Are all outside directors created equal with respect to firm disclosure policy?

Abstract

Empirical evidence on the association between outside directors and firms’ voluntary disclosures is mixed and controversial. We hypothesize that the outside directors do not represent a homogeneous group of people as considered in the literature. Using hand-collected data from a sample of biotechnology firms, we find that the aforesaid association differs based on the directors’ professional backgrounds. Our results are consistent with two ideas. First, an outside director’s influence on firm disclosure policy is shaped by her professional background. Second, firms match outside directors’ professional backgrounds with their disclosure policy. We cannot distinguish between the two explanations. Yet, we make an important contribution to the literature. We show that the impact and the selection prospects of outside directors are not as uniform as previously considered in the literature. Thus, the researchers examining financial disclosures must take into account the background characteristics of all outside directors, not just of those in the audit committee. And investor bodies must consider the background characteristics of candidates in their recommendation for outside-director selection.

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Notes

  1. 1.

    See Jensen and Meckling (1976), Fama (1980), Williamson (1981), and Lambert (2001).

  2. 2.

    Prior studies find both negative and positive association between the proportion of outside directors and voluntary disclosures (Forker 1992; Adams and Hossain 1998; Eng and Mak 2003; Gul and Leung 2004; Cheng and Courtenay 2006; Barako et al. 2006; Leung and Horwitz 2004; Cerbioni and Parbonetti 2007; Li et al. 2008).

  3. 3.

    This product classification is consistent with the US Food and Drug Administration (FDA) classification. A successful drug goes through three phases of clinical trials (on humans), after the efficacy of the compound is assessed in the preclinical stage. The probability of a chemical compound making it through from discovery to final commercialization is assessed at about one in five thousand (Healy et al. 2002).

  4. 4.

    Larcker and Rusticus (2010, p. 187) assert: "When the instrument is only weakly correlated with the regressor, IV methods can produce highly biased estimates when the instrumental variable is even slightly endogenous. In those cases, it is likely that IV estimates are more biased and more likely to provide the wrong statistical inference than simple OLS estimates that make no correction for endogeneity".

  5. 5.

    See Guo et al. (2004; biotechnology firms), Rajgopal and Shevlin (2002; oil and gas sector), and Srivastava (2014; software industry).

  6. 6.

    For a complete review of literature on financial reporting environment, see Beyer et al. (2010).

  7. 7.

    Liu et al. (2019a), DeBoskey et al. (2019), Huang et al. (2016), Lu and Boateng (2018), Ting-Kai and Hsuan-Ling (2018), Jaggi et al. (2018), Mitra et al. (2019), Liu et al. (2019b), Tosun et al. (2019), Lu and Boateng (2018), Belghitar et al. (2016), Cao et al. (2018), Faleye et al. (2018), Boateng et al. (2017), Wang et al. (2017), Lai and Tam (2017), Chan et al. (2017), Sokolyk (2015), Yu et al. (2015), Cao et al. (2015), Ge and Kim (2014), Wang (2012), Leung and Horwitz (2010), Cheng et al. (2008), Fich and Slezak (2008).

  8. 8.

    We find similar results after controlling for discretionary accruals (Jones 1991; Kothari et al. 2005).

  9. 9.

    We admit that these p values are smaller than the conventional cut offs of 0.10. However, ours is a small sample study that uses firm-fixed effects in addition to multiple control variables.

