11 September, 2016

PROFESSIONAL EXPERTISE ON BOARDS,

CORPORATE LIFECYCLE, AND FIRM PERFORMANCE

by

Attila Balogh 1

The University of Sydney Business School

Abstract

This study demonstrates that suitable professional expertise on corporate boards can have a

significant impact on firm outcomes. We examine diversity of expertise on boards, its link

to shareholder value, and extend the literature by introducing corporate lifecycle and

industry sectors to explore when specific types of expertise matter. Exploring dominant

cash flow patterns, we find a strong link between firm value and financial, mining and

engineering expertise of early stage firm boards across ASX-listed companies in 2014. We

also find a relationship between firm performance and financial, mining, and other

unclassified board expertise for companies in the shake-out stage.

JEL Classification: G32, G34, M40

Keywords: Corporate governance, Board of directors, Professional expertise, Life-cycle

theory

1 Discipline of Accounting, The University of Sydney Business School, Codrington Building (H69), Darlington NSW 2006, Australia; Email: attila.balogh@sydney.edu.au

1 Introduction

Is there a relationship between firm performance and the range of expertise that directors

bring to corporate boards? Whilst previous studies have shown no overall or cross-

sectional link between general board composition and firm value, there is evidence in the

literature that taking a more granular view of how boards are structured and operate can

uncover structures that enhance company performance and value. Early research suggests

a significant relationship for companies experiencing major events such as takeovers and

CEO turnover (Brickley, et al., 1994, Byrd and Hickman, 1992, Kosnik, 1987, Rosenstein

and Wyatt, 1990, Weisbach, 1988). Studies examining more nuanced characteristics such

as board committee composition (Klein, 1998) and industry experience on specific

subcommittees (Wang, et al., 2013) have found that independent directors enhance value

on audit and compensation committees. The domain of professional expertise was

highlighted by Anderson et al. (2011) who in examining occupational heterogeneity on

boards discovered that investors value diverse talents and perspectives that directors bring

to their monitoring and advising duties. Their study suggests that whilst professional

diversity introduces greater coordination problems and communication challenges, they

are counterbalanced by improved problem-solving, strategy formulation and resource

utilisation. In Australia, prior research has focused on the presence of accounting expertise

on boards with Aldamen et al. (2012) finding a positive relationship between accounting

expertise on audit committees and market performance during the 2007-08 global financial

crisis.

This analysis contributes to the existing literature in a number of ways. First, it will use a

sample of ASX listed companies from 2014. Previous Australian studies on expertise

diversity and firm performance use data from periods prior to the second and third editions

of the Australian Stock Exchange (ASX) Corporate Governance Principles and

Recommendations (Christensen, et al., 2010, Cotter and Silvester, 2003, Gray and

Nowland, 2015). The new edition was published in March 2014 and the guidance to

disclose and discuss the mix of skills and diversity for the board by using a skills matrix

was changed from commentary to a specific recommendation. This development

represents a move towards a more prescriptive approach with a view to increasing

accountability towards investors, but also in order to identify gaps in the board’s combined

skillset.

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Second, this study adds further granularity to Gray and Nowland’s (2015) study on

diversity of expertise on ASX boards. It examined whether greater diversity enhances

shareholder value by using various expertise diversity indices and finding that shareholders

benefit from expertise diversity on boards only within a subset of specialist business

expertise. This analysis suggests that companies in different industries may benefit from

different sets of skills and examines the relationship between firm value and the existence

of specific skills on the board given the company’s industry sector or stage in corporate

lifecycle.

Third, this study introduces the concept of corporate lifecycle in examining board diversity

of expertise as it relates to firm value. It is likely that companies will require a different

board skillset depending on whether they are experiencing a steady growth phase or

operate a mature business. This study will examine the relationship between firm value or

firm performance and the existence of specific skills on the board, given the company’s

phase in its corporate lifecycle.

In contrast to the Sarbanes-Oxley Act of 2002 in the United States, the ASX Corporate

Governance Principles and Recommendations are not mandatory for listed companies in

Australia. Accordingly, there is likely to be more variation in governance structures

because companies can voluntarily select the recommendations they adhere to. This study

will provide critical input to boards of directors and help them better understand the

implications of adopting specific recommendations and deviating from others.

