is quite similar to the Generalized Method of Moments (difference GMM) methodology I use for robustness, but there is one impor tant difference. They use the level of total payouts as the dependent variable and the level of institutional holdings as the independent variable whi le I use the change in total payouts and institutional holdings. I used levels instea d of changes to compare my outcomes to theirs and I obtained inconclusive result s which were similar to theirs. Shleifer and Vishny (1986) and others have theorized that large investors such as institutions are important monitors of firm mana gement. Institutional investors can influence management through methods such as proxy votes, shareholder proposals, publicity generation and the th reat of “voting with their feet” thus depressing stock share price as the shares are sold.

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10 Graham, Harvey, and Rajgopal (2005) survey and inter view CFOs who view institutional investors as the most important marg inal investors. These CFOs point out that institutional investors are importa nt because they can lower stock price by herding out of a stock after an earnings miss or they can provide easier access to capital leading to a lower future cost of capital if they are pleased with firm management. There is evidence that the simple act of selling shares can lead to governance changes that better disciplin e management (Gillan and Starks (2007)). Shareholder proposals spons ored by institutions get more votes and a more positive stock price reaction (Gilla n and Starks (2000)). Carleton, Nelson, and Weisbach (1998) show how one inst itution, TIAA-CREF, had a high degree of success in influencing management th rough private negotiations. There is ample evidence that institutions influence fi rm corporate governance and financial policies in a variety of areas Institutional shareholders have been shown to: reduce empire building behavior in capital expenditures and acquisitions (Xu (2008)), positively influence terminat ions of poorly performing CEOs and firm valuation over time (Aggarwal et al. ( 2010)), have a positive impact on R&D and its productivity (Aghion, Van Reenen and Zingales (2009)), and lower borrowing costs when using bonds (Bhojraj and Sengupta (2003)). There is also evidence that monitoring by institution al investors: leads to higher firm valuations and better operating performance (Fe rreira and Matos (2008)), discourages earnings management (Cornett, Marcus and Tehr anian (2008) and Chung, Firth, and Kim (2002)), improves return on asse ts (Elyasiani and Jia

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11 (2010)), and improves pay-for-performance sensitivity of executive compensation (Hartzell and Starks (2003)). Ajinkya, Bhorjraj, and Sengupta (2005) find that fi rms with greater institutional ownership are more likely to issue earning s forecasts and that these forecasts are more likely to be accurate. Cornett et al (2007) find a positive relationship between institutional ownership and opera ting cash flow returns (though just for institutions with no business relation ship with the firm). Foreign institutional ownership increases the probability of succe ssful cross-border mergers (Ferreira, Massa, and Matos (2010)). Institutio nal owners use their influence over management to use larger audit firms because such firms are perceived to provide higher quality audits (Kane and Velury (2004)). There is some evidence that institutions can have a neg ative effect on corporate governance or fail to monitor management ef fectively. Burns, Kedia, and Lipson (2010) find that institutional ownership is positively related to financial misreporting overall, although this relationship can be modified by the nature of the institutional owner. Fidrmuc, Goergen, and Renneb oog (2006) discover that stock market reaction to U.K. insider transactions is higher when the dominant shareholders are institutions. They argue that this ind icates that institutions do not monitor effectively or mitigate asymmetric informa tion problems in the U.K. Several studies have found a relationship between i nstitutional investors and payout policies. Lie and Lie (1999) contend that m anagers are more sensitive to shareholders’ tax situations if institution s own a higher percentage of the firm’s shares indicating that institutions have mor e influence on management

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12 than other owners. Jagannathan, Stephens, and Weisbach (2000) find higher institutional ownership in firms that are increasing p ayouts, especially if the increased payout comes in the form of dividends. They e xplain that tax-exempt institutions that do not share in the tax benefits of r epurchases may be behind the preference for increased dividends. It is also likely that prudent investor rules could be the cause of the preference for firms that incre ase dividends. Hankins, Flannery, and Nimalendran (2008) document that instit utions have reduced their holdings in dividend-paying stocks as the “prudent investo r” rule replaced the more-stringent “prudent man” rule in most states durin g the 1990s. Sulaeman (2008) proposes that management reacts to institutiona l investors’ leverage preferences by using repurchases to increase firm leverage if the firm’s current leverage is below the aggregate preference of its inst itutional shareholders. DeAngelo, DeAngelo, and Skinner (2000) assert that spe cial dividends are declining because institutional ownership levels are risin g and the informational advantage of institutional investors allows them to disce rn that special dividends are not generally economically different from regular dividends. Financial research offers many theories as to why firms make payouts, how payout levels are determined and why different g roups of investors appear to prefer payouts including psychological explanations, f irm quality signaling, cash flow uncertainty motivations, agency theories, and cl ientele effects. Shefrin and Statman (1984) argue that investor preference fo r cash dividends is a psychologically driven result of self-control problems an d regret aversion.

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13 Firms may be attempting to signal future profitabili ty by making payouts (Bhattacharya (1979) and Miller and Rock (1985)). Ofe r and Thakor (1987) develop a model in which firms signal their true value using dividends, repurchases or both. Vermaelen (1984) theorized that repurchases could be used as a credible signal of firm quality as managers of inferior firms could not mimic this signal without decreasing the value of their untendered shares. There is evidence that signaling is occurring, although its effe ctiveness is questionable. Massa, Rehman, and Vermaelen (2007) demonstrate that a firm provides a positive signal about itself and a negative one about its competitors when it repurchases shares. This induces competing firms to make r epurchases too in an attempt to mimic the signal. Several recent studies are not supportive of signaling theory. Amihud and Li (2006) find that abnormal returns on dividend ann ouncements have declined through the years as the level of institutional ownersh ip has risen. They argue that since institutional investors are better informed t he information content of dividend announcements has fallen. Therefore, firms hav e chosen to pay fewer dividends because the advantage of using them as costly sig nals of firm quality has fallen. The international study of Denis and Osob ov (2008) casts doubt on the use of dividends to signal because dividends are prim arily paid by firms that need to signal profitability the least (i.e. firms wit h the highest earnings). Li and Zhao (2008) find that firms with higher information asymmetry are less likely to repurchase stock or pay, initiate, or increase dividends.

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14 Fuller and Blau (2010) find some support for signalin g theory in their proposal that different explanations for dividends a re needed for different kinds of firms. They find that high quality firms pay dividen ds to dispose of excess free cash-flow while lower quality firms pay dividends to sig nal future earnings and reduce excess free cash flow. Their results also apply to t otal payout and repurchases. Several studies have determined that free cash flow le vels and composition are related to payouts. Firm management ap pears to consider the permanence of cash flows when considering whether to mak e payouts and the payout method to employ. Guay and Harford (2000) an d Jagannathan, Stephens, and Weisbach (2000) demonstrate that firms choose dividen d increases to distribute relatively permanent cash flow changes and re purchases to distribute temporary cash flow increases. Their evidence appears to b e related to the finding of Chay and Suh (2009) of a cash-flow uncerta inty effect on dividends that is unrelated to firm maturity. They conclude tha t this cash-flow uncertainty effect is stronger than agency or investment opportunity explanations for dividends. There may be an interaction between the p ermanence of cash flows and the quality of corporate governance. Harford, M ansi, and Maxwell (2008) find that firms with weaker governance (as measured by antitakeover provisions and inside ownership) avoid future payout commitments by usi ng repurchases in lieu of dividend increases. Agency-based theories (Easterbrook (1984) and Jensen (19 86)) propose that payouts can be used to mitigate potential overinv estment or empire building

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15 problems. Grullon, Michaely and Swaminathan (2002) of fer a “maturity hypothesis” to explain payouts linking the decision to pa y dividends (or repurchase) with a firm’s age and resultant decline in risk and investment opportunities. Grullon and Michaely (2004) find that repurchase announcements get a more positive reaction among firms that are likel y to overinvest. They interpret this as indicating that these firms are signali ng a reduction in agency costs. Similarly, Officer (2010) finds that dividend ini tiations get higher announcement returns in firms with poor investment op portunities and high cash flow. In tests on 4,000 companies from 33 countries, La Porta et al. (2000) offer support for an agency model that they call the “outcom e model”. In this model, firms make payouts because the opportunities to steal o r misinvest are legally restrained and minority investors are powerful enough to extract the payments. Gugler (2003) provides evidence that Austrian firms th at do not have good growth prospects make payouts. He finds that changes in di vidends result in an almost equal and opposite change in R&D and capital inv estment indicating that payouts compete with R&D and capital investment for in ternal cash flows. If management’s decision to make a payout and the form of that payout (dividend or repurchase) is influenced by the characteri stics of current stockholders or the type of stockholders that management w ants to attract, the firm is said to be influenced by a clientele effect. Lee et al. (2006) find evidence of a clientele effect in their study of Taiwanese firms. After legalization of repurchases in Taiwan in 2000, firms with more heavil y taxed shareholders were more likely to begin repurchases. This shows that manage ment was both

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16 cognizant of and deferential to their shareholders’ pr eferences or that management was pressured by shareholders to adopt a giv en payout policy. Many financial researchers have investigated how cliente le effects relate to institutional shareholders. This is true despite the finding of Brava et al. (2005) that many financial executives believe institutions are indifferent between dividends and repurchases. On the other hand, Jain (20 07) finds that firms that repurchase more have higher institutional ownership an d concludes that the institutions are attracted by the repurchasing. Bartov, Krinsky, and Lee (1998) find in a study of matched firms that firms with higher levels of institutional holdings repurchase more shares. They note that many pro minent institutional investors, notably Fidelity, have openly expressed their preference for stock repurchases over dividends. They also explain that this p reference is logical since institutions may be acting as good stewards for their investors whose income is taxable by reducing their taxes through the s ubstitution for repurchases in place of dividends. Tax differentials among varied classes of investors play a key role in clientele effects. Scholz (1992) finds that individual investors consider dividend yield and their personal tax situation when choosing in vestments providing evidence of a dividend clientele effect. Lie and Lie (1999) show that managers are more likely to choose repurchases as a means of payout if their firm has a low dividend yield indicating that they are sensitive to the tax implications of payouts to their shareholders. Allen, Bernardo, and We lch (2000) find that institutions prefer dividends because dividends are tax ed for individuals but are

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17 often untaxed for institutions. Dhaliwal and Li (2006 ) results support this view in that excess trading volume around ex-dividend days is dr iven by tax-advantaged institutional investors such as pensions trading with taxdisadvantaged individuals. Their results also highlight the important insight that institutional shareholders are not necessarily homogenous. Therefore, clientele effects may differentiate between different types of institutiona l shareholders. Moser (2007) differentiates between classes of institutional investors a nd finds that firms increase the percentage of payouts that go towards repur chases as taxdisfavored institutional ownership increases, but decrease the percentage as taxfavored institutional ownership increases. Renneboog and Trojanowski (2010) report a result that is inconsistent with tax-clientele explanations for payouts. They find that tax-exempt financial institutions in the U.K. prefer repurchases over dividen ds. On the other hand, this result is consistent with the adverse selection theory beca use the tax-exempt institutions’ informational advantage over other less informed investors could allow them to profit from repurchases at the expense o f the other investors. The 1982 adoption of safe harbor provisions in the U. S. which made it considerably easier for firms to repurchase larger quant ities of their own shares led to an upsurge in repurchases. Because of the increase in repurchases, the amount of funds dispersed to shareholders in the U.S. th rough repurchases now supersedes the amount of funds paid out through dividen ds. Dividends also seem to be declining, but the evidence on this has bee n the subject of some debate.

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18 Skinner (2008) finds that there are now three main t ypes of firms that make payouts: dividend payers that make regular repurcha ses, regular repurchasers, and occasional repurchasers. Firms that only pay dividends are now extremely rare. He argues that repurchases are fund amentally determined by earnings and they are increasingly replacing dividend s, even for firms that still pay dividends. Other research also supports the view that repurchases are replacing dividends. Grullon and Michaely (2002) argue for thi s view and find that the stock market reaction to dividend cuts is much less negative for firms that are repurchasing shares. Li and Zhao (2008) find that firm s are less likely to increase dividends if they repurchase more. Brockman, Howe, and M ortal (2008) contend that managers prefer repurchases to dividends because of tax and flexibility advantages, and rising stock market liquidity has enabled them to make repurchases their payout method of choice. Banerjee, Gatchev, and Spindt (2007) propose that stock market liquidity and dividend s are viewed as substitutes by investors. Therefore, the decline in the p ropensity to pay dividends can largely be explained by rising stock market liquidity Notably, they find that changes in repurchase and institutional ownership are no t responsible for the decline in dividends. On the other hand, there is evidence that while repu rchases are rising, they are not acting as substitutes for dividends. Fama a nd French (2001) find that fewer firms are paying dividends, but they argue that repurchases are not replacing dividends because repurchases are primarily be ing used to increase

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19 the payout of dividend payers. DeAngelo, DeAngelo, a nd Skinner (2000) report that the disappearance of special dividends does not ap pear to be related to increases in repurchases. Denis and Osobov (2008) conclud e that repurchases are not being substituted for dividends in their study of international firms. There also appears to be some question as to whether f irms are actually paying out less in dividends and more in repurchases when adjusted for firm characteristics. DeAngelo, DeAngelo, and Stulz (2006) ar gue that dividends are disappearing. They report a strong association between a company’s earned/contributed equity mix, which they use as a pro xy for the life-cycle stage of a company, and dividends. They find that when contr olling for a firm’s life-cycle stage that the decline in dividends is even more prono unced than the one found by Fama and French. Eije and Megginson (2008) provid e evidence that dividends are declining and repurchases are growing in 15 European Union countries. They find that a firm’s life-cycle stage is not related to dividends in 15 European Union countries, although the age of the fir m is associated with increased cash payouts. Grullon et al. (2010) find that the propensity to p ay using either dividends or repurchases or both has been relatively constant over the last 30 years with net payouts actually increasing over time when adjusted for firm characteristics. Denis and Osobov (2008) also find that aggregate divid ends have not declined in an international study. Boudoukh et al. (2007) show th at payout yields have replaced dividend yields as a significant predictor of eq uity returns after the enactment of the safe harbor provisions indicating the rising importance of

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20 repurchases and a relative stability in total payouts. DeAngelo, DeAngelo, and Skinner (2004) argue that aggregate real dividends pa id by industrial firms have actually increased even though dividend payers have decr eased. They find that the reduction in payers comes from firms that paid very small dividends and that the increase from the big dividend payers overwhelms the dividends not paid by the minor payers. Baker and Wurgler (2004) present a theory that manag ers cater to investors by paying dividends when investors put a premi um on dividend-paying stocks and by not paying dividends when investors prefer n on-payers. They establish an empirical link between such catering and th e change in propensity to pay dividends found in Fama and French (2001). Li and Lie (2006) extend the catering theory by finding support for the assertion th at managers consider investor demand for dividends when changing existing pa youts. They find that managers increase dividends when the dividend premium i s high and increase repurchases when the dividend premium is low. Hoberg a nd Prabhala (2009) argue that such catering does not occur if adjustments for firm risk as proxied by stock price volatility are made. Hoberg and Prabhala (20 09) find that approximately 40% of the disappearing dividends docu mented in Fama and French (2001) can be explained by firm risk as proxied b y stock price volatility. Ferris, Jayaraman, and Sabherwal (2009) provide evide nce of catering in common law countries, but not in civil law nations. Eij e and Megginson (2008) demonstrate that dividend catering is not occurring in 1 5 European Union countries.

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21 1.2.2 Hypotheses Agency costs are incurred by investors when a firm’s manag ement uses its superior knowledge of the firm’s business activities to make decisions that benefit management at the expense of shareholders. Ag ency-based free cash flow theory (Easterbrook (1984) and Jensen (1986)) sugg ests that firms with higher free cash flow and poor growth opportunities s hould have higher payouts (through higher dividends or stock repurchases). The higher payouts serve to prevent management from using discretionary funds to inv est in projects that provide less benefit to shareholders than the higher pa youts do. Therefore, institutional shareholders should attempt to reduce age ncy costs by encouraging management to raise payouts. Agency-based theory implies that larger institutional investor holdings will lead to higher payouts. The relationship predicted by this theory should be stronger in firms with high free cash flow and poor inv estment opportunities. My first hypothesis is derived from the agency-based theory: H1 : Greater institutional investor holdings will lead to higher payouts (through dividends or repurchases), especially in firms w ith high free cash flow and poor investment opportunities.

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22 The adverse selection theory of Barclay and Smith (198 8) and Brennan and Thakor (1990) asserts that stock repurchases create an opportunity for more informed shareholders to profit at the expense of less informed shareholders. In this theory, more informed investors can more capably asce rtain the true value of the firm. If the firm is undervalued, more informed investors will not offer their shares for repurchase. If the firm is overvalued, more informed investors will offer their shares for repurchase. Less informed investors don’t know enough about the company to judge if it is undervalued or overval ued. Since the managers of a firm should be at least as well informed as institutional shareholders, the adverse selection theory relies on the presumption that managers will sometimes knowingly offer to repurcha se shares that are overvalued. This is counterintuitive behavior that imp lies management is intentionally reducing the value of their firm. Yet, there is evidence that management engages in such behavior. D’Mello and Shro ff (2000) find that insiders are net sellers in the year before repurchases o f overvalued firms, while they are net buyers in the year before repurchases of u ndervalued firms. This evidence indicates that insiders are more knowledgeable about the true value of their firm and that they do sometimes conduct repurchase s even though they are aware their firm is overvalued. D’Mello and Shroff ( 2000) provide one possible explanation for this behavior. They note that repurch ases have been used to defend against hostile takeovers by increasing leverage and reducing the liquidity of the stock. In this case, management benefits from repu rchasing overvalued

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23 shares because they are more likely to retain their lucr ative executive positions if the hostile takeover does not occur. The adverse selection theory predicts that institutiona l investors will prefer repurchases if they are more informed than other inve stors about a firm’s true value. Previous research indicates that institutional inv estors are better informed than other investors. For example, Bennet, Sias, and S tarks (2003) find that institutional investors have an informational advantag e over other shareholders which varies with firm characteristics and information asym metry. Institutions also have an informational advantage in newly public firm s (Field and Lowry (2009)) and seasoned equity offerings (Chemmanura, He, and Hu (2009)) which is largely the result of better analysis of publicly avail able information. According to the adverse selection theory, institutional shareholders prefer repurchases because their informational advantage allow s them to ascertain the value of their shares more accurately than other share holders. If a firm is difficult to value accurately, it is said to have higher informat ion asymmetry (a larger information gap between informed and uninformed inv estors). Therefore, if the adverse selection theory holds, institutional investors sho uld favor repurchases more in firms that have a higher degree of informati on asymmetry. If institutions prefer repurchases equally in all firms, this could provide support for the adverse selection theory, but it also may provide evidence that institutions prefer repurchases for other reasons. For ex ample, Bartov, Krinsky, and Lee (1998) find in a study of matched firms that f irms with higher levels of institutional holdings repurchase more shares. They argue that institutions prefer