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Acknowledgements

This paper is based on one of the three essays for the completion of Enache’s doctoral dissertation at University of Padua. We thank the editor, Cheng-Few Lee, two anonymous reviewers, Vasiliki Athanasakou, David Emanuel, Fabrizio Ferri, Ferdinand Gul, Igor Goncharov, Jae B. Kim, Paul Laux, Baruch Lev, Brian Rountree, Seyed Mehdian, Stephen Penman, Raffaela Santolini, Ana Simpson, Tony van Zijl, Anne Wyatt, Hila Fogel-Yaari and seminar participants at the 2018 Hawaii Accounting Research Conference, 2012 American Accounting Association annual meeting, 36th European Accounting Association Annual Meeting, 2013 Accounting and Finance Conference, Ninth Workshop on Corporate Governance, University of Michigan—Flint, Victoria University of Wellington, London School of Economics, Massey University, University of Padua, University of Calgary and Eighth Workshop on Visualising, Measuring Intangibles, and Intellectual Capital for their very valuable comments. This paper received the 2012 European Institute for Advanced Studies in Management Best Junior Contribution to Intangibles Literature Theory and Practice Award—Mention d’Honeur. Enache and Srivastava acknowledge funding received from Social Sciences and Humanities Research Council for the project “Final Disclosure and Corporate Governance.” Enache also acknowledges funding received from Social Sciences and Humanities Research Council for the project “Board Composition and Financial Analysts’ Earnings Forecasts in the Biotech Sector.” In addition, Srivastava acknowledges the financial support from Daniel R. Revers T’89 Faculty Fellowship at Tuck School of Business, Dartmouth College, Canada Research Chair program of the Government of Canada, and Haskayne School of Business, University of Calgary. We are thankful to authors of Enache and Hussainey (2019) for sharing data on disclosure index.

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Appendices

Appendix 1

Classification of outside directors (following Enache and Garcia-Meca 2019).

Following are excerpts of biographies of outside board members for each of their three categories: business experts, support specialists, and community influentials (Hillman et al. 2000).

  1. 1.

    Business experts are current or past senior officers of other companies, and they act as a sounding board for the formulation and implementation of high-level strategies. They are likely to partake in the formulation, and overseeing the implementation, of firms’ strategic decisions.

    • Gordon Binder (Acadia Pharmaceuticals): “Has served as a director of our company since June 2003. Mr. Binder is the founder and managing director of Coastview Capital. Mr. Binder was the chief executive officer of Amgen, the world’s largest biotechnology company, from 1988 through 2000. During his tenure as chief executive officer, Amgen grew from four hundred employees to rank within the top 20 pharmaceutical companies in worldwide revenues, the top 15 in United States sales, and the top ten in market capitalization. Mr. Binder serves on the boards of the Massachusetts Institute of Technology, the California Institute of Technology, and the American Enterprise Institute for Public Policy Research.”

    • Christopher S. Henney (Xcyte): “Dr. Henney had served as one of Xcyte’s directors since March 2005, and continued on as Vice Chairman of the Company. Previously, Dr. Henney co-founded three major publicly held U.S. biotechnology companies, Immunex, ICOS and Dendreon, and held a seat on the board of directors and executive positions at each company. From 1995 to January 2003, Dr. Henney was Chairman and Chief Executive Officer of Dendreon Corporation. Dr. Henney currently serves as the Chairman of Oncothyreon, Inc. and Anthera Pharmaceuticals, Inc. and on the board of directors of AVI BioPharma, Inc. Dr. Henney received a Ph.D. in experimental pathology from the University of Birmingham and a D.Sc. from the same university for contributions to the field of immunology.”

  2. 2.

    Support specialists are experts in functional areas and could counsel managers to improve firms’ financial, legal, and commercial transactions. For example,

    • Franklin P. Johnson (Amgen): “Mr. Johnson is the General Partner of Asset Management Partners, a venture capital limited partnership, and has been a private venture capital investor for more than five years. He received his B.S. in Mechanical. From Stanford University. Mr. Johnson is the General Partner of Asset Management Partners, a venture capital limited partnership, and has been a private venture capital investor for more than five years.”

    • David Tendler (Savient). “Mr. Tendler has been a director since 1994 and is a member of our audit and finance and nominating and corporate governance committees. Since 1985, Mr. Tendler has been a Partner of Tendler Beretz L.L.C., an international consulting firm. From 1994 to 2005, Mr. Tendler was a director of V.I. Technologies, Inc., a biotechnology products company. From 1980 to 1985, Mr. Tendler was Co-Chairman and Chief Executive Officer of Phibro-Salomon, Inc., an international commodity trading and investment banking company that is now a part of Citigroup.”

    • Carl E. Kaplan (Savient): “Mr. Kaplan has been a Senior Partner of the law firm Fulbright & Jaworski L.L.P. since 1989 when it merged with Reavis & McGrath. Prior to the merger, he was a Partner of Reavis & McGrath. Fulbright & Jaworski rendered legal services to Savient.”