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2 Related literature

The past three decades have seen a steadily increasing focus on corporate governance by

practitioners and likely as a result of this interest a consistently growing volume of

academic research. The unique role of the board of directors in a corporate governance

context was identified early on, as this is the group bearing ultimate responsibility in the

system of internal controls and setting the rules of the game for the CEO (Jensen, 1993).

Codes governing the code of behaviour emerged in a number of developed countries

including Australia: the ASX Corporate Governance Council was established in August

2002, the first edition of its Principles of Good Corporate Governance and Best Practice

Recommendations was published in March 2003 with the third edition released eleven

years later in 2014.

The remit of the board of directors can be encapsulated in four key areas this study will

refer to as the Four Cs: control, counsel, connections and compliance, each underpinned by

complementary and at times competing theories that have deep roots in the corporate

governance literature (Mallin, 2010, Monks and Minow, 2011). The first two functions

focus on internal activities of monitoring management on behalf of shareholders (control),

and providing mentoring (counsel). The second two emphasise the external roles that

boards perform: offering external linkages (connections) and ensuring adherence to laws

and regulations (compliance).

2.1 Agency theory – exercising control

The board’s role in exercising control and providing monitoring over management is most

commonly evaluated through agency theory. A principal-agent problem is inherent when

ownership and control of assets are separated and shareholders are unable to affect

practical control over management: the board’s main role is to oversee management,

safeguard shareholder rights and ensure their equitable treatment (Fama and Jensen, 1983,

Jensen and Meckling, 1976). This conflict also encompasses asymmetric information and

potential moral hazard, where the agent (director) has better information and possesses the

trust of the principal and hence is in a position to act contrary to the interest of the

principal.

The contrasting stewardship theory posits that managers are inherently honest and driven

by maintaining and enhancing their professional reputation. Accordingly, the theory

suggests that managers will not engage in opportunistic or self-enriching behaviour to the

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detriment of the company or their own reputation; they are good agents maximising

shareholder value and any further monitoring is unnecessary and increases transaction

cost.

While many of the potential problems may be overcome through contract, it is not feasible

to negotiate and enforce a comprehensive agreement for every potential scenario. A better

solution is to develop overarching mechanisms in order to govern stakeholder relationships

and minimise conflicts; this is the domain of corporate governance.

2.2 Resource dependency theory - providing counsel and delivering connections

Companies operate in an ecosystem. Whilst the basic resources of capital, labour, and raw

material are still important, their success increasingly depends on more nuanced ones.

Strategic relationships with other businesses, regulators and the government are just as

critical and so is the quality of their labour force in terms of their connectedness in the

community. Resource dependence theory captures at least two of the four critical elements

identified as key responsibilities of directors. Companies benefit from their professional

expertise as they provide counsel and mentoring to management and outside directors can

offer connections by delivering essential linkages through their professional networks

across industry, regulators and government (Hillman, et al., 2000, Pfeffer and Salancik,

2003). Experienced professionals on boards can offer their guidance and facilitate

relationships with providers of a wide array of external capabilities that can help

companies succeed. As an additional facet of the resource dependency theory’s practical

application, companies also benefit from an enhanced reputation when well-regarded

directors join the board (Pfeffer, 1972).

2.3 Corporate Law – ensuring compliance

Directors oversee management on behalf of shareholders with a view to maximising

shareholder value whilst giving due regard to the environment, fair trading, operational

health and safety matters, legal issues and the economic environment. Given that the

conduct of the board of directors and management have an impact on such wide ranging

matters, the freedom given to this stakeholder group needs to be balanced with the need for

holding them accountable as they discharge the duties of their office. As a corporate

governance tool, law has developed over time to impose standards on company directors

and best practice recommendations have been developed to incentivise companies to

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comply, with the promise of increased shareholder wealth as a reward (Agrawal and

Knoebler, 1996).

2.4 Board heterogeneity and fit for purpose

Both the agency theory and resource dependency perspectives suggest that more diverse

boards would lead to desired outcomes such as increased shareholder wealth and firm

performance. Varied backgrounds – be it ethnicity, gender, age or expertise – bring fresh

perspectives that may not have been considered by more homogenous boards (Carter, et

al., 2003). Directors that are less connected to the CEO and top management would be

expected to be stronger monitors. More and tougher monitoring, however, may be

suboptimal given the specific company, leading to the notion that boards need to be fit for

purpose and that different stages in corporate lifecycle and different industries may all

need different boards. Accordingly, treating board structure and diversity measures as

either independent or dependent variables can both lead to valid lines of enquiry.