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24 repurchases over dividends to lower the tax burden on their taxable shareholders. Their reasoning can explain a preference for repurchases by institutions, but unlike the adverse selection theory, th is tax effect should not be more pronounced in firms with higher information asymm etry. My next hypothesis is based on the adverse selection theory: H2 : Higher institutional investor ownership leads to a hi gher level of repurchases, especially in firms with higher asymmetric i nformation. If institutional investors prefer repurchases to divide nds as predicted by the adverse selection theory, institutional investors ma y be the driving force behind the gradual substitution of repurchases for divi dends found by Grullon and Michaely (2002). As a result, institutional investor s may encourage repurchases over dividends as a method of payout. This sub stitution hypothesis predicts that an increase in institutional ownership will lead to an increase in repurchases as a percentage of total payout. H3 : Higher institutional investor ownership leads to a hi gher percentage of total payout going toward repurchases and a lower per centage of total payout going towards dividends. An endogenous relationship exists between institutional investors and payout policy so simply showing a relationship between institutional investors

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25 and payout policy will not provide sufficient evidence to support any of the payout policy theories presented in this paper. Causality is al so important. The causal relationships in the payout policy theories state that a ll else being equal: agencybased theory predicts that institutional investor changes influence total payout (dividends and stock repurchases) policy changes, adverse sel ection theory predicts that institutional investor changes influence st ock repurchase policy changes, and the substitution hypothesis states that instit utional investors have a positive influence on the percentage of total payout which is made up of stock repurchases. 1.3 Data, Methods, and Summary Statistics 1.3.1 Data and Methods I gather yearly institutional and insider ownership d ata from CDA / Spectrum Compact Disclosure for each year from 1990 to 2 005. Financial firms and utilities are excluded because they are highly regu lated by the government. The ownership data is then merged with Compustat data. The final sample includes 10,668 firms and 79,890 firm-years. Some firms are missing data or not present in the sample for enough firm-years to perfor m certain analysis. In such cases, they are not used. Annual dividends and stock repurchases are measured in do llars and scaled by the dollar book value of assets. Repurchases are defined as the dollar

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26 amount of stock repurchases minus the dollar amount of st ock issues. If stock issues are greater than stock repurchases, the repurchase amo unt is set to zero. Changes in repurchases are measured as the repurchases of the current year minus repurchases of the previous year, divided by previ ous year book value of assets. Changes in dividends are measured similarly. Total payout is defined as the sum of the dollar value of common dividends and r epurchases. Fama and French (2001) find in a study of U.S. firms that dividends are trending through time. They also find that firm prof itability, size and growth opportunities are related to dividends. Therefore, I control for differences across firms using variables that control for these relationship s. Profitability is proxied by earnings before interest and taxes scaled by total assets. Size is measured using log of market value and log of revenue. I use q to control for growth opportunities. Following Dlugosz et al. (2006), I calculate the varia ble q as the ratio of the market value of assets to the book value of assets where ma rket value is calculated as the sum of the book value of assets and the m arket value of common stock less the book value of common stock and deferred taxes. All regressions include dummy variables for each year of the data sample to control for time effects on the relationship between instituti onal ownership and payouts. DeAngelo, DeAngelo, and Stulz (2006) report a stron g association between a company’s earned/contributed equity mix, whi ch they use as a proxy for the life-cycle stage of a company, and dividends. T herefore, the earned/contributed equity mix defined as retained ear nings to the book value of total equity is used to control for firm life-cycle stage Firm stock turnover is

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27 included as a control because Banerjee, Gatchev and Spind t (2007) find that turnover is related to dividends. Jensen (1986) propos es debt can substitute for dividends, so firm debt to asset ratio is included. The measure of cash flow used in my analysis of agency-b ased free cash flow theory is net income plus depreciation and amorti zation minus capital expenditures. Notably, this cash flow measure does not subtract dividends or repurchases as many measures of cash flow do. This is done to simplify the analysis of dividends, repurchases or payouts as a percenta ge of free cash flow. The cash flow measure is divided by total book value of assets to provide scale. The detailed definitions of all variables are shown i n Table 1 1. If there is a relationship between institutional invest ors and payouts, it is difficult to discern if institutional investors influence payouts or if payouts influence institutional investors or both. Therefore, I have to adopt a regression methodology which accounts for endogeneity and establish es causality. 2 To help address this causality issue, I run regressions on ch anges in dependent variables from year t – 1 to t on changes in independent variables from t – 2 to t – 1 to establish causality. All regressions use firm fix ed effects. Firm fixed effect regressions are useful because they cont rol for all stable characteristics of a firm (including industry), whether me asured or not. This appealing feature of firm fixed effects regressions comb ined with the use of yearly dummy variables to control for time-varying om itted characteristics helps to control for endogeneity issues in my analysis. Using th e yearly dummy 2 I attempted two-stage least squares’ (instrumental variables) regressions but was unable to come up with instrumental variables which were stat istically and conceptually sound.

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28 variables with fixed effects effectively gives each year i ts own intercept. Intercepts in fixed effects regressions are calculated as an average value of the unobserved fixed effects for each firm which is not relev ant in my analysis. The yearly intercept values are also not relevant to my an alysis. Therefore, the intercept term and yearly dummy coefficients are not re ported in my regression results. It is highly probable that the relationship between in stitutional ownership and payouts is an endogenous one. Although I use control variables in the change regressions to control for endogeneity, it is stil l desirable to use an alternate method to further address potential endogen eity. Therefore, I also use the Arellano and Bond (1991) difference Generalized Method of Moments (difference GMM) methodology. This methodology is ide al for use in panel samples with a limited number of time periods and a la rge number of firms. Difference GMM is useful when independent variables ar e not strictly exogenous and when firm fixed effects exist. An in-depth explana tion of the difference GMM method used in this paper is included in Appendix C. 1.3.2 Summary Statistics and Data Correlations Table 1 2 displays selected firm characteristics for my sample. Panel A includes all firms in the sample and panel B includes onl y firms that have a payout (either dividends or stock repurchases or both). S tatistics are shown for

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29 two time periods, 1990 – 1997 and 1998 – 2005, and f or the total sample. Means are shown and medians are shown in parentheses below. Some patterns appear in the data for all firms and i n firms with a payout. The percentage ownership of institutional investors ( Inst ) increases over time. Firm size ( MktCap ) and q increase from the first time period to the next as wel l. Retained earnings to total equity ( LifeCycle ), a proxy for firm life-cycle, indicates that firms included in the sample are less mature in the later years. The median firm retained earnings to total equity is positive dem onstrating that in most firmyears, firms are mature enough to have earned positive earnings during their lifetime. In contrast, the average retained earnings to total equity is negative indicating a skewness towards the large minority (over 3 8%) of the firm-years with negative retained earnings. Firms with a payout have higher institutional ownersh ip, a larger size and a lower q than those without a payout. Firms that have a payou t have a higher median and slightly lower mean in retained earnings t o total equity. Table 1 2 also displays summary statistics for payout-rel ated variables in Panels C and D. Only means are shown because medians are zero for almost all of the variables. As expected, all payout variables are lower in Panel C which includes all firms than in Panel D which only includes f irms that have a payout. Consistent with Fama and French (2001), dividends to asse ts ( Div ) goes down over time as repurchases to assets ( Repurch ) goes up. Total payout increases ( PayIncr ) outnumber total payout decreases ( PayDecr ). The percentage of firms

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30 increasing repurchases per share ( RepIncr ) is higher than the percentage of firms decreasing repurchases per share ( RepDecr ). Table 1 3 presents a correlation table for selected fi rm variables which are important in my analysis. Correlations that are sig nificant at the 5% level are marked with an asterisk. Total payout to assets ( Payout ) is significantly positively correlated with repurchases to assets ( Repurch ), institutional ownership ( Inst ) and market value of common stock ( MktCap ). Repurchases to assets is significantly positively correlated with institutional ow nership and market value of common stock. Institutional ownership is significantly positively corre lated with market value of common stock and negatively correlated with T obin’s q ( q ). Market value of common stock, retained earnings to total equity and T obin’s q are all not significantly correlated with each other.

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31 Table 1 1: Variable Definitions Payouts Variable Description Definition Panel A: Summary Statistics and Correlation Table V ariables N Number of Firms The number of firms. Inst Institutional Ownership The fraction of shares owne d by institutions. MktCap Market Capitalization The dollar market value of common stock in millions. LifeCycle Firm Life-cycle The ratio of retained earnings to t otal equity. q Investment Opportunities Market value of assets to the book value of assets CashFlow Free Cash Flow Free cash flow to total assets. Div Dividend Ratio Dividends to book value of assets. Payout Payout Ratio Total payout divided by book value of assets. Repurch Stock Repurchase Ratio Stock repurchases to book va lue of assets. PayIncr Payout Increases The percentage of firms which increased their total payout per share. PayDecr Payout Decreases The percentage of firms which decreased their total payout per share. RepIncr Stock Repurchase Increases The percentage of firms which increased their repurchases per share. RepDecr Stock Repurchase Decreases The percentage of firms which increased their repurchases per share. Panel B: Regression Dependent Variables (Measured as changes in values from year t – 1 to t .) Payout Payout Ratio Total payout divided by book value of assets. Repurch Stock Repurchase Ratio Stock repurchases to book va lue of assets. Panel C: Regression Independent Variables (Measured as changes in values from year t – 2 to t 1.) Inst Institutional Ownership The fraction of shares owne d by institutions. CashFlow Free Cash Flow Free cash flow to total assets. q Investment Opportunities Market value of assets to the book value of assets Debt Debt Ratio Debt to assets. Turnover Stock Turnover Firm common stock turnover. LifeCycle Firm Life-cycle The ratio of retained earnings to t otal equity. MktCap Market Capitalization The dollar market value of common stock in millions. ROA Return on Assets Earnings before interest and taxes divided by total assets. Insider Insider Ownership The fraction of shares owned by i nsiders. Insider2 Insider Ownership Squared The squared value of Insider. Revenue Revenue The logarithm of firm revenue.

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32 Table 1 2: Summary Statistics Panel A: All Firms Years N Inst MktCap LifeCycle q CashFlow 1990 1997 37,492 28.9% 2,106 -0.69 2.81 -0.16 (23.6%) (163) (0.29) (1.85) (0.01) 1998 2005 42,398 33.3% 4,891 -0.53 4.68 -0.39 (25.8%) (350) (0.18) (1.86) (0.01) Total 79,890 31.3% 3,603 -0.61 3.81 -0.28 (24.6%) (239) (0.24) (1.85) (0.01) Panel B: Firms with a Payout 1990 1997 13,934 37.9% 4,858 0.46 2.07 0.03 (38.0%) (547) (0.64) (1.75) (0.04) 1998 2005 15,716 42.8% 10,806 -1.49 2.22 0.02 (43.9%) (1,146) (0.57) (1.75) (0.04) Total 29,650 40.5% 8,030 -0.57 2.15 0.02 (40.4%) (816) (0.61) (1.75) (0.04) Panel C: All Firms Years Div Repurch PayIncr PayDecr RepIncr RepDecr 1990 1997 0.81% 0.60% 24.80% 18.70% 13.66% 12.79% 1998 2005 0.66% 1.04% 24.57% 20.65% 17.56% 16.58% Total 0.73% 0.83% 24.67% 19.78% 15.82% 14.89% Panel D: Firms with a Payout 1990 1997 2.21% 1.64% 65.60% 33.06% 35.82% 21.84% 1998 2005 1.80% 2.82% 64.70% 34.83% 45.65% 26.90% Total 2.00% 2.26% 65.10% 34.04% 41.29% 24.66% Panels A and B, show means on the first row and med ians in parentheses on the second row. In Panels C and D, means are shown. Table 1 3: Correlations Payout Repurch Inst MktCap LifeCycle q Repurch 0.6528* Inst 0.0801* 0.0957* MktCap 0.0539* 0.0332* 0.0865* LifeCycle 0.0008 0.0004 0.0013 0.0009 q -0.0025 -0.0023 -0.0135* -0.0019 0.0013 CashFlow 0.0032 0.0024 0.0232* 0.0023 -0.0008 -0.4194* indicates two-tailed significance at 5%.

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33 1.4 The Effect of Institutional Owners on Total Pay outs 1.4.1 Do Institutional Owners Influence Total Payouts? According to the agency-based free cash flow theory, curr ent institutional owners positively influence future total payouts (di vidends and repurchases). Since I have no predictions on how payouts affect institu tional holdings, I only analyze the effect of institutional ownership on payou ts. Institutional investor ownership and payout levels are almost certainly endog enously related. Firms with higher payout levels tend to have higher institu tional ownership levels, so I need to combat the effect that this endogenous relatio nship has on my analysis. Therefore, I test the effect that changes in institutio nal ownership have on subsequent changes in payouts rather than looking at the ir levels. To test the effect that changes in institutional ownersh ip have on changes in payouts in the subsequent year, the following firm and year fixed effects model is estimated. (1-1) it it it i t it Control Inst Firm Year Payout 1 1 Payout it represents the firm’s payout to asset ratio. Year t represents year fixed effects and Firm i represents firm fixed effects. Inst i t-1 is the percentage of

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34 the firm’s shares owned by institutional investors. Control i t-1 represents a vector of time-varying firm level control variables ( q debt, stock turnover, retained earnings to total equity, log of market capitalizatio n, ROA, insider ownership, insider ownership squared, and log of revenue), and it is the error term. The independent variables are measured as the change from year t – 2 to year t – 1. The dependent payout variable is measured as the change from year t 1 to year t Table 1 4 reports on the effect that changes in insti tutional ownership have on total payout to assets ratios ( Payout ) in the subsequent year using firm and year fixed effects model (1-1). The first regressio n only uses the control variables as independent variables. The statistically sign ificant coefficients indicate that payouts increase as q decreases, debt decreases, market capitalization increases, and return on assets decreases. Payouts also increase for small decreases in insider ownership. The control var iable results remain largely consistent in the other regressions shown in the table. The second regression includes the variable ( Inst ) representing the change in the percentage of institutional ownership. Th e statistically significant coefficient shows that an increase in institutional owner ship leads to an increase in payout levels in the subsequent year. Statistical significance is important to my analysis, but practical (or economic) significance is as well. Therefore, I use an ex ample to give some perspective as to the magnitude of the effect of institu tional ownership on payouts. For this example, I use a hypothetical firm with an institutional

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35 ownership percentage (40%) and a payout to assets ratio (1.900%) quite close to the sample median for firms with payouts. It is importa nt to note for this analysis that institutional ownership percentage is measured from 0% to 100% (or 0 to 1). Using the coefficient in the second regression (0.0106), a rise from 40% to 50% institutional ownership should lead to an additional 0 .106% in the payout ratio, all else being equal. In this example, the firm’s payout r atio would subsequently increase from 1.900% to 2.006%. Institutional ownership percentages are higher in firm s with payouts than in firms without payouts. Therefore, the results discussed thus far could be influenced by the tendency of institutional investors t o invest more in firms that had a payout. To attenuate that influence, the third regression is ran only on firms that did not have a payout in year t – 2. Regression (3) shows that institutional owners have a significantly positive effect on future p ayouts in firms that did not have a payout in the previous year. The fourth regre ssion shows that an increase in institutional ownership leads to an increase in payo uts among firms that had a payout in the previous year as well. In this case though the t-statistic shows that the coefficient falls just a little short of the 10% sig nificance level (with a p-value of 0.103). Institutional investors use their influence to raise to tal payouts. Results for robustness checks using the Arellano and Bond (1991) differ ence Generalized Method of Moments (difference GMM) methodology to fu rther control for endogeneity and using data from the years 1990-1997 a nd 1998-2005 separately confirm this finding. Robustness tests are shown and discussed in Appendix A.

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36 1.4.2 Are Potential Agency Problems a Factor? According to agency-based theory, institutional investor s should not only encourage higher payouts, they should encourage higher payouts primarily in firms with poor investment opportunities. I test this pr ediction using q as a proxy for investment opportunities. I sort the sample of firm s each year into investment opportunity deciles. I assign each firm-year to one of t hree groups. Firms in the bottom three deciles (Low q ) have poor investment opportunities, those in the next four deciles (Medium q ) have moderate investment opportunities, and those in the highest three deciles (high q ) have good investment opportunities. I then run regressions using the firm and year fixed e ffects model (1-1) that show the effect that changes in institutional owne rship have on total payout to assets ratios ( Payout ) in the subsequent year. I add a new control variabl e, free cash flow ( CashFlow ), to the model because of its importance to the agency based theory. Regressions are run on the low q medium q and high q groups separately based on which group a firm is in during yea r t – 1. The results are shown in Table 1 5. The first and second regressions, which include only firms in the poor and moderate investment opportunities groups respectively, have a significantly positive coefficient for the variable Inst This indicates that an increase in institutional ownership leads to an increase in payouts for these groups. The third regression indicates that institutional owners do not hav e a significant effect on

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37 payouts in firms with good investment opportunities. Th is pattern is consistent with the agency-based theory. Agency-based theory also predicts that institutional in vestors should encourage higher payouts primarily in firms with high f ree cash flow. I test this prediction by assigning each firm-year to one of three groups: low cash flow (bottom three deciles), moderate cash flow (middle fou r deciles), and high cash flow (top three deciles). Once again, I use the firm a nd year fixed effects model (1-1) to access the impact institutional ownership has on payouts in the subsequent year. The results are shown in Table 1 6. The first regression shows that institutional owners have no effect on payouts in firms with low free cash flow. Higher payou ts are encouraged by institutional owners in the moderate cash flow firms. I n the group of firms with the highest cash flow, institutional investors have the strong est positive influence on total payouts. Consistent with agency-based theory, the pattern indicates that an increase in institutional ownership leads to a stronger increase in payouts as free cash flow increases. Institutional investors encourage higher payouts, espe cially in firms with poor investment opportunities or high free cash flow. Robustness checks using groups formed on the basis of a combination of firm in vestment opportunities and free cash flow and using difference GMM methodology supp ort this result. The robustness tests are reported and discussed in Appendix A. My results provide evidence that an increase in institut ional investors leads to a subsequent increase in total payout. Addition ally, the evidence

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38 demonstrates that institutional investors use their influ ence to encourage higher payouts primarily in firms that are the most prone to agency problems, those with poor investment opportunities and high free cash flow. The results support the agency-based theory prediction that institutional owner s encourage higher payouts to prevent management from misusing discretionar y funds.

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39 Table 1 4: Institutional Ownership and Payouts (1) (2) (3) (4) All Firms All Firms No Payout at year t 2 Payout at year t 2 Payout Payout Payout Payout Inst 0.0106*** 0.0072** 0.0182 (2.75) (2.19) (1.63) q -0.0005*** -0.0004*** -0.0002** -0.0063*** (3.21) (3.16) (2.03) (2.68) Debt -0.0114*** -0.0115*** -0.0031** -0.1961*** (3.08) (3.01) (2.21) (4.64) Turnover 0.0000 -0.0000 0.0000 -0.0043** (0.56) (0.33) (0.27) (2.13) LifeCycle -0.0000 -0.0000 -0.0000 -0.0000* (0.95) (1.05) (0.15) (1.82) MktCap 0.0081*** 0.0073*** 0.0031*** 0.0456*** (6.48) (6.58) (3.11) (6.16) ROA -0.0012** -0.0012** -0.0003 -0.0395*** (2.16) (2.25) (1.03) (3.10) Insider -0.0196* -0.0205* -0.0141 -0.0271 (1.88) (1.95) (1.63) (1.01) Insider2 0.0137 0.0146 0.0158* 0.0189 (1.29) (1.36) (1.72) (0.68) Revenue -0.0014 -0.0015 -0.0018 -0.0090 (1.19) (1.24) (1.32) (1.64) Observations 45,418 44,933 25,794 19,096 Firms 7,782 7,759 6,239 4,244 R-squared 0.06 0.16 0.34 0.17 Robust t statistics in parentheses significant at 10%; ** significant at 5%; *** sig nificant at 1% This table reports estimates of firm and year fix ed effect regressions of changes (from year t 1 to t ) in total payout divided by book value of assets ( Payout ). All independent variable values are calculated a s changes in that independent variable from year t 2 to t 1. Regressions (1) and (2) include all firms. Regressi on (3) includes only firms that had no payout in year t 2 and regression (4) includes only firms that had a payout in year t 2.