  3. 3.

    Community influentials lack senior management and general business experience, but they bring their social capital to the organization. They provide non-business perspectives on issues, problems and ideas and enhance firms’ linkages with outside institutions.

    • Faye Wattleton (Savient): “Ms. Wattleton has been a director since 1997. She is the chairperson of our nominating and corporate governance committee and a member of our audit and finance committee. Ms. Wattleton is a director of Quidel Corporation, a medical diagnostics company. Since 1995, Ms. Wattleton has been President of the Center for the Advancement of Women, a non-profit corporation. From 1978 to 1992, Ms. Wattelton was President of Planned Parenthood Federation of America, Inc. (New York).”

    • Joseph L. Bower (Anika Therapeutics): “Dr. Bower joined the Board of Directors of Anika Therapeutics in February 1993 and has served as Lead Director since April 2005. He has held various positions at the Harvard Business School since 1963, where he was named Professor of Business Administration in 1972 and Donald Kirk David Professor of Business Administration in 1986. He has served as Chairman of the Doctoral Programs, Director of Research, Senior Associate Dean for External Relations, Chair of the General Management Area and is currently Chair of the General Manager Program. Dr. Bower received an A.B. from Harvard University and an M.B.A. and a D.B.A. from the Harvard Business School. He is a director of Brown Shoe Company, Inc., New America High Income Fund, Sonesta International Hotels Corporation, Loews Corporation and TH Lee Putnam EOP.”

    • Ann M. Veneman. (Alexion Pharmaceuticals, Inc): “Ann M. Veneman, J.D. has been a director of Alexion since May 2010. She was most recently the Executive Director of UNICEF, a position she held from her appointment by the United Nations Secretary General in May 2005 until April 2010. As Executive Director, Ms. Veneman worked on behalf of the United Nations children’s agency to help children around the world by advocating for and protecting their rights. Ms. Veneman was responsible for more than 11,000 UNICEF staff members in more than 150 countries. Prior to joining UNICEF, Ms. Veneman served as Secretary of the U.S. Department of Agriculture, or USDA, from January 2001 until January 2005. From 1986 until 1993, she served in various positions at the USDA, including Deputy Secretary, Deputy Undersecretary for International Affairs and Commodity Programs, and Associate Administer of the Foreign Agricultural Service. From 1995 until 1999, Ms. Veneman served as Secretary of the California Department of Food and Agriculture. Ms. Veneman has also practiced law in Washington, DC and California in both the private and public sectors. Ms. Veneman received a B.A. from the University of California, Davis, a Master’s degree in Public Policy from the University of California, Berkeley, and a J.D. from the University of California, Hastings College of Law.”

Appendix 2: Stages in the development of a new biotechnology product (drug)

Drug development refers to a series of processes that are followed before a drug is brought to market. It is complex, expensive, and spread over ten to 12 years (Babiarz 2008). The Food and Drug Administration (FDA) establishes the guidelines for drug development. Based on these guidelines, the processes can be divided into two phases: preclinical and clinical.

In the preclinical phase, after a chemical compound is discovered that can potentially treat a disease, its chemical makeup, stability, and solubility are assessed. Before testing the compound on humans, its safety, toxicity, pharmacokinetics, and metabolism also are considered. Furthermore, an assessment is made for the dosage and schedule of its administration. Tests are conducted using in vitro methods (e.g., with isolated cells) or with laboratory animals. The company submits to the FDA the results of the preclinical testing and the proposed plan for clinical testing. If the FDA approves the plan, then the company files an investigational new drug (IND) application for human testing.

In the clinical phase, testing has three phases. In Phase I, the drug is tested on 20–80 healthy volunteers to assess its side effects and how the drug is metabolized and excreted. In Phase II, the drug is tested on 50–300 patients to assess the effectiveness and its short-term side effects. In Phase III, the safety and effectiveness of the drug is assessed on up to 3000 patients using different dosages and in combination with other drugs. Thereafter, the company submits the test results as well as the proposed manufacturing process to the FDA to seek approval for marketing the new drug.