2.5 Taxonomy of Board Structure and Composition

The domain of board diversity typically examines various aspects of board structure and

composition to identify a link between the diversity measure as independent variable and

measures of firm performance. The market performance measure classically used is

Tobin’s Q, and the accounting measure typically investigated is return on assets (ROA).

The overall aim of these studies is to uncover a model board structure that leads to higher

firm value and improved company performance.

Board structure literature has primarily investigated aspects of board size, board

composition and internal dynamics. The common element across these studies is the focus

on a subset of firms sharing similar characteristics. Eisenberg, Sundgren and Wells (1998)

directed their attention to small and mid-sized firms in Finland to find a negative link

between board size and profitability, while Yermack (1996) found a similar conclusion for

US industrial corporations; Alvarez, Anson and Mendez (1997) studied listed Spanish

firms and concluded a non-linear relationship. Hunter (1997) investigated rural electricity

distributors in the US and showed that large boards have an adverse impact on firm

efficiency. In looking at board composition and investigating director independence

Baysinger and Butler (1985) find a mild, lagged effect on organisational performance for

266 major US companies. Further studies in this area and those examining the internal

dynamics of boards find no cross-sectional relationship between the board feature in

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question and firm performance when looking at large, heterogeneous samples (Baysinger

and Butler, 1985, De Andres, et al., 2005, Hermalin and Weisbach, 1988, Hermalin and

Weisbach, 1991, John and Senbet, 1998, Klein, 1998, Rosenstein and Wyatt, 1997,

Rosenstein and Wyatt, 1990, Vafeas, 1999, Weisbach, 1988).

Aspects of board composition that have been linked to company value include directors’

gender, age, entrenchment status, professional background, expertise and their affiliation

to the company (Alves, et al., 2015, Christensen, et al., 2015, Klein, 1998). It has been

suggested that inside directors (current employees), grey directors (affiliated non-

employees) or independent directors (unaffiliated non-employees) may play a different

role and have an impact on shareholder wealth (Faleye, 2015). Further, outsiders can be

classified based on their background as either being corporate, financial or neutral

outsiders (Rosenstein and Wyatt, 1990) which in turn may prove relevant in terms of how

they add value.

2.6 Board Diversity of Expertise

Research studies on the professional background and expertise of directors have been

limited to date. Gray and Nowland (2015) provide a summary of the Australian research

that has focused on specific types of expertise such as accounting and political background

(Aldamen, et al., 2012, Christensen, et al., 2010, Gray, et al., 2016). They also provide the

first comprehensive categorisation of director expertise and identify 11 distinct groups:

academics, accountants, bankers, consultants, doctors, engineers, executives, lawyers,

other CEOs, politicians and scientists. Their study found that diversity of expertise on

boards had no overall impact on firm performance, but a negative relationship was found

between non-business related expertise and firm performance as measured ROA.

International studies that look at the background of board members provide critical insight

into some of the underlying reasons behind director appointments and make inferences

about the inner working of boards based on the skillset of its members. Agrawal and

Knoeber (2001) point to the prevalence of directors with backgrounds in politics for

companies with significant government contracts; qualifications in law for firms where

environmental regulation is higher, while Fich (2005) documents positive market response

to the appointment of successful CEOs of other companies. Güner, Malmendier and Tate

(2008) investigate the presence of financial expertise on boards and the resulting increased

external funding, but find that it does not necessarily benefit shareholders.

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2.7 Linking Boards to Firm Performance

The relationship between the composition of the board of directors and firm performance

has been the subject of numerous studies over the past three decades and the only

consistent conclusion has been the lack of compelling evidence for any overall or cross-

sectional link. According to Hermalin and Weisbach (1991) and Bhagat and Black (2002)

there is no compelling relationship between board composition and firm performance by

purely looking at the balance of inside and outside directors. Research conducted by

Agrawal and Knoebler (1996) suggests that there is a negative relationship between firm

performance and the percentage of outsiders on boards and concludes that board structures

are suboptimal because they are determined internally by shareholders. These outcomes

suggest taking a more granular view of directorships – ‘adding structure’ as suggested by

Klein (1998) – and examining the roles directors play by going beyond merely classifying

directors as insiders and outsiders. A more in-depth analysis could be undertaken by

examining memberships of board subcommittees, attendance records and the professional

expertise of board members serving on subcommittees.