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40 Table 1 5: Institutional Ownership, Payouts and, Investment Opportunities Low q Medium q High q Payout Payout Payout Inst 0.0173* 0.0188*** 0.0080 (1.91) (2.97) (1.07) CashFlow -0.0016 -0.0059* 0.0051*** (1.15) (1.72) (3.28) q 0.0058** -0.0021*** -0.0004*** (2.15) (2.60) (2.76) Debt -0.0132 -0.0296*** -0.0033* (1.42) (2.61) (1.95) Turnover -0.0000 -0.0014*** -0.0009** (0.12) (2.86) (2.44) LifeCycle -0.0000 0.0000 -0.0000 (0.94) (0.81) (0.31) MktCap 0.0046* 0.0209*** 0.0073*** (1.74) (5.06) (4.07) ROA 0.0019 -0.0031 -0.0057*** (0.28) (0.55) (3.80) Insider -0.0119 -0.0435** 0.0047 (0.40) (2.12) (0.30) Insider2 0.0094 0.0416* -0.0154 (0.34) (1.95) (0.72) Revenue -0.0023 -0.0063 0.0003 (0.60) (1.24) (0.24) Observations 13004 18829 12403 Number of Firms 3971 5504 3793 R-squared 0.25 0.25 0.42 Robust t statistics in parentheses significant at 10%; ** significant at 5%; *** sig nificant at 1% This table reports estimates of firm and year fi xed effect regressions of changes (from year t 1 to t ) in total payout divided by book value of assets ( Payout ). All independent variable values are calculated as changes in that i ndependent variable from year t 2 to t 1. Sample firms used in regressions (1), (2), and (3) include only Low, Medium and High q firms, respectively. The Low, Medium and High q groups include the lowest three, middle four, and highest three q deciles from year t 1, respectively. Deciles are formed on a yearly b asis.

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41 Table 1 6: Institutional Ownership, Payouts, and Free Cash Flow (1) (2) (3) Low CashFlow Medium CashFlow High CashFlow Payout Payout Payout Inst 0.0003 0.0093* 0.0271** (0.04) (1.86) (2.14) CashFlow -0.0008 -0.0002 0.0005 (0.90) (0.10) (0.35) q -0.0003 -0.0008** -0.0025*** (1.50) (2.42) (3.24) Debt -0.0003 -0.0643*** -0.0788* (0.54) (4.54) (1.75) Turnover -0.0000 -0.0006** -0.0027 (0.19) (2.17) (1.63) LifeCycle -0.0000 -0.0000 0.0000 (0.08) (0.48) (1.00) MktCap 0.0033 0.0076*** 0.0260*** (1.60) (5.26) (4.76) ROA 0.0003 -0.0009 -0.0217*** (0.37) (0.17) (2.61) Insider -0.0075 -0.0129 -0.0229 (0.25) (0.80) (1.33) Insider2 0.0010 0.0089 0.0155 (0.03) (0.53) (0.80) Revenue -0.0010 -0.0038** -0.0061 (0.62) (1.98) (0.81) Observations 11014 18905 14317 Number of Firms 4530 5591 4457 R-squared 0.47 0.43 0.25 Robust t statistics in parentheses significant at 10%; ** significant at 5%; *** sig nificant at 1% This table reports estimates of firm and year fixed effect regressions of changes (from year t 1 to t ) in total payout divided by book value of assets ( Payout ). All independent variable values are calculated as changes in that independent varia ble from year t 2 to t 1. Sample firms used in regressions (1), (2), and ( 3) include only Low, Medium and High CashFlow firms, respectively. The Low, Medium and High CashFlow groups include the lowest three, middle four, and highest three CashFlow deciles from year t 1, respectively. Deciles are formed on a yearly b asis.

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42 1.5 The Effect of Institutional Owners on Stock Rep urchases 1.5.1 Do Institutional Owners Influence Stock Repurcha ses? The adverse selection model predicts that an increase in current institutional ownership will lead to an increase in fu ture repurchases. Institutional ownership levels and stock repurchase levels have an endog enous relationship. Therefore, I test the effect that changes in institutio nal ownership have on subsequent changes in repurchases. To test the influence that institutional owners have on future repurchases, the following firm and year fixed effects model is esti mated. (1-2) it it it i t it Control Inst Firm Year Rpurch 1 1 This model is identical to model (1-1) except for the dependent variable. In this model, Rpurch it represents the change in the repurchase to asset ratio fo r each firm in each year. As in model (1-1), the indepen dent variables are measured as the change from year t – 2 to year t – 1. The dependent repurchase variable is measured as the change from year t 1 to year t Table 1 7 reports on the effect that changes in insti tutional ownership have on stock repurchases ( Repurch ) in the subsequent year. The first regression only uses the control variables as independent variables. The

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43 statistically significant coefficients indicate that repurcha ses increase as q decreases, debt decreases, retained earnings to total equ ity decreases, market capitalization increases, and return on assets decreases. T he results for the control variables remain largely consistent throughout t he rest of the regressions shown in the table. In the second regression, I add a variable ( Inst ) representing the change in total institutional ownership. There is a positive a nd significant relationship between institutional ownership and subsequent stock repu rchases. Institutional owners prefer to own firms that repurcha se stock. Therefore, the results in the second regression could be influenced by the tendency of institutional investors to invest more in firms that ha ve repurchased stock previously. To alleviate that influence, the third r egression is ran only on firms that did not have a repurchase in year t – 2. The third regression demonstrates that institutional owners have a significantly positive influence on future stock repurchases in firms that did not repurchase stock in th e previous year. The fourth regression shows that institutional owners encoura ge higher repurchases in firms that had repurchases in the previous year. Institutional investors encourage firm management to increase stock repurchases. Results for robustness checks, which are shown in Appendix A, using data from the years 1990-1997 and 1998-2005 sepa rately confirm this result.

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44 1.5.2 Is Information Asymmetry a Factor? Adverse selection theory predicts that institutional inv estors will use their influence to persuade management to increase repurchases. Additionally, the theory predicts that institutional investors will find repurchases more attractive as information asymmetry increases. I test this prediction using retained earnings to total equity ( LifeCycle ) as a proxy for information asymmetry. DeAngelo, DeAngelo and Stulz (2006) use this measure as a proxy f or firm life-cycle. They assert that this is a valid proxy for firm information asymmetry. This relationship between firm life-cycle and information asymmetry seems logical because the further along a firm is in its life-cycle the more info rmation an investor will have about the firm to judge its prospects, all else being e qual. I sort the sample of firms each year into informatio n asymmetry deciles. I assign each firm-year to one of three groups. Firms in the bottom three deciles (Early LifeCycle ) have high information asymmetry, those in the next four deciles (Middle LifeCycle ) have moderate information asymmetry, and those in t he highest three deciles (Late LifeCycle ) have low information asymmetry. I then run regressions using the firm and year fixed effects model (1-2) that show the effect that changes in institutional owner ship have on repurchases in the subsequent year. Regressions are run on the high, moderate, and low information asymmetry groups separately based on which g roup a firm is in during year t – 1. The results are shown in Table 1 8.

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45 The first two regressions show that institutional invest ors encourage increased repurchases in firms with high and moderate i nformation asymmetry. The third regression shows a statistically weak positive rel ationship between institutional ownership and future stock repurchases in lo w information asymmetry firms. It is notable that the Inst coefficient for the low information asymmetry firms group is higher than for the other tw o groups despite not being statistically significant. This may be explained by the h igher propensity of firms with low information asymmetry to make repurchases. This higher propensity is shown in the average repurchase to asset ratios for the three groups (not shown): high information asymmetry (0.36%), moderate information asymmetry (0.64%), and low information asymmetry (1.55%). The results shown in this table provide evidence that supports the adverse selection theo ry. A robustness test using the difference GMM methodology confirms this resu lt. It is shown in Appendix A. Institutional investors use their influence to persuade management to increase repurchases. This relationship is more significan t in firms with higher information asymmetry. This evidence provides support fo r the adverse selection theory which predicts that institutional owners encourage higher stock repurchases to gain an advantage over other less inform ed investors.

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46 Table 1 7: Institutional Ownership and Stock Repu rchases (1) (2) (3) (4) All Firms All Firms No Repurchase at year t 2 Repurchase at year t 2 Repurch Repurch Repurch Repurch Inst 0.0104*** 0.0098*** 0.0220* (3.15) (3.16) (1.66) q -0.0004*** -0.0003*** -0.0002** -0.0108*** (3.03) (2.96) (2.20) (4.69) Debt -0.0099*** -0.0099*** -0.0057** -0.2002*** (2.99) (2.92) (2.21) (5.31) Turnover 0.0000 -0.0000 0.0000 -0.0046*** (0.68) (0.43) (0.49) (2.97) LifeCycle -0.0000** -0.0000*** -0.0000 -0.0000** (2.51) (2.75) (0.47) (1.97) MktCap 0.0066*** 0.0058*** 0.0035*** 0.0527*** (6.41) (6.83) (4.24) (7.56) ROA -0.0011** -0.0011** -0.0005 -0.0647*** (2.27) (2.34) (1.54) (4.97) Insider -0.0110 -0.0119 -0.0045 -0.0557 (1.51) (1.62) (0.86) (1.47) Insider2 0.0085 0.0094 0.0076 0.0404 (1.12) (1.23) (1.37) (1.02) Revenue -0.0010 -0.0011 -0.0020 -0.0044 (0.93) (1.02) (1.64) (0.72) Observations 45611 45126 34083 11043 Firms 7801 7778 7525 3588 R-squared 0.05 0.19 0.33 0.25 Robust t statistics in parentheses significant at 10%; ** significant at 5%; *** sig nificant at 1% This table reports estimates of firm and year fix ed effect regressions of changes (from year t 1 to t ) in repurchases divided by book value of assets ( Repurch ). All independent variable values are calculated a s changes in that independent variable from year t 2 to t 1. Regressions (1) and (2) include all firms. Regression (3) inclu des only firms that had no payout in year t 2 and regression (4) includes only firms that ha d a payout in year t 2.

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47 Table 1 8: Institutional Ownership, Repurchases, and Firm Life-cycle (1) (2) (3) Early LifeCycle Middle LifeCycle Late LifeCycle Repurch Repurch Repurch Inst 0.0131** 0.0058* 0.0216 (2.01) (1.94) (1.48) q -0.0004 -0.0009*** -0.0003* (1.23) (4.85) (1.79) Debt -0.0025 -0.0298*** -0.0071* (0.63) (6.01) (1.96) Turnover -0.0001 -0.0006*** -0.0039* (0.42) (3.03) (1.67) LifeCycle -0.0000*** 0.0001 -0.0000 (2.68) (0.63) (0.77) MktCap 0.0020 0.0077*** 0.0173*** (1.48) (6.19) (5.02) ROA 0.0005 0.0026 -0.0014** (0.25) (0.45) (2.13) Insider 0.0093 -0.0017 -0.0457* (0.96) (0.23) (1.83) Insider2 -0.0117 0.0016 0.0346 (0.83) (0.19) (1.45) Revenue -0.0015 -0.0018 -0.0005 (0.89) (0.60) (0.29) Observations 11505 18633 14988 Number of Firms 3639 4730 3093 R-squared 0.17 0.28 0.32 Robust t statistics in parentheses significant at 10%; ** significant at 5%; *** sig nificant at 1% This table reports estimates of firm and year fix ed effect regressions of changes (from year t 1 to t ) in repurchases divided by book value of assets ( Repurch ). All independent variable values are calculated as changes in that i ndependent variable from year t 2 to t 1. Sample firms used in regressions (1), (2), and (3) include only Early, Middle and La te LifeCycle firms, respectively. The Early, Middle and Late LifeCycle groups include the Earliest three, Middle four, and Latest three LifeCycle deciles from year t 1, respectively. Deciles are formed on a yearly basis.

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48 1.6 The Effect of Institutional Owners on Payout Co mposition Grullon and Michaely (2002) find that repurchases are g radually being substituted for dividends. The substitution hypothesis su ggests that institutional shareholders are encouraging the trend towards increased repurchases in lieu of dividends. This hypothesis predicts that an increase in in stitutional ownership will lead to an increase in repurchases as a percentage of tota l payout. To test this prediction, I use a measure of payout com position that evaluates the contribution to total payout made by di vidends and stock repurchases equally. The measure which is calculated for each firm for each year is represented by PayComp and is shown in equation (1-3). (1-3) Div Rpurch Div Rpurch PayComp In equation (1-3), Rpurch is the dollar value of stock repurchases and Div is the dollar value of common stock dividends. PayComp is undefined for firms with no payouts. It is equal to zero for firm-years wi th an equal dollar value of repurchases and dividends (this only occurs six times in my sample). If the majority of a firm’s payout in a given year is made through dividends, PayComp will be a negative number. If the majority of the payout is made through repurchases, PayComp will be a positive number. If the entire payout is

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49 made using dividends, PayComp will have a value of negative one. If stock repurchases are the only means of payout, PayComp will have a value of positive one. In my sample, the median and mean for PayComp are -0.51 and 0.16 respectively. This indicates firms are more likely to use dividends over repurchases as their primary method of payout for the full sample period. I use the following firm and year fixed effects model to estimate the influence that institutional owners have on a firm’s ch oice between the use of repurchases or dividends in determining their payout co mposition. (1-4) it it it i t it Control Inst Firm Year PayComp 1 1 This model is identical to models (1-1) and (1-2) excep t for the dependent variable. In this model, PayComp it represents the change in the payout composition measure for each firm in each year. As in the earlier models, the independent variables are measured as the change from year t – 2 to year t – 1. The dependent payout variable is measured as the change from year t 1 to year t Table 1 9 reports on the influence that institution al ownership changes have on payout composition ( PayComp ) in the following year. The first regression uses only control variables as independent var iables. The statistically significant coefficients indicate that payout composition tilts toward dividends as q debt, retained earnings to total equity (a proxy fo r firm maturity), and return on

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50 assets increase. Payouts tilt toward stock repurchases as marke t capitalization or revenue increases. In the second regression, I add a variable ( Inst ) for institutional ownership. The result indicates a significantly positive relationship between institutional owners and an ensuing tendency to use repurchases as a gre ater part of the total payout composition. This tendency holds regardless o f whether the majority of the firm’s payout was dividends or repurchases in the previous year. The third regression shows that institutional owners encourage an in crease in repurchases as part of total payout composition in firms that favor ed dividends as a means of payout in the previous year. The fourth regression pro vides evidence that institutional owners also encourage repurchases over div idends in firms that favored repurchases as a means of payout in the previo us year. The substitution hypothesis is supported because institutio nal investors prefer repurchases over dividends and they use their inf luence to tilt payout composition towards repurchases. For robustness, I test thi s assertion using difference GMM and separately for the years 1990 – 1 997 and 1998 – 2005. The results which are shown and discussed in Appendix A support the substitution hypothesis and indicate that institutional investors enco uraged an increase in repurchases as a part of total payout more intensely du ring the latter period.

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51 Table 1 9: Institutional Ownership and Payout Com position (1) (2) (3) (4) All Firms All Firms Dividends > Repurchases at year t 2 Dividends Repurchases at year t 2 PayComp PayComp PayComp PayComp Inst 0.2701*** 0.2244*** 0.3739*** (4.17) (2.69) (2.59) q -0.0184** -0.0175** -0.0196** -0.0170 (2.29) (2.27) (2.06) (0.90) Debt -0.9427*** -0.9430*** -1.0601*** -0.6407*** (10.17) (10.05) (9.18) (2.91) Turnover 0.0090 0.0071 -0.0426** 0.0266* (0.71) (0.52) (2.47) (1.85) LifeCycle -0.0000*** -0.0000*** 0.0027 -0.0001*** (5.62) (5.48) (0.47) (26.31) MktCap 0.2515*** 0.2447*** 0.2601*** 0.2935*** (8.81) (8.67) (7.41) (3.90) ROA -0.2041* -0.2163** 0.1117 -0.7461*** (1.89) (2.01) (0.72) (3.13) Insider -0.1804 -0.1730 -0.1760 -0.1015 (1.35) (1.28) (1.01) (0.35) Insider2 0.0351 0.0355 0.1468 -0.2353 (0.20) (0.20) (0.69) (0.63) Revenue 0.0731** 0.0685** 0.0324 0.1428** (2.17) (2.05) (0.73) (2.40) Observations 16095 15933 9969 4282 Number of Firms 3245 3217 1849 1690 R-squared 0.13 0.13 0.16 0.34 Robust t statistics in parentheses significant at 10%; ** significant at 5%; *** sig nificant at 1% This t able reports estimates of firm and year fixed effec t regressions of changes (from year t 1 to t ) in a measure of payout composition ( PayComp ). PayComp is equal to 1 if payout is composed entirely of dividends and 1 if payout is composed entirely of s toc k repurchases. All independent variable values are calculated as chang es in that independent variable from year t 2 to t 1. Regressions (1) and (2) include all firms. Regression (3) includes only firms in which dividen ds exceeded repurchases in year t 2 and regression (4) includes only firms in which repurchases exceeded dividends in year t 2.

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52 1.7 Conclusion Institutions own almost 70% of U.S. public corporations. They have an informational advantage over other investors and have the capability to be better monitors of corporate management than individual inve stors. I test several theories about the relationship between institutional investors and payout policy in this paper. The agency-based free cash flow theory (Jensen (1986)) su ggests that firms with higher free cash flow and poor growth oppo rtunities should have higher payouts (through higher dividends or stock repurchases). I find that higher institutional ownership leads to increases in total payo uts, especially in firms with high free cash flow and poor investment opportunities. The adverse selection theory of Barclay and Smith (198 8) and Brennan and Thakor (1990) predicts that institutional investor s will encourage repurchases, especially in firms with high information asymmetry. This prediction holds as higher institutional ownership causes firms to in crease repurchases and this relationship is stronger in firms with higher info rmation asymmetry. I find no evidence that institutional investors encourage dividen d increases. Grullon and Michaely (2002) argue that firms have be en increasingly using funds that would have previously been used for dividen ds to make repurchases. My evidence that an increase in institutional ownership leads to an increase in the proportion of total payout going towards repurcha ses and consequently a

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53 decrease in the proportion of payout going towards di vidends provides support for their argument. Institutional investors have a large and growing posit ion as owners of public corporations. My results provide evidence that in stitutional investors are engaged in corporate governance and corporate payout policy.