Appendix 3: Product disclosure index (following Enache and Hussainey 2019; Appendix C)

The disclosure index is constructed for each biotechnology product by hand-collecting relevant information from annual reports (Business section of Form 10-K), following Guo et al. (2004). Data are obtained with request from and Enache and Hussainey (2019) and supplemented with additional hand collection. Information is derived for the following five categories: product specifications, target disease, clinical trials, future development plans, and market information. The procedure for assigning scores in each category is tabulated (with a detailed example) in “An example of the measurement of product disclosure index” section of “Appendix 3”.

Measurement of product disclosure index

I. Product specifications
1. How does the product work? (3 points = three sentences; 2 = two sentences; 1 = one sentence; 0 = none)
2a. Why is it better than previous products? (2 = name mentioned; 1 = no name mentioned; 0 = no discussion)
2b. Why is it better than competing products? (2 = name mentioned; 1 = no name mentioned; 0 = no discussion)
3. What is the chemical/biological structure? (2 = chemical compound; 1 = general discussion; 0 = not mentioned)
Subtotal I = total scores of (1 + max(2a, 2b) + 3)
II. Target disease
1. What kind of diseases does the product treat? (2 = disease name mentioned; 1 = disease name not mentioned; 0 = no discussion)
2. What are other possible uses of the drug? (2 = disease name mentioned; 1 = disease name not mentioned; 0 = no discussion)
Subtotal II = total scores of (1 + 2)
III. Clinical trials
1. Number of patients (1 = given; 0 = absent)
2. Patients information (with what diseases) (1 = given; 0 = absent)
3. Doses (amounts) used in the clinical trial (1 = given; 0 = absent)
4. Method used in the clinical trial (1 = given; 0 = absent)
5. Treatment schedule (duration or frequency) (1 = given; 0 = absent)
6. Trial results [detailed = pro and cons + numbers (3); general = numbers (2); brief = no numbers (1); none (0)]
Subtotal III = total scores of (1 + 2 + 3 + 4 + 5 + 6)
IV. Future plans
1a. Is there any plan to try the product on new diseases? (2 = disease name mentioned; 1 = no name mentioned; 0 = no discussion)
1b. Is there any plan to try the product with other products? (2 = name mentioned; 1 = no name mentioned; 0 = not mentioned)
2. Future plan for clinical trials
2a. Planned date (1 = mentioned; 0 = not mentioned)
2b. Number of patients for the planned trial (1 = mentioned; 0 = not mentioned)
2c. Patient information for the planned trial (what disease) (1 = mentioned; 0 = not mentioned)
2d. Duration (1 = mentioned; 0 = not mentioned)
2e. Method (1 = mentioned; 0 = not mentioned)
3. Possible alliance (2 = name mentioned; 1 = no name mentioned; 0 = not mentioned)
Subtotal IV = total scores of (max(1a, 1b) + 2a + 2b + 2c + 2d + 2e + 3)
V. Market information
1. Number of patients affected by the disease (1 = mentioned; 0 = not mentioned)
2. Number of incidents (market size) (1 = mentioned; 0 = not mentioned)
Subtotal V = total scores of scores (1 + 2)
Overall disclosure score = sum of Subtotals I–V
Scaled disclosure score = overall disclosure score divided by 30 for products either in or beyond the clinical trials phase; by 22 for the products that did not reach clinical trials