Klein (1998) investigates the role of the insider director by observing their committee

membership. He posits that an inside directors’ activity are more consistent with profit

maximising behaviour when they fall within domains of advising and strategy, such as

participating in the work of a long-term investment committee as opposed to serving on a

monitoring committee such as the executive remuneration committee. Consistent with this

theory, Klein (1998) finds a strong positive link between inside directors on finance and

investment subcommittees and measures of both stock market and accounting

performance.

2.8 Gap in Knowledge

There is a growing volume of literature studying board composition and linking it to

shareholder wealth. This study builds on recent work on board structure, diversity of

expertise and director selection to apply it in the context corporate lifecycles. The

Sarbanes-Oxley Act 2002, the UK Corporate Governance Code 2014 and the ASX

Corporate Governance Principles and Recommendations 2014 all promote the notion of

independent directors on boards. Literature, however, suggests that truly independent

boards may miss out on expertise contributed by internal directors and those with

technology know-how possessing firm-specific information and deeper insight into the

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company’s operations, which would in turn make them both better monitors and advisors

(Fama and Jensen, 1983). This study will investigate whether firm-specific information

and deeper insight offered by professionals with domain specific expertise are important

for companies depending on their corporate lifecycle phase.

The study will introduce the concept of corporate lifecycle and its interaction with board

expertise as it relates to firm value and firm performance. It is likely that companies will

require a different board skillset in different industries and stages of corporate lifecycle.

This study will examine the relationship between the existence of specific skills on the

board and firm value given the company’s industry and lifecycle.

In an extreme view, no two firms are exactly the same and hence each of them will have its

own unique optimal board structure and composition. This study will attempt to uncover

common success factors to improve firm value and performance.

2.9 Corporate lifecycle

Corporate lifecycle literature describes evolving internal and external factors that influence

how businesses develop. Some of these factors are strategic decisions taken by the firm;

others are related to its endowment of financial and human capital, and yet others relate to

external factors such as the macroeconomic environment or competitive forces (Dickinson,

2011). Firms enter different stages in their lifecycle as these factors change, and when they

do, boards need an alternative set of skills driven by the resource dependency theory.

There is an equally plausible scenario where the appointment of a new director results in a

different aggregate board skillset that leads to a new phase in the firm’s lifecycle.

Regardless of the direction of causality, different stages of lifecycle are likely to be

associated with a different collective set of expertise at the board level.

Studies have used a ranking method to allocate companies in different life stages without

defining key characteristics of a specific lifecycle stage. Variables used by Anthony and

Ramesh (1992) and DeAngelo, DeAngelo and Stulz (2010) in ranking companies included

dividend payouts, sales growth, capital expenditure as a ratio of firm value, market-to-

book ratio, age and abnormal stock returns as lifecycle stage proxies. A shortcoming of

this approach is that sorting does not capture essential internal and external factors that

pivot firms from one stage to the next; they merely represent their relative standing

compared to other companies in the given sample. Dickinson (2011) developed a

theoretically more robust approach using lifecycle theory as her starting point and

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examining firms’ most likely cash flow characteristics in each phase. First, the sign of

operating, investing and financing cash flows are observed and in the next stage they are

mapped to the five lifecycle stages defined by Gort and Klepper (1982). The method has

been applied in the wider accounting and finance literature, contemporary corporate

governance studies (Al-Hadi, et al., 2016, Hasan, et al., 2015, Koh, et al., 2015, Oler and

Picconi, 2014) and will be used in this analysis.

The Australian Accounting Standards Board (AASB) is authorised under section 334 of

the Corporations Act 2001 to make accounting standards by legislative instruments. AASB

Standard 107 Statement of Cash Flows (2015) sets out the requirements related to

classifying cash flows from operating, financing and investing activities. Operating cash

flows capture cash receipts from ordinary business activities and payments related to

generating revenue. Investing activities presented in the statement of cash flows are

expenditures made to acquire resources that are expected to generate future income and

when those resources are recognised as assets in the firm’s statement of financial position.