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54 2 Institutional Investors and R&D Investment 2.1 Introduction In many firms, one of the most important financial de cisions made by management executives is deciding how much the firm shou ld invest in research and development (R&D). Generally, R&D investment is b eneficial to shareholders. For example, Chan, Lakonishok, and Sougian nis (2001) provide evidence that the level and changes of R&D investment a re positively associated with future returns. Still, the benefits of R&D invest ment may not be experienced by management or shareholders until after an extended period of time. Eberhart, Maxwell, and Siddique (2004) find that investors under react to the benefits of R&D increases. They find evidence that firms experienc e abnormally positive stock returns for the 5-year period following an R&D in crease. R&D investment is more likely to offer little or no return than compara ble investments. Kothari, Laguerre, and Leone (2002) demonstrate that the futu re benefits from R&D are far more uncertain than benefits from many other uses o f funds such as investments in property, plant, and equipment. The delayed and risky benefits of R&D can cause agency problems. Agency problems arise when managers act in their own int erests at the expense of shareholders’ interests. Underinvestment in R&D may be advantageous to

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55 management but not shareholders. Underinvestment may i ncrease short-term earnings at the expense of long-term value because R&D investment is expensed immediately, but the payoff from R&D is rare ly realized in the same accounting period as when the investment is made. There fore, short-term earnings move inversely to R&D investment. Porter (1992) argues that because U.S. institutional m anagers are measured on their short-term performance that they fo cus on short-term returns in their investments. This drives them to focus on near-t erm indicators that provide limited information like current earnings whe n valuing investments. Management reacts to this pressure by decreasing investmen t in R&D. He contends that in comparison to European and Japanese comp anies which tend to have long-term shareholders with larger stakes, U.S. firms invest less in R&D and their investment projects are of shorter duration. He notes that both U.S. and foreign CEOs believe that U.S. companies have shorter investment horizons than their international competitors. There is evidence that managers sometimes intentionally invest at less than the optimal level. Graham, Harvey, and Rajgopa l (2005) interview executives and find out that approximately 80% of the m would reduce R&D to meet an earnings target. Bhojraj and Libby (2005) con duct an experiment in which 89 experienced financial managers choose between p rojects where a conflict exists between near-term earnings and total cash flow. In the experiment, managers favor projects that will maximize short-term e arnings over projects which will maximize total cash flows when increased capit al market pressure

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56 resulting from a pending stock issuance is present. Manageme nt reduces R&D investment when the investment is likely to jeopardize reporting positive or increasing income in the near term (Baber, Fairfield a nd Haggard (1991)). Harter and Harikumar (2004) find evidence that managers with greater earnings-based compensation tend to invest in projects with more immed iate payoffs. Bhojraj et al. (2009) argue that dedicated earnings guiders engag e in myopic R&D to beat analysts’ forecasts. Additionally, they find that manag ers know they are underinvesting as evidenced by increased insider selling f ollowing underinvestment in R&D. Institutional investors may help mitigate the potenti al problem of underinvestment in R&D or they may exacerbate it. Inst itutional investors pool large sums of money which they then invest in various in vestments including equity. Common institutional investors include banks, in surance companies, mutual funds, investment advisors, pension funds, hedge f unds and university endowments. Institutions own nearly 70% of the shares of U.S. corporations. 3 The empirical evidence on the relationship between i nstitutional investors and R&D is mixed. Bange and De Bondt (1998) find in a study of 100 firms with large R&D budgets that management is less likely to mana ge earnings by cutting R&D if institutional ownership is higher. Conversely, i n a study of 557 manufacturing firms from 1985 – 1990, Samuel (2000) finds that institutional ownership has a negative effect on R&D expenditures. Still, institutional investors are generally believed to be more effective monitors of firm management than other investors. One reason for this is that the 3 Bogle, John C. (2010) Restoring Faith in Financial Markets, Wall Street Journal (January 19).

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57 relative cost of monitoring and influencing management is higher for noninstitutional shareholders than for institutions because co sts are spread across fewer shares (Parrino, Sias and Starks (2003), Almazan, Hartzell, and Starks (2005) and Maug (1998)). I examine the effect that institutions have on R&D. First, I determine if institutional investors use their influence to persuade m anagement to invest more in R&D. I then analyze the effect that firm stock liqui dity, information asymmetry, free cash flow, and investment opportunities have on t he relationship between institutional investors and R&D investment. Edmans (2009) creates a model which predicts that investor s that hold a large proportion of a firm’s shares (blockholders) can enco urage managers to invest for long-run growth at the expense of current e arnings as long as a firm has sufficient stock liquidity. In the Edmans’ model, blo ckholders can encourage investment by influencing current stock prices to capture its long-term effect. The blockholders ability to exert this encouragement is positi vely related to the liquidity of the firm’s common stock. An interesting aspe ct of this model which is pertinent to my paper is that it demonstrates that inv estors can add value to a firm even if they do not directly intervene in a fir m’s management. Edmans’ model could apply to institutional investors if institutional investors act in unison to affect investment policy. These actions do not even have to be coordinated as long as institutional investor s tend to behave similarly. There is evidence that institutional investors sometimes a ct in near unison or “herd”. Sias (2004) provides evidence that institutions garner information from

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58 each others’ trades which results in herding. Institution al investors herd into and out of the same industries (Choi and Sias (2009)). Mut ual fund investors have been found to herd into small stocks (Wermers (1999)). In stitutional investors herd on dividend signals (Rubin and Smith (2009)) and into stocks with positive return momentum (Nofsinger and Sias (1999)). Edmans conducts no empirical investigation of his theory in his paper. He does note that others have conducted empirical studies in which the findings are congruent with his theory. For example, Lee and O’Nei ll (2003) find that ownership concentration as proxied by blockholders that ow n more than 3% of a firm is positively related to R&D investment. Cronqvist and Fahlenbrach (2009) provide evidence that an increase in certain categories of blockholders leads to higher investment in R&D. These studies do not investiga te the effect that liquidity has on the relationship between blockholders and R&D. My research is novel in that I look specifically at the effect that l iquidity has on the relationship between institutional investors and subsequent R&D inve stment. Edmans’ theory leads to the hypothesis that institutio nal investors will encourage R&D investment more in firms with greater sto ck liquidity. In this theory, the managers of firms with high institutional ownership invest more in R&D to raise firm value because they realize that insti tutional owners will be able to discern the true value of the investments. High firm stock liquidity is an important aspect of the theory because it allows institu tional investors to divest their shares if they discern that managers are not invest ing properly and thus

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59 offers the institutional investors an incentive to gathe r costly information required to monitor the firm effectively. Bhide (1993) offers a countervailing argument to Edma ns’ theory about the relationship between blockholders and investment wh ich I also expand to include institutional investors. Bhide does not provide extensive empirical evidence for his theory, instead relying on logic and sp ecific examples. Bhide notes that U.S. regulations provide a wedge between i nvestors and management, thus encouraging institutions to prefer disp ersed arm’s-length holdings over long-term concentrated holdings. The regu lations that encourage institutions to disperse their holdings among firms that they have an arm’s-length relationship include: pension and mutual funds are requ ired to have diversified holdings, ERISA discourages pension managers from sitting on boards by holding them to a higher standard than other director s, insider-trading rules place special restrictions on investors that hold more than 10% of a company’s stock or serve on its board. These regulations increase the costs and reduce the benef its to institutional investors of monitoring management. Bhid e asserts that higher liquidity allows large investors to divest their shares r ather than expend resources to acquire the information necessary to monito r a firm effectively. He argues that since the large investors cannot sell their sh ares in firms with lower liquidity without accepting a significant discount, they prefer to expend effort to encourage management to invest more to enhance the val ue of their long-term

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60 investment. Bhide’s argument provides the basis for my h ypothesis that institutional investors will encourage R&D more in firm s with low liquidity. The essential difference between the Edmans (2009) the ory and the argument in Bhide (1993) can be summarized as follows. Bhide asserts that blockholders increase firm value by monitoring, and sinc e monitoring and divestment are mutually exclusive, liquidity decreases th e propensity of blockholders to monitor. Edmans argues that blockholder lo yalty to a firm that makes sound investment decisions at the expense of weak ea rnings allows the blockholder to increase firm value. Loyalty and divestm ent are again mutually exclusive, but the loyalty in the face of weaker earnin gs provides a particularly strong indicator of value which is strengthened if the b lockholder could have easily sold their shares. Paradoxically, the power of bl ockholder loyalty to add value depends on the ease of divestiture. If institutional investors affect R&D investment more in firms with high stock liquidity, a logical inference is that they influen ce management through the threat of divestment. On the other hand, if they aff ect R&D investment more in firms with low liquidity, then they are more likely to influence management using tactics such as proxy votes and shareholder proposals. In addition to the effect that liquidity has on the r elationship between institutional investors and R&D investment, I also exami ne the effect that information asymmetry has on the relationship. Firms i n which investors know relatively more about the firm’s future prospects are co nsidered low information asymmetry firms. The importance of information asymmet ry in R&D budgets to

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61 investors is demonstrated by Aboody and Lev (2000) who find that R&D is a major contributor to insider gains and information asym metry between insiders and investors. Information asymmetry is likely to be important to the relationship between institutional investors and R&D. Institutional investors have an informational advantage over other shareholders which varies with fir m characteristics and information asymmetry (Bennet, Sias, and Starks (2003) ). Institutions have an informational advantage in newly public firms (Field and Lowry (2009)) and seasoned equity offerings (Chemmanura, He, and Hu (200 9)) which is largely the result of better analysis of publicly available informa tion. If institutional investors encourage R&D investment more in low information asymm etry firms, it indicates that they are not more effective than other investors at monitoring firms which are difficult to monitor. Conversely, if institutional in vestors have a more positive effect on R&D investment in high information asymmetry firms, it shows that institutional investors are effective monitors of firms that are difficult for other investors to monitor. Finally, I test a hypothesis that institutional invest ors will encourage R&D more in firms that are less prone to overinvestment pro blems. This theory is derived from the work of Jensen (1986) which asserts that managers that put their interests above shareholders’ interests will be mor e prone to overinvest if their firm has high free cash flow and poor investment opportunities. Jensen uses empirical evidence involving debt and acquisitions to supp ort his theory. According to my hypothesis, institutional investors should e ncourage R&D

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62 investment primarily in firms that have good investme nt opportunities and not simply because a firm has high free cash flow (available funds). If institutional investors don’t take investment opportunities into accou nt when using their influence to convince management to increase R&D, then the relationship between institutional shareholders and R&D does not pr ovide evidence of superior monitoring ability. My results indicate that institutional investors encourage higher R&D investment in general. I also find that institutional investors positively influence R&D investment primarily in firms with high liquidity This provides empirical support for my hypothesis based on the Edmans (2009) m odel which predicts that shareholders that lack control rights can help contr ol managerial investment myopia in firms with high liquidity by gathering inf ormation about the fundamental value of investment policies and impounding them into stock prices. I also provide evidence that institutional investors i nduce R&D investment more effectively in firms with high information asymm etry. Finally, I find that institutional investors encourage R&D investment more as investment opportunities rise, but not as free cash flow rises. Jensen (1986) provides evidence that managers tend to overinvest if they have free cash flow even if they do not have adequate investment opportunities. H e finds that debt helps to control this tendency. My results provide evidence that institutional investors help control managerial overinvestment by only encouraging h igher R&D investment in firms that have adequate investment opportunities.

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63 I conclude that, holding other factors constant, higher i nstitutional investor ownership leads to higher R&D investment. I also find t hat this relationship strengthens as firm stock liquidity increases, information a symmetry increases and investment opportunities increase. 2.2 Literature Review and Hypotheses 2.2.1 Literature Review Investment in R&D is essential to many firms and an impo rtant factor for success in many others. Generally, markets view increased R&D investment as a signal that a firm has good long-term opportunities. For example, Sundaram, John, and John (1996) find that the percentage change i n R&D expenditures of firms announcing R&D increases has a positive impact on the stock price of the announcing firm. Yet, the stock market’s valuation of th e intangible capital created by R&D varies widely by time period (Hall (19 93)). Also, market reaction to R&D increase announcements is not uniform for all fir ms. Chan, Martin, and Kensinger (1990) find that markets react positively whe n high-technology firms announce increases and negatively when low-technology firms announce increases. This indicates that generally investors believe t hat high-technology firms do not invest enough in R&D and that low-techno logy firms invest too much. Despite the generally favorable view of R&D inv estment, economists have

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64 developed theories that explain how firm management might be motivated to underinvest in R&D. Management can be susceptible to pressure to underinvest b ecause of concerns about avoiding hostile takeovers and the time h orizons of influential investors (Froot, Perold and Stein (1992) and Stein ( 1988)). Also, managers may underinvest because their concentration of wealth in a single firm makes them more risk averse (Stein (1988)) or to mislead the market about their firms’ worth by managing earnings (Stein (1989)). Tying managemen t salary and bonuses to earnings can also create incentives to underinvest (Bange and De Bondt (1998)). On the other hand, managers may have an incentive to invest too much because increased corporate investment leads to increased firm size which often leads to increased power and compensation for managers (Jensen (1 986)). Empirical studies have found evidence that managers some times underinvest (engage in managerial myopia) to improve short-term results at the expense of long-term firm value. Cheng, Subramanyam, and Zhang (2007) find that firms that frequently issue earnings guidance invest significantly less in R&D to meet or beat analysts’ earnings forecasts. Holden an d Lundstrum (2009) report that managers increase R&D and their firms beco me less likely to beat analysts’ earnings forecasts after the introduction of lon g-term stock options (LEAPS) for their firm. They argue that the decline in the use of sub-optimal R&D investment to manage earnings is caused by the new-found ability of informed traders to profit from their long-term superior infor mation through the use of LEAPS.

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65 If management’s income is tied to short-term earnings or if a manager will not be around by the time a long-term investment beg ins to pay off, management may have an incentive to manage earnings through unde rinvestment. CEO turnover rates are increasing and are becoming more str ongly related to firm stock performance (Jensen, Murphy, and Wruck (2005) and Ka plan and Minton (2006)). This trend may be increasing the likelihood th at managers’ will invest myopically. It has been demonstrated that late careerstage CEOs manage earnings while early career stage CEOs do not (Demers and Wang (2010)) and CEOs spend less on R&D near the end of their careers (Dech ow and Sloan (1991)). Peng and Roell (2008) find that option-base d pay increases the probability of securities class action litigation and earn ings manipulation. This result suggests that option-based compensation gives execut ives an incentive to focus on short-term stock prices. Although there is substantial evidence that management of some firms systematically underinvest, there is also considerable evi dence that managers do not methodically underinvest. Cazier (2009) follows CE Os throughout time and finds no evidence that they reduce spending on R&D as th ey near retirement, although he does find that older CEOs spend less on R&D in general. On the other hand, in a study on data from 1970 to 1989, Gi bbons and Murphy (1992) find that R&D spending tends to be the largest during a CEO’s final years in office. They offer three explanations for this somewhat puzzling result: CEOs may believe R&D offers a more immediate payoff than other projects, R&D is formulated by a group of executives that are likely to have different retirement

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66 dates than the CEO, and the potential impact on the CEO’s wealth from R&D is too small to justify manipulative behavior. Their resu lts could also be explained by the finding in Cheng (2004) that board compensat ion committees recognize the potential for CEOs nearing retirement to underin vest in R&D and mitigate underinvestment through compensation incentives. The use of long-term compensation incentives is increasi ng. Stock-based pay for executives rose in both new and old economy fir ms from 1992 2001 (Murphy (2003)). Stock-based pay may alleviate underi nvestment problems. Coles, Daniel, and Naveen (2006) find that higher sen sitivity of CEO wealth to stock volatility results in riskier policy choices, including rel atively more investment in R&D. Kang, Kumar, and Lee (2006) also p rovide evidence that long-term investment is positively related to equity-b ased incentive compensation. R&D investment can also be impacted by the monitoring i nfluence of institutional shareholders. Many studies have found ev idence that institutional investors influence R&D investment. Yet, these studies ha ve offered mixed results as to the effectiveness of institutional investors a s monitors of R&D investment. For example, Aghion, Van Reenen, and Zingales (2009) argue that institutional investors have a positive impact on R&D an d its productivity by reducing the career risk faced by CEOs who invest in risky R&D projects. They find that CEOs are less likely to be fired after profi t downturns resulting from such projects if institutional ownership is higher. Huang and Shiu (2009) assert that

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67 foreign institutional investor ownership in Taiwanese f irms is positively associated with R&D investment and subsequent firm perfor mance indicating that the institutional investors have an informationa l advantage over domestic investors. Szewczyk, Tsetsekos, and Zantout (1996) find tha t abnormal returns from announced increases in R&D expenditures were positive ly related to institutional ownership indicating that the market view s institutional owners as effective monitors of R&D. David, Hitt, and Gimeno (2 001) observe that institutional ownership does not lead to increased R&D u nless the institutional owners attempt to influence R&D by resorting to activi st actions such as public announcements, shareholder proposals, proxy contests or di rect negotiations with managers. Bushee (1998) finds that greater instit utional ownership decreases the likelihood that R&D will be cut following a poor earnings performance. Wahal and McConnell (2000) find no supp ort for the assertion that that high transient institutional ownership leads to l ower R&D. In a study of technology and healthcare firms, Le, Walters, and Krol l (2006) find that transient and long-term institutional investors actively monitor and influence R&D spending. On the other hand, there is evidence that institutiona l investors are not effective monitors of R&D investment. Chung, Wright, a nd Kedia (2003) find that institutional holdings had no effect on the market val uation of R&D investments. Jones and Danbolt (2003) argue that U.K. institutional investors react to shortterm performance measurement pressures by taking a myopi c view of R&D expenditures. They find that firms with higher instit utional ownership have a less

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68 positive stock price response to R&D increase announcements. You, Chen, and Holder (2008) demonstrate that institutional investors have no effect on R&D levels in American pharmaceutical firms. They also find t hat institutional ownership leads to less efficient R&D in American and Kor ean pharmaceutical firms indicating support for a myopic influence on mana gement by institutional investors. Using a sample which only includes firms that ex perience an earnings decline, Bushee (1998) argues that managers reduce R& D to boost earnings in firms with high levels of transient institutional own ership It may be difficult for even large investors to monito r R&D. Zeckhauser and Pound (1990) hypothesize that firms in industries with high R&D investment to sales levels have higher information asymmetry and a re thus more difficult to monitor. Using blockholders’ effect on earnings growth as a measure of monitoring ability, they found that investors that own over 15% of a company are effective monitors in firms that are in industries with low R&D levels (and thus low information asymmetry) but not in industries with high R&D levels. 2.2.2 Hypotheses Shleifer and Vishny (1986) and others have theorized that large investors are important monitors of firm management. Institutional investors can influence management through proxy votes, shareholder proposals, p ublicity generation or the threat of “voting with their feet” thus depressing stock share price as the shares are sold. The influence of institutions is demonst rated by the fact that their

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69 shareholder proposals get more votes and a more positiv e stock price reaction (Gillan and Starks (2000)). Ryan and Wiggins (2002) fi nd that institutional owners influence R&D directly by monitoring management and i ndirectly by influencing compensation policy. The influence institutional invest ors can wield is reflected in the view of CFOs that institutional investors are the m ost important marginal investors (Graham, Harvey, and Rajgopal (2005)). This influence gains empirical evidence from the finding of Gillan and Starks (2007) that institutional investors can influence management through the threat of divest ing their shares. CFO interviews point out other reasons that institutional i nvestors are important: they can lower stock price by herding out of a stock after an e arnings miss or they can provide easier access to capital leading to a lower futur e cost of capital if they are pleased with firm management (Graham, Harvey, and Ra jgopal (2005)). If institutional investors herd, then they may have an influence that is similar to that of large shareholders. Evidence has bee n found that institutions or large shareholders influence corporate governance. Che n, Harford and Li (2007) find that monitoring by institutions with concentrated long-term holdings improves the performance of firms involved in mergers. Cronqvi st and Fahlenbrach (2009) find that large shareholders influence corporate invest ment, financial and executive compensation policies. Wahal and McConnell (2000) and Lee and O’Neill (2003 ) find a positive relationship between institutional investors and R&D in vestment. They do not establish that institutional investors use their influen ce to persuade management to increase R&D investment. Given that the market gene rally rewards increased

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70 R&D investment and that institutional investors have be en shown to effectively monitor management, I hypothesize that the influence of institutional investors will lead to higher R&D investment. H1 : Institutional investors will encourage higher R&D inv estment, after controlling for firm characteristics. In a model proposed by Edmans (2009), investors with la rge holdings in a firm have strong incentives to monitor the firm. They use private information that they gather as a result of these incentives to make trad ing decisions. Therefore, they make trading decisions based on the fundamental va lue of the firm rather than current earnings. This encourages management to inv est for the long-term rather than for short-term profits. Management can thu s avoid a depressed stock share price that results from large investors divesting th eir shares. In Edmans’ model, the ability of blockholders to influence manage ment is enhanced by high firm stock liquidity. Although Edmans’ model is built upon the actions of blockholders, his model demonstrates how shareholders can i nfluence management even if they do not have control rights. T herefore, institutional investors that demonstrate herding behavior because of si milar motives can effectively act as blockholders. Therefore, I test an e xtension of the Edmans’ model by investigating if the ability of institutiona l investors to encourage higher R&D investment is enhanced by high stock liquidity.