An example of the measurement of product disclosure index

Company MAXYGEN
Product MAXY-G34
Development stage Phase II
Disclosure index (information is drawn from the Business section, Part I, of Form 10-K) Score contents
I. Product specifications  
1. How does the product work? (3 = three sentences; 2 = two sentences; 1 = one sentence; 0 = none) 1. Helps the body make blood cells.
2a. Why is it better than previous products? (2 = name mentioned; 1 = no name mentioned; 0 = not mentioned) 2. MAXY-G34 reduces the duration of neutropenia when compared with the currently marketed products (Neulasta and Neupogen).
2b. Why is it better than competing products? (2 = name mentioned; 1 = no name mentioned; 0 = not mentioned) 2. MAXY-G34 protects patients from chemotherapy and radiation therapy–related infections, shortens the duration of hospital stays, and helps keep patients on schedule for their cancer treatments.
3. What is the chemical structure in addition to its chemical name? (2 = name mentioned; 0 = not mentioned) 0. Not mentioned.
Subtotal I = total scores of (1 + max (2a, 2b) + 3) 3, out of a maximum of 7.
II. Target diseases  
1. What kind of diseases does the product treat? (2 = disease name mentioned; 1 = disease name not mentioned; 0 = not mentioned) 2. Neutropenia.
2. What are the other possible uses? (2 = disease name mentioned; 1 = disease name not mentioned; 0 = not mentioned) 0. Not mentioned.
Subtotal II = total scores of (1 + 2) 2, out of a maximum of 4.
III. Clinical trials  
1. Number of patients (1 = mentioned; 0 = not mentioned) 1. 47
2. Patients information (with what disease) (1 = name mentioned; 0 = not mentioned) 1. Patients with breast cancer who have failed at least one potentially curative treatment regimen.
3. Doses (amounts) used in the clinical trial (1 = mentioned; 0 = not mentioned) 1. 5 to 100 µg/kg was given.
4. Method (via what kind of media) used in the clinical trial (1 = mentioned; 0 = not mentioned) 1. Subcutaneous injection.
5. Treatment schedule (duration or frequency) (1 = given; 0 = absent) 1. Single dose MAXY-G34 therapy being administered per three-week chemotherapy cycle with each patient receiving six cycles of docetaxel.
6. Results (3 = detailed discussion; 2 = general discussion; 1 = brief discussion; 0 = no discussion) 2. Results of the Phase I clinical trial indicate that the drug MAXY-G34 was generally safe and well tolerated through the study.
Subtotal III = total scores of (1 + 2 + 3 + 4 + 5 + 6) 7, out of a maximum of 8.
IV. Future development plans  
1a. Is there any plan to try the product on new diseases? (2 = name mentioned; 1 = no name mentioned; 0 = not mentioned) 2. Hemophilia.
1b. Is there any plan to try the product with other products? (2 = name mentioned; 1 = no name mentioned; 0 = not mentioned) 0. Not mentioned.
2. Future plan for clinical trials  
2a. Planned date (1 = mentioned; 0 = not mentioned) 1. 2008.
2b. Number of patients for the planned trial (what disease) (1 = mentioned; 0 = not mentioned) 0. Not mentioned.
2c. Patient information for the planned trial (what disease) (1 = mentioned; 0 = not mentioned) 1. Breast cancer patients.
2d. Duration (1 = mentioned; 0 = not mentioned) 0. Not mentioned.
2e. Method (1 = mentioned; 0 = not mentioned) 0. Not mentioned.
3. Alliance (2 = name mentioned; 1 = no name mentioned; 0 = not mentioned) 2. Entered into a strategic alliance with Roche.
Subtotal IV = total scores of [max (1a, 1b) + 2a + 2b + 2c + 2d + 2e + 3) 6, out of a maximum of 9.
V. Market information  
1. Number of patients affected by the disease (1 = mentioned; 0 = not mentioned) 0. Not mentioned.
2. Number of incidents (market size) (1 = mentioned; 0 = not mentioned) 0. Not mentioned.
Subtotal V = total scores of (1 + 2) 0, out of a maximum of 2.
Overall disclosure score = sum of Subtotals I–V 18, out of a maximum of 30.
Scaled disclosure score = overall disclosure score divided by 30 because MAXY-G34 is in clinical trials phase 0.60, out of a maximum of 1.00.

Appendix 4

See Table 7.

Table 7 Definitions of variables

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Enache, L., Parbonetti, A. & Srivastava, A. Are all outside directors created equal with respect to firm disclosure policy?. Rev Quant Finan Acc 55, 541–577 (2020). https://doi.org/10.1007/s11156-019-00852-1

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Keywords

  • Biotechnology firms
  • Corporate governance
  • Voluntary disclosures
  • Proprietary costs
  • Outside directors

JEL Classification

  • M40
  • M41
  • G14
  • G32
  • G34