Finally, cash from financing activities are separately classified from other cash when they

are either proceeds from, or represent claims on future cash flows to providers of capital.

Building on Dickinson (2011) and Gort and Klepper (1982) the following section will

provide an overview of corporate lifecycle stages and describe cash flow characteristics

representative of each phase. Table 1 provides a summary overview of these patterns.

Early stage

Companies in the introduction stage of their development often experience negative

operating cash flows due to inconsistency of revenues and uncertainty about their cost

structure. They deploy capital to develop production capacity and acquire long-term assets

that are recorded as negative investment cash flows. Lacking consistent operating cash

flows, these firms need sources of financing to grow and will access debt and capital

markets as sources of funding; the net impact in this stage is positive financing cash flows.

Growth phase

Firms with proven business models and customers start attracting consistent positive

operating cash flows. Driven by this optimism, they will continue investment activity,

scale production, and work towards achieving economies of scale in order to deter

competitors. The sources of this new investment are positive net operating cash flows and

cash from financing activities. Pecking order theory suggests that firms prefer internal to

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external financing and will access debt before they issue equity in order to benefit from the

tax shield of interest payments, balanced with the risk of over-borrowing (Myers, 1984).

Mature firms

Businesses continue to enjoy positive cash flows in the mature lifecycle stage and benefit

from certainty in cost structure and potential revenues. While they are likely to continue

their investing activities to maintain assets, mature firms, by definition, have access to

fewer positive net present value projects that would warrant external financing and hence

net cash from financing activities will become negative. Firms in this stage shift focus

towards retiring debt, paying dividends or engaging in share buybacks.

Decline stage

A firm in its decline stage experiences weakening growth rates and deteriorating pricing

power. These symptoms can stem from product obsolescence or increased competition and

result in negative operating cash flows. In order to service debt or repurchase shares, firms

may need to liquidate assets and for the first time since the company’s inception, investing

cash flows will be positive. In this lifecycle phase, providers of capital may receive

payments, loans may be renegotiated, or new preferred equity instruments are issued.

Financing cash flows may be either positive or negative depending on the net impact of the

two factors.

Shake-out stage

There are three remaining cash flow pattern combinations that the literature does not

specifically map to a lifecycle stage. Dickinson (2011) calls this the shake-out stage and

maps to Gort and Klepper’s (1982) Stage 4, characterised by the non-equilibrium phase of

negative net market entry by firms and potential structural changes in the industry. In this

phase, either all cash flows are positive; all are negative; or positive operating and

investing cash flows are combined with negative financing cash flows.

Table 1

Predicted sign of cash flows from operating, investing and financing activities

Early Growth Mature Shake-out Decline

Operating – + + – + + – –

Investing – – – – + + + +

Financing + + – – + – – +

This table shows the eight possible combinations of operating, investing and financing cash flow patterns and their association to five distinct corporate lifecycle stages.

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3 Professional expertise on corporate boards

This analysis looks at a sample of Australian companies to study the relationship between

diversity of expertise on corporate boards and firm performance. The list of company

directors have been obtained for all ASX listed companies on record for 2014 from the

SIRCA Corporate Governance database. It contains director expertise with each director

classified by his or her type of professional expertise, qualification and experience –

finance, accounting, legal, HR, mining, engineering and other. This study will use the

expertise field and disregard the descriptive qualification and experience fields. Since

primary fields of expertise are not distinguished from secondary fields, where a director

has more than one field of expertise, all of them are included with an equal weight. The

dataset includes 5,172 directorships across 764 companies on record in 2014. Of all

directors, 891 posses financial, 1,090 accounting, 465 legal, 12 human resources, 529

mining, 263 engineering and 2,118 other expertise. With 5,368 individual expertise

instances listed, each director possesses 1.038 types of expertise on average. In the next

stage, GICS sector classifications were acquired from the Morningstar DatAnalysis

Premium database and matched to the sample. Missing sector qualifications from 50

delisted or renamed entities were identified through an internet search. Additional

variables were created for the number of directors on each board; board diversity measured

as the instance and product of different expertise types on a given board, and a board

diversity index for the concentration of expertise. A detailed description of the variables is

included in the Appendix.