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71 According to Bhide (1993), greater liquidity allows la rge investors to divest their shares rather than expend costly resources to monito r management and encourage investment. An implication I have derived fr om this assertion is that high stock liquidity increases the incentives for institutio nal investors to divest their shares rather than expend costly effort encouragin g higher R&D investment. Although Edmans presented evidence of empirical research by others that is consistent with his model and Bhide provided logic and specific incidents that supported his model, the contradictory predictions that I derive from these two papers has not been directly tested to my knowledge. My application of the Edmans’ model to institutional investors leads to hypoth esis H2A and the predictions that I derive from Bhide’s theory leads to hypothesis H2B. H2A : Institutional investors will encourage R&D investment more as firm stock liquidity increases H2B : Institutional investors will encourage R&D investment more as firm stock liquidity decreases Previous research has provided evidence that institutio nal investors’ informational advantage over other investors gives the m the ability to be more effective monitors. The superior monitoring ability of institutional investors may vary with the level of firm information asymmetry be tween insiders and outside shareholders. Zeckhauser and Pound (1990) provide eviden ce that monitoring by

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72 another group of informed investors, shareholders that own more than 15% of a firm, is only effective in firms with low information asymmetry. This indicates that it is possible that institutional shareholders will not b e able to effectively monitor R&D investment in firms with high information asymmetr y. On the other hand, institutional investors may be able to more effectively exploit their informational advantage in firms with high information asymmetry l eading to more effective monitoring of R&D in such firms. These two conflicting possibilities are the basis for my next two hypotheses. H3A : Institutional investors will encourage R&D investment more as information asymmetry decreases H3B : Institutional investors will encourage R&D investment more as information asymmetry increases Agency costs are incurred by investors when a firm’s manag ement uses its superior knowledge of the firm’s business activities to make decisions that benefit management at the expense of shareholders. Ag ency-based free cash flow theory (Easterbrook (1984) and Jensen (1986)) sugg ests that firms with higher free cash flow and poor growth opportunities h ave higher discretionary funds that can be misused by management. If institutional investors are better monitors than other investors, agency-based theory implies that institutional investo rs will encourage R&D

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73 investment more in firms with good investment opportun ities, but they will not encourage R&D investment more in firms with high free cash flow (unless the high free cash flow is accompanied by good investment op portunities). This leads to my final hypothesis. H4 : Institutional investors will encourage R&D investment more as investment opportunities increase. In the absence of in creased investment opportunities, institutional investors will not encoura ge R&D investment more as free cash flow increases. An endogenous relationship probably exists between inst itutional investors and investment policy so simply showing a relationship bet ween institutional investors and R&D investment will not provide sufficient evidence to support any of the investment policy theories. Causality is also impo rtant. In all my hypotheses, a change in institutional ownership leads t o a change in R&D investment policy. 2.3 Data, Methods and Summary Statistics 2.3.1 Data and Methods

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74 I compile yearly ownership data for institutional and insider ownership from CDA / Spectrum Compact Disclosure from 1990 to 2005 Utilities and financial firms are excluded because they are highly r egulated. The ownership data and Compustat data are then merged. The final sa mple includes 10,668 firms and 79,890 firm-years. If a firm is missing data o r is not present in the sample for enough firm-years to perform certain analysi s, it is not used. Following Bushee (1998), I use R&D investment per shar e (adjusting for stock splits) as my primary measure of R&D investment. I a lso use R&D to assets as a measure of R&D investment for some of my robust ness checks. Many others have used R&D to sales as a measure of R& D investment, but my sample includes numerous small firms with negligible sales Therefore, results using R&D to sales as a dependent variable tend to be dominated by firms with the lowest sales figures. R&D investment per share is an effective measure to use in discerning if a firm increased or decreased R&D in vestment, but it does not provide a proper scale for use in linear regressions. Therefore, I use logit regressions in my analysis using a binary dependent varia ble which indicates either R&D increases or decreases. As in previous studies, missing values of R&D expenditures are assumed to be zero (e.g., Coles, D aniel, and Naveen (2006) and Cheng (2008)). Since a variety of factors can jointly affect institutio nal ownership and investment levels, thus inducing a spurious correlation, se veral control variables must be used in my regressions. I start with the same contr ol variables used by Wahal and McConnell (2000) in their study of the effe cts of institutional investors

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75 on R&D and capital investment with one exception; I sub stitute q for the book-tomarket ratio. Following Dlugosz et al. (2006), I calcu late the variable q as the ratio of the market value of assets to the book value of assets where market value is calculated as the sum of the book value of asset s and the market value of common stock less the book value of common stock and defer red taxes. I use total debt to total assets because firms may for ego R&D investment if funds are required to service debt. I include earnin gs before interest and taxes (EBIT) scaled by total assets because the availability of internally generated firms may have an impact on R&D investment decisions. I use insi der percentage ownership and insider percentage ownership squared becau se insider owners are widely documented to have an effect on corporate p olicies and firm value (e.g. Morck, Shleifer and Vishny (1988)). I also use log of sales as an independent control variable to control for firm size I add some control variables that were not used by Wah al and McConnell (2000). Capital expenditures scaled by assets is used to c ontrol for funds required for this use that are not available for R&D investment and for transition into a more mature firm life-cycle which requires a dif ferent investment mix (Bushee (1998)). I also use a proxy for firm life-cycle retained earnings to the book value of total equity (DeAngelo, DeAngelo, and Stulz (2006)), because R&D investment may vary as a firm becomes more mature. I use the log of market capitalization of equity because smaller firms are more likely to suffer cash flow constraints that may limit cash available for R&D investm ent (Jalilvand and Harris (1984)). I use free cash flow scaled by total assets becau se firms with negative

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76 free cash flow may be forced to curtail R&D expenditur es to preserve funds (Bushee (1998)). Free cash flow is defined as net income plus depreciation and amortization minus capital expenditures. In some of my analysis, I determine if liquidity has an effect on the relationship between institutional investors and R&D in vestment. Therefore, I use firm stock turnover as a proxy for liquidity as a control v ariable. Firm stock turnover is defined as the number of common shares trad ed in a year divided by common shares outstanding. The detailed definitions of a ll variables are shown in Table 2 1. The relationship between institutional investors and R &D investment is almost certainly endogenous and my hypotheses are contin gent on institutional investors influencing R&D. Therefore, I must use a regre ssion methodology which accounts for endogeneity and establishes causality. 4 I run regressions on changes in dependent variables from year t – 1 to t on changes in independent variables from t – 2 to t – 1 to establish causality. I use firm fixed effect regressions to control for all stabl e characteristics of a firm (including industry), whether measured or not. I use ye arly dummy variables to control for time-varying omitted characteristics. Firm a nd year fixed effects alleviate endogeneity problems. Firm fixed effects reg ressions with yearly dummy variables effectively give a separate intercept to each year. Intercepts in fixed effects regressions are calculated as an average value of t he unobserved fixed effects for each firm. This intercept and the yearly inte rcept values are not 4 I attempted two-stage least squares’ (instrumental variables) regressions but was unable to come up with instrumental variables which were stat istically and conceptually sound.

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77 relevant to my analysis. Therefore, the intercept term and yearly dummy coefficients are not reported in my regression results. Although I use firm and year fixed effects and control variables in the change regressions to control for endogeneity, I also use the Arellano and Bond (1991) difference Generalized Method of Moments (diff erence GMM) methodology for robustness. Difference GMM is ideal fo r use in panel data with limited time periods, a large number of firms, indep endent variables that are not strictly exogenous, and firm fixed effects. The differ ence GMM method I use is explained in-depth in Appendix C. 2.3.2 Summary Statistics and Data Correlations Table 2 2 displays sample summary statistics. Panel A i ncludes all firms and panel B includes only firm-years in which the firm made R&D investments. Statistics are shown for two time periods, 1990 – 1997 a nd 1998 – 2005, and for the total sample. Means are shown and medians are shown in parentheses below. There are patterns in the data for all firms and in firms with R&D investment. The percentage ownership of institutional investors increases over time. Institutional ownership for all firms and for f irms that invest in R&D is quite similar. R&D expenses to sales increases from the first pe riod to the next. Notably, the average R&D to sales ratio is much higher than the median R&D to

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78 sales ratio in all groups. This is an indication of skewn ess. The average is dominated by a few firms with very large R&D to sales ratios. Firm size and q increase from the first time period to the next as wel l. Retained earnings to total equity is a proxy for firm life-cycle in which a more positive number indicates a more mature firm. Overall, firms included in the sample are less mature in the later years. This is probab ly because of the large number of firms which came public during the run-up to the internet bubble. Compared to the entire sample, firms with R&D expenses are less mature (earlier in their life-cycle) and have higher liquidi ty. Table 2 3 displays correlations for selected firm vari ables. Correlations that are significant at the 5% level are marked with a n asterisk. R&D to sales ( R&D ) is significantly negatively correlated with institutio nal ownership ( Inst ). Institutional ownership ( Inst ) is significantly negatively correlated with Tobin’s q ( q ) and significantly positively correlated with free cash flow to assets ( FCF ). Institutional ownership is significantly positively rel ated to Market value of common stock ( MktCap ). Free cash flow to assets is significantly negatively relate d to Tobin’s q Retained equity to total equity ( LifeCycle ) and firm stock turnover ( Liquidity ) are not significantly correlated with any of the other var iables.

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79 Table 2 1: Variable Definitions R&D Variable Description Definition Panel A: Summary Statistics and Correlation Table V ariables N Number of Firms The number of firms. Inst Institutional Ownership The fraction of shares owned by institutional investors. R&D R&D Expenses Research and development expenses divided by previous year’s sales q Investment Opportunities Market value of assets to the book value of assets MktCap Market Capitalization The dollar market value of common stock in millions. LifeCycle Firm Life-cycle The ratio of retained earnings to t otal equity. Liquidity Stock Turnover Number of common shares traded in a year divided by common shares outstanding FCF Free Cash Flow Net income plus depreciation and amortization minus capital expenditures scaled by total assets. Panel B: Regression Dependent Variables (Measured as changes in values from year t – 1 to t .) R&D_Incr R&D Increase Binary variable equal to one if there is an increas e in R&D expenses per split-adjusted common share and zero otherwise. R&D_Decr R&D Decrease Binary variable equal to one if there is a decrease in R&D expenses per split-adjusted common share and zero otherwise. R&D_Assets R&D to Assets R&D expenses divided by previous year’s total assets Panel C: Regression Independent Variables (Measured as changes in values from year t – 2 to t 1.) Inst Institutional Ownership The fraction of shares owned by institutional investors. q Investment Opportunities Market value of assets to the book value of assets Debt Debt Ratio Debt to assets. ROA Return on Assets Earnings before interest and taxes divided by total assets. Insider Insider Ownership The fraction of shares owned by i nsiders. Insider2 Insider Ownership Squared The squared value of Insider. MktCap Market Capitalization The dollar market value of common stock in millions. CapEx Capital Expenditures Capital expenditures to total assets FCF Free Cash Flow Net income plus depreciation and amortization minus capital expenditures scaled by total assets. Liquidity Stock Turnover Number of common shares traded in a year divided by common shares outstanding LifeCycle Firm Life-cycle The ratio of retained earnings to t otal equity. Revenue Revenue The logarithm of firm revenue.

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80 Table 2 2: Summary Statistics Panel A: All Firms Years N Inst R&D q MktCap LifeCycle Liquidity FCF 1990 1997 37492 28.9% 1.155 2.81 2106 -0.69 4.46 -0.16 (23.6%) (0.000) (1.85) (163) (0.29) (0.64) (0.01) 1998 2005 42398 33.3% 1.656 4.68 4891 -0.53 4.80 -0.39 (25.8%) (0.003) (1.86) (350) (0.18) (0.86) (0.01) Total 79890 31.3% 1.433 3.82 3603 -0.61 4.64 -0.28 (24.6%) (0.000) (1.85) (239) (0.24) (0.74) (0.01) Panel B: Firms with R&D Expenses 1990 1997 17240 29.8% 2.479 3.04 3007 -1.75 6.88 -0.11 (24.1%) (0.059) (2.12) (157) (0.26) (0.75) (0.02) 1998 2005 21751 33.3% 3.197 3.97 6360 -0.48 6.30 -0.33 (25.8%) (0.096) (2.23) (317) (0.01) (1.01) (-0.00 ) Total 38991 31.8% 2.896 3.56 4894 -1.04 6.55 -0.24 (24.9%) (0.078) (2.18) (226) (0.14) (0.88) (0.01) Means are shown on the first row and medians are sh own in parentheses on the second row. Table 2 3: Correlations R&D Inst q MktCap LifeCycle Liquidity Inst -0.0133* q 0.0028 -0.0135* MktCap -0.0049 0.0865* -0.0019 LifeCycle -0.0010 0.0013 0.0013 0.0009 Liquidity -0.0002 -0.0009 -0.0003 -0.0008 0.0002 FCF -0.0032 0.0232* -0.4194* 0.0023 -0.0008 0.0000 indicates two-tailed significance at 5%.

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81 2.4 The Effect of Institutional Owners on R&D Inves tment 2.4.1 Does Increased R&D Lead to Lower Earnings? It is generally assumed that corporate investment in R&D will have a longterm payoff in the aggregate. Otherwise, there would be no reason to make such investments. An essential component of arguing that a reduction in such investment is myopic in nature is an existence of a nega tive relationship between investment and short-term reported earnings. This link se ems clear because, as noted in Wahal and McConnell (2000), accounting method s decrease short-term earnings as R&D spending is expensed immediately, but an increase in earnings from these investments may not occur for years. Neverthel ess, I use a method similar to the one they used to show a negative relati onship between investment spending and short-term earnings for my sample. I run firm fixed effect regressions using current year n et income before extraordinary items divided by total assets from the p revious year as the dependent variable. The only independent variable i s current R&D expenditures divided by the previous year’s sales. The results for the se regressions are not shown in any table, but are described here. I run the regression for the entire sample, for year s 1990 – 1997, and for years 1998 – 2005. The coefficient is significantly negat ive in all three regressions with t -statistics of 8.58, 3.80 and 5.67 respectively. I also r un the

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82 regression on a year-by-year basis. The coefficients for the R&D variables on a year-by-year basis are all negative with a minimum t -statistic of 2.10. The value for R 2 is over 0.05 for all but two of the years. The evide nce indicates that R&D expenditures reduce current reported earnings. 2.4.2 Do Institutional Owners Influence R&D Investment? I investigate the influence that institutional invest ors have on R&D investment by estimating the following firm and year fixed effects logit model. (2-1) it it it i t it Control Inst Firm Year RDChg 1 1 The dependent variable RDChg it is a binary variable set to either zero or one. In most of my analysis, it is set to one if there i s an increase in R&D investment per share and to zero if not. In a robustn ess check, it is set to one if there is a decrease in R&D investment per share and to zero if not. The independent variable of interest ( Inst it-1 ) represents the effect of changes in institutional ownership percentage on changes in R&D inv estment in the following year. In model (2-1), Year t represents year fixed effects, Firm i represents firm fixed effects, Control it-1 represents a vector of time-varying firm level control variables ( q debt, ROA, insider ownership, insider ownership square d, log of market capitalization, capital expenditures to assets, fr ee cash flow to assets,

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83 stock turnover, retained equity to total equity, and l og of revenue), and it is the error term. The dependent variable is calculated on the change in R&D from year t 1 to year t The independent variables are measured as the change from year t – 2 to year t – 1. The logit model drops firms from the regression t hat never have a change in the dependent variable. This means that when the dependent variable is an R&D increase binary variable, firms that increase t heir R&D investment in every year of the sample and firms that don’t increase their R&D investment in any year of the sample are dropped from the regression I consider this an advantage to the model since only firms that change R&D policy are included in regression samples. Table 2 4 reports results on the influence that chang es in institutional ownership have on R&D investment per share increases in t he subsequent year. The first regression uses only control variables as indepen dent variables. Increases in R&D investment occurs more often as return on assets, market capitalization, free cash flow, and revenue increase a nd as q decreases,. The second regression shows that an increase in institutio nal ownership leads to an increased probability that a firm will incr ease R&D investment in the ensuing year. This result could simply be a byproduct of a tendency of institutional investors to invest more in firms that reg ularly increase their investment in R&D. To control for this possibility, the third regression is run only on firms that did not increase R&D investment in year t – 2. The third regression indicates that an increase in institutional investor owne rship has a positive effect

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84 on the probability of an R&D investment increase even if the firm did not increase R&D investment in the year preceding the increase in in stitutional ownership. The fourth regression is run only on firms that increas ed R&D investment in year t – 2. The evidence indicates that institutional inve stors encourage R&D investment increases in this group as well. The results for one of the control variables appear to be counterintuitive and deserve some discussion. It seems that R&D investment should go up as q increases. But, q is largely a ratio of market capitalization to book val ue of assets. Therefore, the counterintuitive coefficient for q could be a function of the effect of q on R&D investment levels being overwhelmed by the ef fect of other control variables, particularly market capitalization. I tested this possibility by substituting total assets for market capitalization as a proxy for firm size. After the substitution, the results were very similar to the ones shown in Table 2 4 except that the coefficient for q switched from significantly negative to significantly positive indicating that the strength of the market capi talization control variable was causing the counterintuitive result for the q control variable. Institutional investors use their influence to persuade management to raise R&D investment. This holds true whether or not the fi rm increased their R&D investment in the previous year. Results for robustness ch ecks using data from the years 1990-1997 and 1998-2005 separately confirm t hat institutional investors encourage higher R&D. Another robustness check co nfirms that institutional investors discourage R&D cuts as well. A fin al robustness test using R&D to assets as the dependent variable and the Arellan o and Bond (1991)

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85 difference Generalized Method of Moments (difference GMM) methodology to further control for endogeneity also confirms that inst itutional ownership increases are positively related to subsequent R&D. Robu stness results are shown and discussed in Appendix B.

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86 Table 2 4: Institutional Ownership and R&D (1) (2) (3) (4) All Firms All Firms No R&D Incr. in year t 2 R&D Incr. in year t 2 R&D_Incr R&D_Incr R&D_Incr R&D_Incr Inst 0.8576*** 0.8496*** 0.6722*** (5.54) (3.13) (2.95) q -0.0406*** -0.0374*** -0.0366*** -0.0470*** (4.97) (4.64) (2.94) (3.23) Debt -0.0920 -0.0807 0.1262 -0.5816** (1.18) (1.04) (1.10) (2.39) ROA 0.1942* 0.1969* -0.0509 0.1756 (1.84) (1.85) (0.40) (0.75) Insider -0.1552 -0.1622 -0.6080 0.8499 (0.46) (0.48) (1.12) (1.57) Insider2 0.3269 0.3213 1.0153 -0.7765 (0.74) (0.72) (1.39) (1.10) MktCap 0.6717*** 0.6324*** 0.6192*** 0.6859*** (15.48) (14.48) (9.16) (9.06) CapEx 0.2181 0.1729 0.1679 -0.0410 (0.96) (0.76) (0.47) (0.12) FCF 0.1560** 0.1637*** 0.1714* 0.3113** (2.54) (2.59) (1.89) (2.27) Liquidity -0.0001 -0.0001 -0.0001 0.0188 (0.24) (0.23) (0.18) (0.98) LifeCycle 0.0001 0.0001 0.0001 -0.0025** (0.66) (0.64) (0.91) (2.21) Revenue 0.0912** 0.0849** 0.0272 0.2382*** (2.47) (2.30) (0.53) (3.37) Observations 18434 18215 6627 8630 Number of Firms 2769 2757 1607 1814 Pseudo R-sqr. 0.04 0.05 0.06 0.07 Absolute value of z statistics in parentheses significant at 10%; ** significant at 5%; *** sig nificant at 1% This table reports estimates of firm and year fix ed effect logit regressions of increases (from year t 1 to t ) in R&D expenditures ( R&D_Incr ). All independent variable values are calculated as chang es in that independent variable from year t 2 to t 1. Regressions (1) and (2) include all firms. Regression (3) includes only firms that had no R&D increase in year t 2 and regression (4) includes only firms that had an R&D increase in year t 2.

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87 2.4.3 Is Stock Liquidity a Factor? According to my hypothesis derived from a model propos ed by Edmans (2009), institutional investors positive influence on su bsequent R&D investment should primarily be concentrated in firms with high stock liquidity. According to my interpretation of Bhide (1993), this relationship should be stronger in firms with low stock liquidity. I test these predictions using f irm stock turnover as a proxy for firm stock liquidity. I sort the sample of fir ms each year into liquidity deciles. I assign each firm-year to one of three groups. Firms in the bottom three deciles have low liquidity, those in the next four de ciles have medium liquidity, and those in the highest three deciles have high liqu idity. The median R&D to sales ratio for firm-years in which the firm made an R&D investment in the low, medium, and high liquidit y groups are 3.48%, 5.90% and 14.76% respectively. The percentage of firm-years i n which the firm made an R&D investment in the low, medium, and high liquidit y groups are 41%, 49% and 59% respectively. Thus, firms with greater liquidity ar e prone to invest more and more often in R&D. I run regressions using the firm and year fixed effects logit model (2-1) that shows the effect that changes in institutional ownership have on R&D investment per share increases ( R&D_Incr ) in the subsequent year. Regressions are run on the low liquidity, medium liquidity, and high liqui dity groups separately based on which group a firm is in during year t – 1. Results are shown in

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88 Table 2 5. The first and second regression, which includes only firm s in the low and medium liquidity groups respectively, shows that institut ional investors have no significant effect on R&D increases in these two groups. O n the other hand, the third regression shows that institutional investors encoura ge R&D increases in firms with high liquidity. These results are consistent wit h the hypothesis derived from the Edmans’ (2009) model which predicts that instit utional investors will encourage R&D in firms with high stock liquidity. A robu stness check using difference GMM supports this finding. It is discussed in A ppendix B. The hypothesis derived from the arguments of Bhide (1993) is not supported.

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89 Table 2 5: Institutional Ownership, R&D, and Stoc k Liquidity (1) (2) (3) Low Liquidity Medium Liquidity High Liquidity R&D_Incr R&D_Incr R&D_Incr Inst -0.2674 0.2409 1.1890*** (0.47) (0.72) (5.45) q -0.0656** -0.0267 -0.0315*** (2.02) (1.46) (2.60) Debt 0.0463 -0.4591* -0.8957*** (0.68) (1.76) (3.13) ROA -0.1388 0.2399 0.4805** (0.51) (1.17) (2.24) Insider -0.3025 -0.4556 0.0283 (0.43) (0.74) (0.04) Insider2 0.2158 0.9333 -0.0420 (0.24) (1.10) (0.05) MktCap 0.5437*** 0.6311*** 0.6303*** (4.31) (6.77) (9.05) CapEx 0.5539 1.0343** -0.3959 (0.79) (2.00) (1.18) FCF 0.5882*** 0.1642 -0.0129 (2.67) (1.52) (0.09) Liquidity 0.1066 -0.0779 -0.0001 (0.48) (1.50) (0.13) LifeCycle -0.0001 -0.0003 0.0002 (0.16) (0.38) (0.43) Revenue 0.1478 0.0440 0.0425 (1.45) (0.59) (0.68) Observations 3224 6166 5521 Number of Firms 741 1303 1127 Pseudo R-squared 0.03 0.04 0.09 Absolute value of z statistics in parentheses significant at 10%; ** significant at 5%; *** sig nificant at 1% This table reports estimates of firm and year fixed effect logit regressions of increases (from year t 1 to t ) in R&D expenditures ( R&D_Incr ). All independent variable values are calculated as changes in that independent variable from year t 2 to t 1. Sample firms used in regressions (1), (2), and ( 3) include only Low, Medium and High Liquidity firms, respectively. The Low, Medium and High Liquidity groups include the lowest three, middle four, and highest three Liquidity deciles from year t 1, respectively. Deciles are formed on a yearly basis.

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90 2.4.4 Is Information Asymmetry a Factor? Zeckhauser and Pound (1990) find that large sharehold ers (holding over 15% of a firm) are not effective monitors of firms wit h high information asymmetry between managers and investors. Their result raises the p ossibility that institutional investors will be ineffective monitors of firms with high information asymmetry. If this is true, institutional investors will not encourage increased R&D investment in firms with high information asymmetr y because they will not be able to ascertain the true value of R&D spending i n such firms. A countervailing possibility is that institutional investor s will encourage R&D investment more in firms that have high information a symmetry because their superior monitoring ability will allow them to discern the value of R&D investments more readily in such firms. I test these contradictory hypotheses using retained ear nings to total equity ( LifeCycle ) as a proxy for information asymmetry. DeAngelo, DeA ngelo and Stulz (2006) use this measure as a proxy for firm l ife-cycle. They assert that this is a valid proxy for firm information asymmetry. This assertion appears logical because the more mature a firm is the more inf ormation an investor will have about the firm to judge its prospects, all else b eing equal. I sort the sample of firms each year into informatio n asymmetry deciles. I assign each firm-year to one of three groups. Firms in the bottom three deciles (Early LifeCycle ) have high information asymmetry, those in the next four deciles

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91 (Middle LifeCycle ) have moderate information asymmetry, and those in t he highest three deciles (Late LifeCycle ) have low information asymmetry. The median R&D to sales ratio for firm-years in which a firm made an R&D investment in the early, middle, and late LifeCycle groups are 19.26%, 6.95% and 3.40% respectively. The percentage of firmyears in which the firm made an R&D investment in the early, middle, and lat e LifeCycle groups are 60%, 44% and 47% respectively. Thus, firms earlier in t heir LifeCycle (with higher information asymmetry) are prone to invest mor e and more often in R&D. I run regressions using the firm and year fixed effect s model (2-1) that shows the effect that changes in institutional ownership have on R&D investment increases in the subsequent year. Regressions are run on t he early, middle, and late LifeCycle groups separately based on which group a firm is in du ring year t – 1. The results are shown in Table 2 6. The first two regressions show that institutional investo rs encourage R&D increases in firms with high and moderate information a symmetry. The third regression shows that institutional investors do not encou rage R&D investment increases at a significant level in firms with low infor mation asymmetry. The pattern indicates that institutional investors encourage R&D investment more in firms with higher information asymmetry. This is consiste nt with the assertion that the superior monitoring ability of institutional in vestors allows them to discern the value of R&D investments more readily than other inve stors, even in firms with high information asymmetry.

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92 A robustness check using the difference GMM methodology con firms this result. Zeckhauser and Pound (1990) used R&D levels as a proxy for information asymmetry. Therefore, I also use R&D to assets as a proxy for information asymmetry to add robustness to my results. This robustness che ck also confirms that institutional investors encourage increased R&D in firms with high information asymmetry. Robustness checks are displayed and discussed in Appendix B.

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93 Table 2 6: Institutional Ownership, R&D, and Firm Life-cycle (1) (2) (3) Early LifeCycle Middle LifeCycle Late LifeCycle R&D_Incr R&D_Incr R&D_Incr Inst 0.6381** 0.7078*** 0.4883 (2.09) (2.80) (1.43) q -0.0458*** -0.0334* -0.0017 (3.35) (1.83) (0.09) Debt -0.3202 -1.0502*** 0.0176 (1.08) (2.89) (0.26) ROA 0.5454*** 0.6316 -0.1661 (3.55) (1.37) (0.97) Insider 0.3653 -0.0498 -0.6912 (0.62) (0.08) (0.99) Insider2 -0.1883 0.2393 1.0595 (0.23) (0.30) (1.18) MktCap 0.5805*** 0.6403*** 0.5050*** (8.46) (6.70) (4.64) CapEx 0.6182 0.1985 -0.6114 (1.58) (0.50) (0.91) FCF 0.0582 0.6432** 0.2967* (0.73) (1.97) (1.91) Liquidity 0.0847*** 0.0017 -0.0929** (3.84) (0.09) (2.03) LifeCycle 0.0002 -0.0460 -0.0002 (1.12) (1.13) (0.52) Revenue -0.0130 0.1008 0.3387** (0.30) (0.85) (2.41) Observations 4637 5793 5693 Number of Firms 1028 1154 855 Pseudo R-squared 0.09 0.06 0.03 Absolute value of z statistics in parentheses significant at 10%; ** significant at 5%; *** sig nificant at 1% This table reports estimates of firm and year fix ed effect logit regressions of increases (from year t 1 to t ) in R&D expenditures ( R&D_Incr ). All independent variable values are calculated as changes in that independent variable from year t 2 to t 1. Sample firms used in regressions (1), (2), an d (3) include only Early, Middle, and Late LifeCycle firms, respectively. The Early, Middle, and Late LifeCycle groups include the lowest three, middle four, and highest three LifeCycle deciles from year t 1, respectively. Deciles are formed on a yearly basis.

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94 2.4.5 Are Potential Agency Problems a Factor? According to agency-based theory, institutional investor s will encourage R&D investment more in firms with good investment oppor tunities, but they will not encourage R&D investment more in firms with high free cash flow (unless the high free cash flow is accompanied by good investment op portunities). I test this prediction using q as a proxy for investment opportunities. I sort the sample of firms each year into investment opportun ity deciles. I assign each firm-year to one of three groups. Firms in the bottom three deciles (Low q ) have poor investment opportunities, those in the next four deciles (Medium q ) have moderate investment opportunities, and those in the h ighest three deciles (high q ) have good investment opportunities. The median R&D to sales ratio for firm-years in which the firm made an R&D investment in the low, medium, and high q groups are 2.75%, 5.66% and 16.70% respectively. The percentage of firm-years in wh ich the firm made an R&D investment in the low, medium, and high q groups are 34%, 49% and 65% respectively. Thus, firms with higher q ’s (and better investment opportunities) are unsurprisingly prone to invest more and more often in R&D. I run regressions using the firm and year fixed effects logit model (2-1) that shows the effect that changes in institutional ownership have on R&D investment increases in the subsequent year. Regressions are run on th e low, medium, and

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95 high q groups separately based on which group a firm is in dur ing year t – 1. The results are shown in Table 2 7. The first regression indicates that for firms with poor investment opportunities, there is not a significant relationshi p between institutional ownership changes and the probability of an R&D increase in the following year. The second and third regressions indicate that institution al investors encourage R&D investment increases in firms with moderate and good investment opportunities. These results are consistent with agency-b ased theory. Institutional investors appear to only use their influe nce to persuade management to increase R&D when sufficient investment op portunities exist. Agency-based theory also predicts that institutional inv estors will not encourage higher R&D simply because high free cash flow increases the amount of discretionary cash that is available to management. I test this prediction by assigning each firm-year to one of three groups: low cash flow (bottom three deciles), moderate cash flow (middle four deciles), and high cash flow (top three deciles). Once again, I use the firm and year fixed ef fects logit model (2-1). The results are shown in Table 2 8. The median R&D to sales ratio for firm-years in which the firm made an R&D investment in the low, medium, and high free cash flow groups are 24.34%, 4.27% and 5.87% respectively. The percentage of firmyears in which the firm made an R&D investment in the low, medium, and high FCF groups are 56%, 43% and 50% respectively. Thus, firms with the lowest fr ee cash flow to asset ratios are prone to invest more and more often in R&D than the other two groups.

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96 The first and second regressions show that institutional i nvestors have a positive effect on R&D investment in firms with low an d medium free cash flow rates. The third regression indicates that institutional investors do not have a significant effect on R&D investment in firms with high free cash flow. The pattern indicates that institutional investors’ encourage ment of R&D investment does not increase as firm free cash flow rises. In fact, it wanes in the highest free cash flow firms. The evidence indicates that an increase in institutiona l investors leads to an increase in R&D investment, especially in firms with g ood investment opportunities. Institutional investors do not encourag e R&D investment in firms with high free cash flow. Therefore, institutional in vestors help to control agency problems by encouraging management to invest more in R&D in firms because good investment opportunities exist, but not simply be cause cash is available. A robustness check using difference GMM confirms this result. An additional robustness check using a combination of investm ent opportunities and free cash flow is also supportive. Robustness checks are shown and discussed in Appendix B.

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97 Table 2 7: Institutional Ownership, R&D, and Inve stment Opportunities (1) (2) (3) Low q Medium q High q R&D_Incr R&D_Incr R&D_Incr Inst 0.3045 0.7199** 0.7077*** (0.77) (2.54) (2.64) q -0.1544 -0.1929*** -0.0359*** (1.49) (3.54) (3.77) Debt 0.0290 0.1851 -0.6954*** (0.10) (1.05) (3.13) ROA 1.0324** 0.6358** 0.2255 (2.24) (2.18) (1.38) Insider 0.5603 -0.6163 -0.7136 (0.66) (1.06) (1.15) Insider2 0.1019 0.5233 1.1121 (0.09) (0.70) (1.31) MktCap 0.2639** 0.8201*** 0.6156*** (2.53) (6.24) (7.93) CapEx 0.6142 0.0936 -0.1428 (1.01) (0.17) (0.44) FCF 0.2224 0.1549 0.0788 (1.49) (1.12) (0.63) Liquidity 0.0224 0.0296 -0.0082 (0.64) (1.27) (0.40) LifeCycle 0.0046* -0.0008 0.0001 (1.89) (1.37) (0.74) Revenue 0.1569 -0.1148 0.0238 (1.23) (1.09) (0.48) Observations 3019 6272 5507 Number of Firms 676 1312 1108 Pseudo R-squared 0.03 0.05 0.05 Absolute value of z statistics in parentheses significant at 10%; ** significant at 5%; *** sig nificant at 1% This table reports estimates of firm and year fix ed effect logit regressions of increases (from year t 1 to t ) in R&D expenditures ( R&D_Incr ). All independent variable values are calculated as changes in that independent variable from year t 2 to t 1. Sample firms used in regressions (1), (2), an d (3) include only Low, Medium and High q firms, respectively. The Low, Medium and High q groups include the lowest three, middle four, and highest three Liquidity deciles from year t 1, respectively. Deciles are formed on a yearly basis.

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98 Table 2 8: Institutional Ownership, R&D, and Free Cash Flow (1) (2) (3) Low FCF Medium FCF High FCF R&D_Incr R&D_Incr R&D_Incr Inst 0.6301* 0.7130** 0.2497 (1.84) (2.45) (0.78) q -0.0273*** -0.0413* -0.0292 (2.62) (1.75) (0.92) Debt 0.0424 -0.6562* -0.3594 (0.63) (1.92) (0.89) ROA 0.3599** -0.2821 -0.7376** (2.39) (0.76) (2.09) Insider 0.2562 -0.1966 -0.2641 (0.36) (0.31) (0.39) Insider2 -0.5947 0.5828 0.0960 (0.64) (0.71) (0.10) MktCap 0.5052*** 0.6855*** 0.4233*** (6.78) (6.68) (3.34) CapEx 0.3918 0.3994 0.6130 (1.12) (0.84) (0.73) FCF 0.1237 0.0297 0.0732 (1.24) (0.33) (0.86) Liquidity -0.0001 -0.0095 -0.0271 (0.17) (0.43) (0.93) LifeCycle -0.0000 0.0005 -0.0007 (0.17) (0.40) (0.76) Revenue -0.0831* 0.0474 0.3471** (1.75) (0.40) (2.00) Observations 3341 5458 4877 Number of Firms 828 1245 967 Pseudo R-squared 0.09 0.04 0.03 Absolute value of z statistics in parentheses significant at 10%; ** significant at 5%; *** sig nificant at 1% This table reports estimates of firm and year fix ed effect logit regressions of increases (from year t 1 to t ) in R&D expenditures ( R&D_Incr ). All independent variable values are calculated as changes in that independent variable from year t 2 to t 1. Sample firms used in regressions (1), (2), an d (3) include only Low, Medium and High FCF firms, respectively. The Low, Medium and High FCF groups include the lowest three, middle four, and highest three Liquidity deciles from year t 1, respectively. Deciles are formed on a yearly b asis.