3.1 Professional expertise across industries

Table 3 shows the percentage of all firms by industry and the types of professional

expertise on boards. The statistics show evidence that of all firms, 57.98 percent have at

least one financial expert, 77.09 percent have at least one accountant and 43.72 percent

have at least one director with legal expertise, 1.44 percent have at least one HR expert,

31.68 have at least one mining expert, 23.30 percent have at least one engineer and 81.81

percent have directors with other, unclassified expertise. The table also provides insight

into clustering of finance experts in the financial sector, mining experts in the energy and

materials sectors and engineers in the industrials and utilities sectors. Accounting expertise

is prevalent across all sectors, but utilities firms have the fewest accountants of all

industries. Other expertise is consistently omnipresent across all sectors, which suggests

that further research is warranted to create a more in-depth and granular classification.

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Table 5 sets out board composition characteristics for the sample of ASX listed firms

across industry sectors. Statistics that are higher (+) or lower (–) than the mean of all

industries are denoted to indicate significance at the 1%, 5% and 10% levels. The average

board has 6.77 members; consumer staples and telecommunication services firms have

significantly larger boards with 8.15 and 8.44 members on average respectively. The

average information technology board is the smallest across all sectors with 5.43 directors.

Across all firms, the average board comprises of 16.6 percent finance experts, 20.3 percent

accountants, 8.66 percent lawyers, 0.22 percent of those with human resources expertise,

9.85 percent with mining expertise and 4.9% percent with engineering expertise. Other,

unclassified expertise comprises the remaining 39.46 percent. Accounting expertise is

more prevalent on consumer discretionary and information technology boards, but less

dominant for consumer staples and energy firms. Finance and legal expertise on boards are

higher at companies in the financial sector.

On average, companies have 3.17 different types of expertise represented on boards, with

some having a single one across all directors and others having up to seven types of

expertise. The average expertise diversity index is 0.55, calculated as the complement of a

Herfindahl-Hirschman type concentration index where a figure closer to 1.00 indicates the

highest diversity. On average, consumer staples and health care firms show significantly

lower expertise diversity on their corporate boards than the average, while the

concentration of mining expertise at energy and materials companies and engineers at

utilities firms make the three sectors the most diverse in terms of board expertise.

Illustrating professional expertise differences across industries, the average board of ten in

the materials industry would have one finance expert, two accounting experts, one lawyer,

two directors with a mining background, one engineer and three directors with other

expertise. Health Care sector board composition is not as well understood, comprising of

six directors with unclassified expertise, one with a finance background, and two

accounting experts complemented by a lawyer.

3.2 Professional expertise across corporate lifecycles

In addition to director expertise data, financial data was obtained from the Morningstar

database for 1,992 ASX listed companies including total current assets, total equity, total

liabilities, cash from operating, financing and investing activities, price to book value and

return on assets for 2014. Tobin’s Q and the natural logarithm of total assets were

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calculated as additional variables. Of the 764 entries in the SIRCA director expertise

dataset, 717 had matching financial data in the Morningstar dataset, which were used to

create the final dataset that encompasses 4,890 directorships. Financial variables were

winsorised at the 1st and 99th percentiles with the exception of total assets. Descriptive

statistics are provided in Table 2. A brief analysis comparing the full sample of 1,992 firms

and the subsample was undertaken and showed that the final sample has a bias towards

larger firms, possibly as the 47 missing firms from the Morningstar dataset included

companies that were either delisted or underwent a name change due to a reverse takeover

during 2014. This bias is not expected to skew the analysis, as total assets are set as a

control variable and cash flow data are in a comparable range for both samples.

Table 2

Sample descriptive statistics

Mean Median Min Max Std. dev.