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99 2.5 Conclusion Research and development (R&D) investment is an importa nt determinant of the future growth in revenue and earnings for man y corporations. The amount of financial resources which are allocated to R&D is an i mportant financial decision for those corporations and a key to survival for many of them. Since institutions own almost 70% of U.S. public corporations, their effect on R&D decisions is important to the success of U.S. corporations. In this paper, I test several hypotheses about the influence institutional i nvestors have on R&D investment policy. I find that companies with higher institutional inve stor ownership, holding other factors constant, invest more in R&D than companie s with lower institutional ownership. I find that an increase in in stitutional ownership leads to an increase in R&D investment. I expand a model that Edmans (2009) proposes about th e effect of blockholders on long-term investment. In his model, blo ckholders make their trading decisions based on the fundamental value of the firm rather than current earnings. The superior monitoring ability of blockhold ers enables them to discern the benefit of the firm investing for the long-term to enhance firm value. Management, which is cognizant of the large shareholder s’ ability to determine the firm’s true value, is thus encouraged to invest for the long-term rather than for short-term profits. Management can thus avoid a depress ed stock share price that results from the blockholders divesting their shares i f management chooses

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100 to invest at a sub-optimal level. I argue that if inst itutional investors herd, they can have an impact similar to the one predicted of block holders in this model. A prediction of the Edmans (2009) model is that the ab ility of large shareholders to encourage higher investment is enhanced by higher firm stock liquidity because the higher liquidity heightens the t hreat of divestment. I find that higher firm stock liquidity enhances the ability of insti tutional investors to use their influence to persuade management to increase their inv estment in R&D. Thus, my results support the Edmans (2009) model. Institutional investors are better informed than other investors. Institutional owners should be able to gauge the long-term benefit of R&D investment more precisely than non-institutional investors. Therefore, I propose a hypothesis that predicts that the positive relationship between instit utional investors and future R&D investment will strengthen in firms with higher in formation asymmetry. My results support this prediction. I find that Institution al investors encourage higher R&D investment primarily in firms with high informat ion asymmetry indicating they have an advantage in discerning the value of R&D investments in such firms. Firms with higher free cash flow and poor growth oppor tunities are susceptible to agency problems because they have higher d iscretionary funds that can be misused by management. Agency-based free cash flow theory predicts that if institutional investors are better mon itors than other investors, they will encourage R&D investment in firms with good invest ment opportunities, but they will not encourage R&D investment simply because a firm has higher free

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101 cash flow. My results support this prediction indicating th at institutional investors help to control agency problems in R&D investment decisio ns. Institutional investor increases precede increases in rese arch and development (R&D) investment overall and specifically in firms with higher stock liquidity, higher information asymmetry, lower free cash flow, and better investment opportunities. Institutional investors effe ctively encourage management to pursue long-term R&D investment policies that are beneficial to shareholders.

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114 Appendices

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115 Appendix A: Payout Robustness Tests This appendix includes robustness checks for tests on the inf luence institutional investors have on payout policy. These r obustness checks have been moved from the main text to this appendix to im prove the flow and clarity of the main text. In all cases, results from the main text are supported. The results in Table A 1, Table A 2, and Table A 3 provide support for agency-based theory. Table A 1 provides evidence that an increase in institutional ownership leads to an increase in total p ayout for two separate time periods: 1990 – 1997 and 1998 – 2005. Table A 2 in dicates that an increase in institutional investors leads to a stronger increase in payouts in firms with poor investment opportunities and high free cash flow. Insti tutional investors do not have an effect on payouts in firms with good investment opportunities or low free cash flow. I employ the Arellano and Bond (1991) difference l inear GMM dynamic panel data methodology to obtain the results shown in Table A – 3. This difference GMM methodology attenuates endogeneity p roblems between dependent and independent variables. Difference GMM m ethodology is explained in greater detail in Appendix C. The resul ts indicate that an increase in institutional shareholders leads to an increase in payou ts, especially in firms with poor investment opportunities and high free cash flow. Table A 4 and Table A 5 display results that prov ide evidence for the adverse selection theory. Table A 4 demonstrates that an increase in institutional ownership precedes a subsequent increase in stock repurchases in both the 1990 – 1997 and 1998 – 2005 time periods. Difference GMM is used to produce the results displayed in Table A 5 which ind icate that institutional investors encourage stock repurchases primarily in firms wit h higher information asymmetry. Support for the substitution hypothesis is provided by the results displayed in Table A 6 and Table A 7. The substitution hy pothesis asserts that the influence of institutional investors will lead to an i ncreased percentage of total payout going towards repurchases. In Table A 6, suppor t is shown for the hypothesis in both the 1990 – 1997 and 1998 – 2005 ti me periods, although the evidence is stronger and more convincing for the latter period. The results for the earlier period are somewhat surpri sing since this time period is entirely included in Fama and French (2001) which finds a decrease in propensity to pay dividends and an increase in repurcha ses. A closer examination of their study indicates that during the 1 990 – 1997 time period, the propensity to pay dividends changed very little (see T able 6 of their study). It also indicates that repurchases declined from the 1988 – 1992 period to the 1993 – 1998 period (see Table 12 of their study). My results f or the earlier time period seem less surprising in light of this information. Still, the contrast between my weak results in the earl y time period and exceptionally strong results in the latter time period raises a question. Why? It could just be a result of the vagaries of the trend not ed in Fama and French (2001). Jagannathan, Stephens, and Weisbach (2000) sho wed that repurchases

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116 are very cyclical with firms increasing stock repurchases afte r poor stock market performances. They also found that dividend increases w ere more common following good performance. Their findings could expl ain the different results for the two time periods. Table A 7 display results for difference GMM regressio ns using payout composition as the dependent variable. The results indica te that institutional investors encourage an increased use of stock repurchases as a percentage of total payout, especially in firms which previously used stock repurchases for more than 50% of their total payout.

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117 Table A 1: Payouts and Time Periods (1) (2) 1990 1997 1998 2005 Payout Payout Inst 0.0118** 0.0138** (2.21) (2.43) q 0.0000 -0.0007*** (0.30) (5.14) Debt -0.0501*** -0.0080** (3.60) (2.47) Turnover -0.0000* -0.0012*** (1.77) (2.87) LifeCycle 0.0000 -0.0000 (1.27) (1.14) MktCap 0.0040* 0.0098*** (1.88) (6.74) ROA -0.0034** -0.0018*** (2.07) (2.94) Insider -0.0243 -0.0184 (1.39) (1.24) Insider2 0.0281 0.0037 (1.64) (0.23) Revenue 0.0005 -0.0014 (0.36) (0.89) Observations 17682 27251 Firms 4809 6128 R-squared 0.13 0.22 Absolute value of t statistics in parentheses significant at 10%; ** significant at 5%; *** sig nificant at1% This table reports estimates of firm and year fix ed effect regressions of changes (from year t 1 to t ) in total payout divided by book value of assets ( Payout ) by time period. All independent variable values are calculated as chang es in that independent variable from year t 2 to t 1. Regression (1) includes the years from 1990 to 1997. Regressio n (2) includes the years from 1998 to 2005.

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118 Table A 2: Payouts, Investment Opportunities, and Free Cash Flow (1) (2) (3) (4) High CashFlow Low q High CashFlow High q Low CashFlow Low q Low CashFlow High q Payout Payout Payout Payout Inst 0.0305** 0.0059 0.0033 0.0080 (2.00) (0.61) (0.54) (1.20) CashFlow -0.0005 0.0122** -0.0039 0.0019* (0.53) (2.50) (1.63) (1.72) q 0.0038 -0.0021*** 0.0005 -0.0004 (0.87) (4.21) (0.42) (1.54) Debt -0.0638* -0.1122*** -0.0075 0.0000 (1.82) (5.66) (1.46) (0.01) Turnover -0.0054 -0.0016*** 0.0000 -0.0010* (1.30) (3.07) (0.52) (1.90) LifeCycle -0.0000 0.0000 0.0000 0.0000 (0.95) (0.29) (0.39) (0.11) MktCap 0.0128** 0.0225*** 0.0062* 0.0043** (2.37) (7.20) (1.96) (2.09) ROA -0.0156 -0.0356*** 0.0043 -0.0022** (0.93) (2.76) (0.73) (2.09) Insider -0.0082 -0.0134 0.0047 0.0027 (0.53) (0.73) (0.17) (0.18) Insider2 0.0087 0.0016 -0.0043 -0.0006 (0.51) (0.07) (0.16) (0.04) Revenue -0.0010 0.0055 -0.0071* -0.0003 (0.18) (1.45) (1.73) (0.21) Observations 10924 13122 11558 8632 Number of Firms 3757 3831 4565 3496 R-squared 0.49 0.20 0.42 0.77 Robust t statistics in parentheses significant at 10%; ** significant at 5%; *** sig nificant at 1% This table reports estimates of firm and year fix ed effect regressions of changes (from year t 1 to t ) in total payout divided by book value of assets ( Payout ). All independent variable values are calculated a s changes in that independent variable from year t 2 to t 1. Sample firms used in regressions (1), (2), (3) and (4) include only firms that are i n the High CashFlow and Low q High CashFlow and High q Low CashFlow and Low q and Low CashFlow and High q groups, respectively. The Low CashFlow and High CashFlow groups include the lowest five and highest five CashFlow deciles, respectively. The Low q and High q groups include the lowest five and highest five q deciles from year t 1, respectively. Deciles are formed on a yearly basis.

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119 Table A 3: Payouts, Investment Opportunities and Free Cash Flow (GMM) (1) (2) (3) (4) (5) All Firms Low q High q Low CashFlow High CashFlow Payout Payout Payout Payout Payout Inst 0.0199** 0.0150** 0.0205 0.0127 0.0266** (2.37) (2.19) (1.56) (1.48) (2.33) Payout 0.0755 0.0585 0.0809*** 0.0492 0.1196*** (2.35) (1.35) (2.56) (1.38) (3.30) q -0.0009 0.0004 -0.0005 -0.0006 0.0006 (0.49) (0.11) (0.30) (0.32) (0.23) Debt -0.4795 -0.0518 -0.0003 0.0343 -0.0848 (1.05) (1.18) (0.01) (0.83) (1.43) Turnover 0.0000 -0.0000 -0.0041* 0.0000 -0.0000 (0.15) (0.24) (1.70) (0.61) (0.51) LifeCycle -0.0001 -0.0000 -0.0001 -0.0001 -0.0001 (0.93) (0.87) (0.93) (1.11) (0.84) MktCap -0.0069 -0.0164* 0.0041 -0.0122 0.0050 (0.62) (1.66) (0.28) (1.37) (0.33) ROA 0.0301 0.0318 0.0026 0.0408 0.0050 (1.26) (0.94) (0.14) (1.01) (0.17) Insider -0.1504** -0.0754 -0.1278 -0.0221 -0.1984 (1.99) (1.26) (1.21) (0.25) (2.05) Insider2 0.1619 0.0703 0.1386 -0.0092 0.2234* (1.50) (1.01) (0.82) (0.08) (1.77) Revenue -0.0544*** -0.0102 -0.0374*** -0.0218 -0.0986 (4.07) (0.91) (3.04) (0.89) (4.29) Observations 35255 18203 17052 16320 20387 Number of Firms 6796 4897 4532 5393 5129 Chi2 ( p -value) 0.000 0.000 0.000 0.000 0.049 J p -value 0.161 0.899 0.106 0.530 0.182 AR(2) p -value 0.190 0.296 0.161 0.187 0.585 Inst lag limits None None None 3 None Payout lag limits None None None None None Robust z stats in parentheses significant at 10%; ** significant at 5%; *** sig nificant at 1% This table reports estimates generated by Arellan o-Bond difference GMM of changes (from year t 1 to t ) in total payout divided by book value of assets ( Payout ). All independent variable values are calculated as chang es in that independent variable from year t 2 to t 1. Sample firms used in regressions (2) and (3) include only Low and High q firms (the lowest and highest five q deciles from year t 1), respectively. Sample firms used in regressions (4) and (5) include only Low an d High CashFlow firms (the lowest and highest five CashFlow deciles from year t 1), res pectively. Deciles are formed on a yearly basis. J is the Hansen-Sargan test of overidentifying restr ictions. AR(2) is the ArellanoBond test of second-order autocorrelation in the er rors. Independent variables Inst and Payout are instrumented using GMM-type instrument lags. A ll available lags are used unless validity tests are rejected, in which case l ags are restricted to the highest number of lags which produce a valid model.

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120 Table A 4: Repurchases and Time Period (1) (2) 1990 1997 1998 2005 Repurch Repurch Inst 0.0065* 0.0152*** (1.89) (2.87) q -0.0000 -0.0006*** (0.29) (4.64) Debt -0.0279*** -0.0079** (4.46) (2.49) Turnover -0.0000 -0.0010** (1.52) (2.56) LifeCycle 0.0000 -0.0000*** (0.78) (2.60) MktCap 0.0038*** 0.0077*** (4.44) (6.12) ROA -0.0020* -0.0016*** (1.67) (2.85) Insider -0.0087 -0.0156 (1.16) (1.31) Insider2 0.0117 0.0085 (1.53) (0.65) Revenue 0.0014* -0.0017 (1.72) (1.13) Observations 17721 27405 Firms 4813 6157 R-squared 0.11 0.26 Absolute value of t statistics in parentheses significant at 10%; ** significant at 5%; *** sig nificant at 1% This table reports estimates of firm and year fix ed effect regressions of changes (from year t 1 to t ) in repurchases divided by book value of assets ( Repurch ) by time period. All independent variable values are calculated as chang es in that independent variable from year t 2 to t 1. Regression (1) includes the years from 1990 to 1997. Regressio n (2) includes the years from 1998 to 2005.

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121 Table A 5: Repurchases and Firm Life-cycle (GMM) (1) (2) (3) All Firms Early LifeCycle Late LifeCycle Repurch Repurch Repurch Inst 0.0181** 0.0178** 0.0090 (2.08) (2.00) (1.03) Repurch 0.0665** -0.0161 0.0553** (2.31) (0.27) (2.00) q -0.0014 0.0029 0.0029 (0.87) (1.12) (1.01) Debt -0.0677 -0.0097 -0.0348 (1.34) (0.22) (0.76) Turnover -0.0000 0.0000 0.0000 (0.14) (0.48) (0.19) LifeCycle -0.0001 0.0000 0.0000 (0.98) (0.16) (0.18) MktCap -0.0014 -0.0317* -0.0031 (0.12) (1.68) (0.22) ROA -0.0125 -0.0049 0.0282 (0.43) (0.20) (1.25) Insider -0.0948 -0.0864 -0.0790 (1.17) (0.85) (0.66) Insider2 0.0714 0.0970 0.1026 (0.64) (0.62) (0.53) Revenue -0.0492*** 0.0252 -0.0916*** (3.59) (1.16) (3.65) Observations 35430 15167 20981 Number of Firms 6823 4359 4285 Chi2 ( p -value) 0.000 0.000 0.000 J p -value 0.140 0.902 0.466 AR(2) p -value 0.404 0.349 0.458 Inst lag limits 3 3 3 Repurch lag limits None 3 None Robust z statistics in parentheses significant at 10%; ** significant at 5%; *** sig nificant at 1% This table reports estimates generated by Arellan o-Bond difference GMM of changes (from year t 1 to t ) in repurchases divided by book value of assets ( Repurch ). All independent variable values are calculated a s changes in that independent variable from year t 2 to t 1. Sample firms used in regressions (2) and (3) include only Early and Late LifeCycle firms, respectively. The Early and Late LifeCycle groups include the lowest and highest fiv e LifeCycle deciles from year t 1, respectively. Deciles are formed on a yearly basis. J is the Hansen-Sargan test of overidentifying restrictions. AR(2) is the Arellano -Bond test of second-order autocorrelation in the errors. Independent variable s Inst and Repurch are instrumented using GMM-type instrument lags. All av ailable lags are used unless validity tests are rejected, in which case lags are restricted to the highest number of lags which produce a valid model.

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122 Table A 6: Payout Composition and Time Period (1) (2) 1990 1997 1998 2005 PayComp PayComp Inst 0.1695 0.3920*** (1.55) (4.28) q -0.0224 -0.0160** (1.17) (2.03) Debt -1.0381*** -0.8678*** (6.17) (7.01) Turnover -0.0707*** 0.0199 (3.03) (1.60) LifeCycle -0.0045 -0.0000*** (1.27) (5.23) MktCap 0.2622*** 0.2691*** (4.42) (7.60) ROA -0.2876 -0.1346 (1.54) (0.95) Insider -0.2815 -0.1641 (1.42) (0.80) Insider2 0.3399 -0.0744 (1.35) (0.27) Revenue 0.1279** 0.0547 (2.25) (1.20) Observations 6937 8996 Firms 1874 2541 R-squared 0.16 0.19 Absolute value of t statistics in parentheses significant at 10%; ** significant at 5%; ***sign ificant at 1% This table reports estimates of firm and year fix ed effect regressions of changes (from year t 1 to t ) in a measure of payout composition ( PayComp ). PayComp is equal to -1 if payout is composed entirely of dividends and 1 if p ayout is composed entirely of stock repurchases. All indepen dent variable values are calculated as changes in that independent variable from year t 2 to t 1. Regression (1) includes the years from 1990 to 1997. Regression (2 ) includes the years from 1998 to 2005.

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123 Table A 7: Payout Composition (GMM) (1) (2) (3) All Firms Dividends > Repurchases at year t 2 Dividends Repurchases at year t 2 PayComp PayComp PayComp Inst 0.1522* 0.2270 0.6629** (1.80) (1.46) (2.42) PayComp 0.2868*** 0.3703*** -0.0287 (9.40) (4.10) (0.17) q -0.1498* -0.2565 0.1344 (1.95) (1.52) (0.51) Debt -0.6513 -1.9627 -5.7731** (1.01) (1.49) (2.22) Turnover -0.1335** -0.3778** -0.1906 (2.16) (2.05) (0.94) LifeCycle -0.0002 0.0025 -0.0000 (0.90) (0.03) (0.01) MktCap 0.3528 0.6304 -1.4589 (1.45) (1.37) (1.63) ROA 0.7810 -0.9096 -4.2384 (0.90) (0.48) (1.28) Insider 0.6801 0.5352 -1.2360 (0.70) (0.26) (0.39) Insider2 -1.4212 -0.1379 1.2102 (1.01) (0.05) (0.24) Revenue -0.4184 0.5868 1.6611 (1.62) (1.15) (1.24) Observations 11911 8328 3583 Number of Firms 2355 1700 1489 Chi2 ( p -value) 0.000 0.000 0.000 J p -value 0.168 0.558 0.612 AR(2) p -value 0.121 0.389 0.103 Inst lag limits 2 3 1 PayComp lag limits None 1 1 Robust z statistics in parentheses, *significant at 10%; **significant at 5%; ***significant at 1% This table reports estimates generated by Arellan oBond difference GMM of changes (from year t 1 to t ) in in a measure of payout composition ( PayComp ). PayComp is equal to 1 if payout is composed entirely of dividends and 1 if p ayout is composed entirely of stock repurchases. All independent variable values are calculated as changes in that i ndependent variable from year t 2 to t 1. Regression (2) includes only firms in which divi dends exceeded repurchases in year t 2 and regression (3) includes only firms in which repurchases exceeded dividends in year t 2. J is the HansenSargan test of overidentifying restrictions. AR(2) is the Arellano-Bond test of se condorder autocorrelation in the errors. Independent variables Inst and PayComp are instrumented using GMMtype instrument lags. All available lags are used unless validity tests are r ejected, in which case lags are restricted to the highest number of lags which produce a valid model.