Total assets (bn) 6.77 0.14 0.00 883.30 61.16

Total equity 0.82 0.09 -0.01 20.54 2.81

Total liabilities 1.60 0.04 0.00 70.10 8.46

Net operating cash flow 0.14 0.00 -0.08 4.60 0.57

Net investing cash flow -0.10 0.00 -3.31 0.13 0.40

Net financing cash flow -0.03 0.00 -2.43 0.55 0.29

Market capitalisation 1.60 0.11 0.00 50.06 6.34

Tobin's Q 2.17 1.15 0.20 26.73 3.56

Price to book value 2.15 1.18 -4.50 22.25 3.45

Return on assets % -0.28 0.02 -7.17 0.46 1.08

Return on equity % -0.17 0.04 -7.69 4.66 1.29

Growth in assets % 0.15 0.02 -0.89 5.59 0.82

Debt to total assets 0.58 0.36 0.00 11.66 1.41

Board size 6.82 6.00 3.36 1.00 24.00

% Independent 30.61 31.25 0.00 76.92 16.40

% Females 9.82 8.33 0.00 46.15 10.32

% Other Directorships 39.85 37.50 0.00 100.00 20.92

% Duality 12.13 0.00 0.00 100.00 32.67

Number of expertise 3.18 3.00 1.09 1.00 7.00

Sum of expertise 7.08 6.00 3.57 1.00 34.00

Expertise index 0.56 0.61 0.19 0.00 0.82

This table shows descriptive statistics of firm characteristics for 717 ASX listed firms based on data available in the SIRCA Corporate Governance database and the Morningstar DatAnalysis Premium database.

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Table 5 sets out the percentage of firms with at least one member of the board possessing a

specific type of professional expertise indicated across corporate lifecycles. It indicates

that finance expertise is less dominant on early stage company boards and legal expertise

is less prevalent for companies in the decline phase. Instances of accounting and human

resources expertise is higher, while mining expertise is lower than average for mature

firms. These differences, however, are only significant at the 5% level. Board

characteristics across corporate lifecycles is summarised in Table 6. It provides evidence

that early stage firms have significantly smaller boards with an average of 5.72 directors

and mature firms have significantly larger boards with an average of 7.88 directors,

compared to the average board size of 6.82 directors across all firms. There are

significantly more finance professionals on shake-out and decline stage company boards

and more engineers on growth company boards than the average. Firms in the decline

stage have significantly fewer legal experts but more mining and finance experts compared

to the average firm. Growth phase and mature companies are significantly more diverse in

terms of professional expertise on their boards.

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Table 3

Percentage of firms with types of professional expertise on the board across industries

All firms

Consumer

Discretionary

Consumer

Staples Energy Financials

Health

Care Industrials

Information

Technology Materials

Telecom

Services Utilities

Finance 57.98 51.04 55.56 59.77 83.51 45.00 57.01 55.10 52.80 56.25 81.82

Accounting 77.09 82.29 85.19 78.16 74.23 78.33 76.64 73.47 75.70 81.25 63.64

Legal 43.72 48.96 33.33 49.43 43.30 31.67 42.99 40.82 44.39 43.75 54.55

HR 1.44 3.13 0.00 0.00 1.03 3.33 0.93 2.04 0.93 6.25 0.00

Mining 31.68 1.04 0.00 63.22 2.06 0.00 18.69 8.16 72.43 6.25 36.36

Engineering 23.30 11.46 22.22 34.48 10.31 15.00 34.58 20.41 26.17 25.00 45.45

Other 81.81 92.71 96.30 72.41 80.41 98.33 86.92 85.71 70.56 93.75 81.82

No. of firms 764 96 27 87 97 60 107 49 214 16 11

This table shows a sample of 764 ASX-listed companies from 2014. It lists the percentage of firms by industry with at least one member of the

board of directors possessing the specific type of professional expertise. The Appendix includes detailed variable definitions.

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Table 4Board composition and characteristics across industries

All firmsConsumer Discretionary

Consumer Staples Energy Financials Health Care Industrials

Information Technology Materials

Telecom Services Utilities

Board Size 6.77 6.85 8.15 + + + 6.86 6.74 6.80 7.15 5.43 – – – 6.49 – 8.44 + + + 7.36

% Finance 16.60 13.27 14.03 14.54 36.32 + + + 11.08 – – 14.88 18.97 12.33 13.04 17.05

% Accounting 20.31 22.42 + + + 18.10 – – – 17.77 – – – 20.98 19.04 20.68 22.76 + + + 20.28 19.57 18.18 – –