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124 Appendix B: R&D Robustness Tests Robustness checks for tests on the influence that institutio nal investors have on investment in R&D are included in this appendi x. These checks have been moved here to improve the clarity and flow of t he main text. In all cases, results from the main text are supported. Previous results indicate that an increase in institutio nal ownership leads to a subsequent R&D investment increase. A logical infer ence from this result is that institutional owners will discourage R&D decreases. The results in Table B 1 confirm that institutional investors dissuade R&D cuts. T able B 2 provides evidence that higher institutional ownership results in increased R&D for two separate time periods: 1990 – 1997 and 1998 – 2005. I use the Arellano and Bond (1991) difference linea r GMM dynamic panel data methodology to obtain the results displayed in T able B 3. This methodology alleviates endogeneity problems. Differe nce GMM is a linear method so I use changes in R&D to assets as my dependen t variable when using this method. Difference GMM methodology is explained i n greater detail in Appendix C. The results indicate that a rise in institut ional investors leads to a rise in R&D investment, especially for firms with high st ock liquidity or high information asymmetry. I use an alternate proxy for information asymmetry, R&D intensity (R&D to total assets) instead of firm life-cycle (retained earnin gs to the book value of total equity) to produce the results shown in Table B 4. Fi rms with high R&D intensity have higher information asymmetry. The results indicate that institutional investors encourage R&D in firms with high and low info rmation asymmetry, but this effect appears stronger in firms with high informat ion asymmetry. The difference GMM regressions shown in Table B – 5 ind icate that an increase in institutional investors leads to increased R&D in firms with good investment opportunities and low free cash flow. Institu tional investors have no significant effect on R&D in firms with poor investment opportunities or high free cash flow.

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125 Table B 1: R&D Decreases (1) (2) (3) (4) All Firms All Firms No R&D Decr. in year t 2 R&D Decr. in year t 2 R&D_Decr R&D_Decr R&D_Decr R&D_Decr Inst -0.8551*** -0.6268*** -0.9682*** (5.54) (2.91) (3.30) q 0.0492*** 0.0461*** 0.0554*** 0.0521*** (5.70) (5.37) (3.91) (3.53) Debt 0.0359 0.0470 0.2919 -0.2044 (0.55) (0.70) (1.56) (1.53) ROA -0.2533** -0.2658*** -0.2621 0.0181 (2.50) (2.63) (1.42) (0.13) Insider -0.1508 -0.1669 -0.9825** 0.4669 (0.45) (0.49) (1.96) (0.78) Insider2 0.0302 0.0541 1.0471 -0.9110 (0.07) (0.12) (1.61) (1.13) MktCap -0.6769*** -0.6399*** -0.6934*** -0.6477*** (15.35) (14.39) (9.85) (8.55) CapEx -0.3083 -0.2568 0.1101 -0.4327 (1.38) (1.15) (0.37) (0.99) FCF -0.1100** -0.1088** -0.1227 -0.1785* (2.02) (2.00) (1.26) (1.81) Liquidity 0.0001 0.0001 -0.0204 0.0001 (0.24) (0.23) (1.10) (0.18) LifeCycle -0.0001 -0.0001 0.0021** -0.0001 (0.50) (0.48) (2.00) (0.72) Revenue -0.0461 -0.0374 -0.1426** 0.0351 (1.24) (1.00) (2.35) (0.59) Observations 18440 18215 9858 5389 Number of Firms 2781 2768 1993 1444 Pseudo R-sqr. 0.04 0.04 0.06 0.08 Absolute value of z statistics in parentheses significant at 10%; ** significant at 5%; *** sig nificant at 1% This table reports estimates of firm and year fix ed effect logit regressions of decreases (from year t 1 to t ) in R&D expenditures ( R&D_Incr ). All independent variable values are calculated as chang es in that independent variable from year t 2 to t 1. Regressions (1) and (2) include all firms. Regression (3) includes only firms that had no R&D decrease in year t 2 and regression (4) includes only firms that had an R&D decrease in year t 2.

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126 Table B 2: R&D and Time Period (1) (2) 1990 1997 1998 2005 R&D_Incr R&D_Incr Inst 0.8601*** 0.8353*** (2.73) (4.30) q -0.0797*** -0.0290*** (3.17) (3.46) Debt -0.7951** -0.0231 (2.27) (0.33) ROA 0.8265** 0.1590 (2.30) (1.36) Insider -0.8434 0.5605 (1.36) (1.28) Insider2 1.2460* -0.7551 (1.66) (1.24) MktCap 0.5291*** 0.5797*** (4.93) (11.33) CapEx -0.2828 0.5615* (0.67) (1.77) FCF 0.5970** 0.1402** (2.46) (2.04) Liquidity -0.0001 0.0215* (0.13) (1.65) LifeCycle 0.0001 0.0001 (0.21) (0.78) Revenue -0.0477 0.0572 (0.57) (1.27) Observations 4888 10919 Number of Firms 1236 2126 Pseudo R-squared 0.04 0.06 Absolute value of z statistics in parentheses significant at 10%; ** significant at 5%; *** sig nificant at 1% This table reports estimates of firm and year fixed effect logit regressions of increases (from year t 1 to t ) in R&D expenditures (R&D_Incr) by time period. All indepen dent variable values are calculated as changes in that i ndependent variable from year t 2 to t 1. Regression (1) includes the years from 1990 to 1997. Regression (2) includes th e years from 1998 to 2005.

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127 Table B 3: R&D, Stock Liquidity and Firm Life-cyc le (GMM) (1) (2) (3) (4) (5) All Firms Low Liquidity High Liquidity Early LifeCycle Late LifeCycle R&D_Assets R&D_Assets R&D_Assets R&D_Assets R&D_Ass ets Inst 0.0744** 0.0275 0.0713* 0.0950* 0.0537 (2.02) (0.39) (1.85) (1.73) (0.95) R&D_Assets 0.2364** -0.0375 0.1785** 0.2167** -0.0971 (2.43) (0.20) (2.12) (2.45) (0.55) q 0.0392*** 0.0431* 0.0268*** 0.0357*** 0.0415* (2.73) (1.80) (3.89) (3.77) (1.95) Debt -0.0442 0.0301 0.0395 0.1095 -0.0130 (0.29) (1.19) (0.29) (0.63) (0.28) ROA 0.1129 0.0697** -0.1460 -0.0665 0.0741 (1.21) (2.13) (1.58) (0.62) (1.15) Insider 0.9170* -0.1522 0.7406 0.6558* -0.0378 (1.72) (0.46) (1.54) (1.79) (0.07) Insider2 -1.6853** 0.4782 -1.3710** -1.2905** -0.0613 (2.15) (1.00) (2.02) (2.18) (0.08) MktCap -0.1634** -0.2276* -0.1101** -0.1351*** -0.1605 (2.49) (1.70) (2.25) (2.99) (1.43) CapEx -0.0658 0.9977* 0.0609 -0.1652 0.5296 (0.30) (1.81) (0.31) (0.45) (1.60) FCF -0.0575 -0.0324 0.0387 0.0729 -0.0562 (0.61) (0.90) (0.48) (1.02) (0.77) Liquidity 0.0000 -0.0083 0.0000 0.0000 0.0000 (0.13) (0.12) (0.05) (0.01) (0.68) LifeCycle 0.0000 0.0000 -0.0000 0.0000 -0.0004 (0.22) (0.03) (0.02) (0.21) (0.74) Revenue 0.0074 -0.0313 0.0506* 0.1379*** -0.0850 (0.21) (1.08) (1.82) (3.04) (1.45) Observations 14341 5987 8354 6759 7859 Firms 3127 1931 2276 2029 1768 Chi2 ( p -value) 0.000 0.000 0.000 0.000 0.049 J p -value 0.343 0.862 0.307 0.676 0.569 AR(2) p -value 0.610 0.129 0.958 0.189 0.213 Inst lag limits None 3 None None None R&D lag limit 3 None 3 1 None Robust z stats in parentheses, significant at 10% ; ** significant at 5%; *** significant at 1% This table reports estimates generated by differe nce GMM of changes (from year t 1 to t ) in R&D expenditures divided by assets ( R&D_Assets ). All independent variable values are changes from year t 2 to t 1. Regressions (2) and (3) include only Low and High Liquidity firms (the lowest and highest five deciles from yea r t 1), respectively. Regressions (4) and (5) include only Early and Late LifeCycle firms, respectively. Deciles are formed on a yearl y basis. J is the Hansen-Sargan test of overidentifying restr ictions. AR(2) is the ArellanoBond test of second-order autocorrelation in the er rors. Independent variables Inst and R&D_Assets are instrumented using GMM-type instrument lags. T he maximum available lags which produce a valid model are used.

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128 Table B 4: R&D and R&D Intensity (1) (2) (3) Low R&D_Assets Medium R&D_Assets High R&D_Assets R&D_Incr R&D_Incr R&D_Incr Inst 0.6061* 0.6958** 1.3596*** (1.90) (2.48) (4.41) q -0.0992* -0.1526*** -0.0467*** (1.71) (5.61) (4.22) Debt -0.1701 -0.1101 0.0520 (0.52) (0.37) (0.45) ROA 0.2177 0.8210** -0.0103 (0.50) (2.47) (0.06) Insider -0.7198 0.1148 0.8346 (1.08) (0.18) (1.15) Insider2 0.9051 -0.0948 -0.9809 (1.05) (0.11) (1.00) MktCap 0.7306*** 0.9929*** 0.7954*** (5.15) (9.19) (9.95) CapEx 0.8465 0.7958 0.8958** (1.45) (1.62) (1.97) FCF 0.0500 0.1900 0.4066*** (1.05) (1.25) (2.76) Liquidity 0.0199 0.0156 -0.0001 (0.49) (0.83) (0.16) LifeCycle 0.0036 0.0003 -0.0000 (1.50) (0.84) (0.09) Revenue 0.0361 0.1874* 0.0331 (0.26) (1.69) (0.60) Observations 4848 6044 3856 Number of Firms 828 1145 817 Pseudo R-squared 0.03 0.08 0.12 Absolute value of z statistics in parentheses significant at 10%; ** significant at 5%; *** sig nificant at 1% This table reports estimates of firm and year fix ed effect logit regressions of increases (from year t 1 to t ) in R&D expenditures ( R&D_Incr ). All independent variable values are calculated a s changes in that independent variable from year t 2 to t 1. Sample firms used in regressions (1), (2), and (3) include only Low, Medium and High R&D_Assets firms, respectively. The Low, Medium and High R&D_Assets groups include the lowest three, middle four, and highest three Liquidity deciles from year t 1, respectively. Deciles are formed on a yearly basis.

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129 Table B 5: R&D, Investment Opportunities and Free Cash Flow (GMM) (1) (2) (3) (4) Low q High q Low CashFlow High CashFlow R&D_Assets R&D_Assets R&D_Assets R&D_Assets Inst 0.0098 0.0679* 0.1319** 0.0177 (0.50) (1.84) (2.31) (1.27) R&D_Assets 0.0416 0.2505** 0.1845* 0.1843*** (0.33) (2.48) (1.93) (3.09) q 0.0271 0.0401*** 0.0404** 0.0149*** (1.44) (2.65) (2.37) (4.47) Debt 0.0574* 0.0011 0.1759 0.0745 (1.83) (0.02) (1.29) (1.17) ROA 0.2186* 0.1417* 0.0939 -0.0200 (1.72) (1.76) (1.01) (0.75) Insider -0.1224 1.0092** 0.4731 0.1663 (0.42) (2.52) (1.11) (1.10) Insider2 0.0528 -1.7979*** -1.0685 -0.2886 (0.13) (2.65) (1.49) (1.17) MktCap -0.0502* -0.1706*** -0.1673** -0.0371** (1.72) (3.11) (2.29) (2.07) CapEx 0.0911 -0.1571 0.0591 -0.0631 (0.53) (0.89) (0.27) (0.54) FCF -0.0517 -0.1097 -0.0388 -0.0034 (1.32) (1.13) (0.43) (0.37) Liquidity 0.0000 -0.0000 -0.0000 -0.0037 (0.72) (0.59) (0.68) (1.06) LifeCycle -0.0001 -0.0001 -0.0003 -0.0000 (1.08) (0.71) (0.56) (1.18) Revenue 0.0033 0.0361 -0.0223 -0.0025 (0.06) (1.27) (0.77) (0.11) Observations 5966 9045 6231 8110 Number of Firms 1867 2409 2355 2228 Chi2 ( p -value) 0.000 0.000 0.000 0.000 J p -value 0.505 0.181 0.673 0.247 AR(2) p -value 0.349 0.431 0.520 0.164 Inst lag limits None None None None R&D lag limits None None 3 3 Robust z stats in parentheses significant at 10%; ** significant at 5%; *** sig nificant at 1% This table reports estimates generated by differe nce GMM of changes (from year t 1 to t ) in R&D expenditures divided assets ( R&D_Assets ). Independent variable values are changes from year t 2 to t 1. Regressions (1) and (2) include only Low and High q firms (the lowest and highest five deciles from yea r t 1), respectively. Regressions (3) and (4) include only Low and High CashFlow firms, respectively. Deciles are formed on a yearly basis. J is the Hansen-Sargan test of overidentifying restr ictions. AR(2) is the Arellano-Bond test of second-order autocorrelation in the errors. Independent variables Inst and R&D_Assets are instrumented using GMM-type instrument lags. T he maximum available lags which produce a valid model are used

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130 Appendix C: Difference GMM Methodology Some of the robustness checks that I use in this paper u se a difference generalized method of moments (GMM) methodology that is based on the methodology employed in Holtz-Eakin, Newey and Rosen (1988). Refinements and validity tests developed in Arellano and Bond (19 91) are also used. I implement the methodology using the Stata command xta bond2 which was developed by David Roodman. This command and its prop er implementation are described in great detail by Roodman (2007). Difference GMM removes fixed effects and uses lagged val ues of the dependent variable and independent variables of inte rest as instruments. This method avoids endogeneity problems associated with usin g fixed-effects when there is autocorrelation in the dependent variable. It also corrects for any concurrent endogeneity problems associated with the inclu sion of lagged independent variables. Recently, many notable research papers have used differ ence GMM in their analysis including Gupta (2005), Cuat (2007), a nd Brown, Fazzari, and Petersen (2009). Almeida, Campello, and Galvao (2010 ) assess the performance of difference GMM and find that its results conform to theoretical expectations in regressions that use data which contains firm-fixed effects and heteroskedasticity. This difference GMM methodology is designed for use wi th panel data containing few time periods and a large number of ind ividuals or firms. My data consists of a maximum of 16 years of data for over 10,00 0 firms. Difference GMM is also designed to be implemented in situations wit h the following characteristics: a dependent variable that depends on past realizations of itself, independent variables that are not strictly exogenous, and firm fixed effects (Roodman (2007)). If conceptually and statistically sou nd instruments for endogenous independent variables are available, firmfixed effects regressions using those instruments would be preferable to using d ifference GMM. Unfortunately, I was unable to find valid instruments. Difference GMM uses lags of the endogenous regressors as instruments. The use of la gs as instruments shrinks the size of the dataset because at least one year of data has to be dropped for each firm. In my implementation of diffe rence GMM, only one year has to be dropped for each firm. The dependent variables in my regressions depend on pa st realizations because current payout policy is largely dependent on p ast payout policy and current R&D investment policy is largely dependent on p ast R&D policy. In my robustness checks that use difference GMM, the independent variables of interest are assumed to be endogenous. In fact, the mai n purpose of my difference GMM robustness checks is to control for the pote ntial (and likely) endogenous relationship between payout policy and inst itutional ownership or between R&D investment policy and institutional ownersh ip. My implementation of difference GMM starts with the f ollowing basic model which will be transformed by the difference GMM process.

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131 (C-1) it i it it it it Control Inst Policy Policy 1 1 1 In this model, Policy it represents the change in the firm policy that I am using as a dependent variable in a given regression: ei ther payout, repurchases, payout composition or R&D to assets. Policy it-1 represents the change in that firm policy in the previous year. The independent variable Inst i t-1 represents the change in institutional ownership percentage in the pre vious year. Control i t-1 represents a vector of time-varying firm level control variables. Year dummies are included as control variables to remove time-related sho cks that affect all firms. Firm-specific (fixed effects) errors are represented by i and it represents a timevarying observation-specific error term. Several econometric problems which are endemic to mode l C-1 can be corrected by difference GMM. The change in institution al ownership percentage ( Inst i t-1 ) is assumed to be endogenous. Therefore, it is instrume nted with lagged changes in institutional ownership. This predetermines t he institutional ownership variable thus rendering it uncorrelated with the err or term. Similarly, the use of the lagged dependent variable ( Policy it-1 ) as an independent variable leads to autocorrelation. This variable is also instrumented w ith lags of itself. Firm-fixed effects are contained in the error term i The difference GMM methodology uses first-differences to transform model C-1 thus removing the firm-fixed effects error term because it is time invariant. The new model is show n below. (C-2) it it it it it Control Inst Policy Policy 1 1 1 The transformed model addresses potential causation an d endogeneity problems that may exist in the relationship between th e policy and institutional ownership. Firm-fixed effects are differenced out. Inst itutional ownership changes predate policy changes indicating causation. Previous pol icy changes are controlled for decreasing the probability that coefficie nts for changes in institutional ownership are simply a result of previous policy changes. Potentially endogenous independent variables are instrumented to control for endogeneity. I was able to use the first lag of independent policy and institutional variables in all my regressions as an instrument. In the difference GMM model, efficiency can be improved by including additional lags. Including the additional lags introduces new information which is useful to the m odel. In conventional two-stage least squares regressions, including additional l ags shrinks the sample size which means additional efficiency comes at a steep cost. Difference GMM does not suffer from this trade-off. In difference GMM additional lags can be included as instruments when available without shrinking the sample size. Therefore, it is generally preferable to include as m any lags as instruments as possible. I use this tactic. Unfortunately, the inclusion of additional lags as inst ruments is not problem-free. Too many instruments can result in overid entification of the model invalidating its results. Therefore, if tests indicate th at a model is overidentified, I

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132 reduce the number of lags used until the tests no longer indicate that the model is overidentified. I employ two important tests of difference GMM model validity which are strongly recommended by Roodman (2007) among others: th e Hansen-Sargan J -test and the Arellano-Bond test for second-order autoco rrelation in differenced residuals. For both tests, a higher p -value indicates a valid model while p -values of less than 0.10 indicate an invalid model. The null hypothesis of the Hansen-Sargan J -test is that the instruments as a group are exogenous. A rejection of this null hypoth esis indicates an invalid model. Therefore, I do not use any model in which th e p -value for the J -test is less than 0.10. The J statistic’s ability to detect overidentification can b e weakened by too many instruments. A general rule of thumb is that the number of firms in the panel should outnumber the number of instruments used i n a difference GMM regression. The minimum number of firms for any regressi on I run is 1,489 while the maximum number of instruments is 208 indicating tha t the J statistic should retain its ability to detect overidentification in all of these regressions. AR(1) autocorrelation in differenced residuals is exp ected. This is because the difference between an error term ( it ) and the error term from the year before ( it-1 ) is expected to be related to the difference between the error term from the year before ( it-1 ) and the error term from two years before ( it-2 ) because both differences contain the error term from the year befo re ( it-1 ). The Arellano-Bond test for second-order autocorrelation is more important because AR(2) autocorrelation indicates an invalid model. The null hypothesis is that there is no autocorrelation. Therefore, I do not use any models in which the null is rejected at the 10% level.

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About the Author Ricky W. Scott has earned a B.A. Degree in Computer Scien ce from the University of Georgia, a Masters in Project Management from Keller Graduate School, and a Masters in Finance from Georgia State Un iversity. He labored for over 15 years as a computer systems analyst at BellSout h, The Home Depot, and Publix Super Markets and served four honorable ye ars in the U.S. Army. He has accepted an Assistant Professor position at Saint Leo University.