% Legal 8.66 9.29 + + 5.88 – – – 9.05 + 9.84 + + + 6.02 – – – 8.45 7.93 9.20 + 7.25 – 9.09 +

% HR 0.22 0.44 + 0.00 – – 0.00 – – 0.14 0.72 + + + 0.13 0.34 0.14 0.72 + + + 0.00 – –

% Mining 9.85 0.15 – 0.00 – 19.39 + + + 0.29 – 0.00 – 4.04 1.72 25.16 + + + 0.72 7.95

% Engineering 4.90 1.62 – – 3.17 7.11 1.59 – – 2.65 – 8.95 + + 4.48 5.37 4.35 13.64 + + +

% Other 39.46 52.80 + + 58.82 + + + 32.15 – – 30.82 – – – 60.48 + + + 42.88 43.79 27.53 – – – 54.35 + + 34.09 – –

No. Expertise Mean 3.17 2.91 – 2.93 – 3.57 + + + 2.95 2.72 – – – 3.18 2.86 – – 3.43 + + 3.13 3.64 + + +

Minimum 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

Maximum 7.00 5.00 – 5.00 – 6.00 + 5.00 – 5.00 – 6.00 + 5.00 – 7.00 + + + 5.00 – 6.00 +

Expertise Index Mean 0.55 0.51 – 0.46 – – – 0.61 + + + 0.54 0.48 – – – 0.55 0.52 0.60 + + + 0.51 0.59 + + +

Minimum 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Maximum 0.82 0.74 – 0.75 0.82 + + + 0.77 0.73 – – – 0.79 + 0.78 0.82 + + + 0.72 – – – 0.75

No. firms 764 96 27 87 97 60 107 49 214 16 11

This table shows board composition and characteristics for all 764 ASX-listed firms in 2014 and separated by industry sector. In case the mean for the industry is significantly higher (lower) than overall means using a one-sample t-test, the results are denoted to show significance at the 1% +++, 5% ++ and 10% + levels for higher

14

means (1% – – –, 5% – –, 10% – for lower means). The Appendix includes detailed variable definitions.

Table 5

Percentage of firms with types of professional expertise across corporate lifecycles

All firms Early stage Growth phase Mature firms Shake-out stage Decline phase

Finance 58.58 47.43 67.69 58.30 63.00 62.92

Accounting 76.99 75.43 76.92 83.41 71.00 70.79

Legal 44.21 37.14 50.77 52.02 44.00 29.21

HR 1.53 0.57 1.54 3.59 0.00 0.00

Mining 31.52 44.57 28.46 19.73 29.00 42.70

Engineering 23.43 18.29 29.23 29.60 15.00 19.10

Other 82.15 74.86 84.62 88.34 80.00 79.78

No. of firms 717 175 130 223 100 89

This table shows a sample of 717 ASX-listed companies from 2014. It lists the percentage of firms by corporate lifecycle with at least one

member of the board of directors possessing the specific type of professional expertise. The Appendix includes detailed variable definitions.

15

Table 6Board composition and characteristics across corporate lifecycles

All firms Early stage Growth phase Mature firms Shake-out stage Decline phase

Board Size 6.82 5.72 – – – 7.54 7.88 + + + 6.35 5.81

% Finance 16.70 15.78 16.00 16.18 19.08 + + + 18.61 + + +

% Accounting 20.32 21.75 + 17.37 – 21.68 + 19.23 19.92

% Legal 8.74 8.18 9.03 9.19 9.54 6.77 – – –

% HR 0.24 0.10 0.20 0.50 + + + 0.00 0.00

% Mining 9.91 16.46 + + + 9.22 5.12 – – – 9.24 15.60 + + +

% Engineering 4.86 3.85 6.28 + + + 5.78 3.13 3.20

% Other 39.23 33.88 – – – 41.90 + + + 41.55 39.79 35.90

No. Expertise Mean 3.18 2.98 3.39 + + + 3.35 + + + 3.02 3.04

Minimum 1.00 1.00 1.00 1.00 1.00 1.00

Maximum 7.00 5.00 – – – 6.00 7.00 + + + 5.00 – – – 6.00

Expertise Index Mean 0.56 0.55 0.56 0.57 + + + 0.53 – – – 0.56

Minimum 0.00 0.00 0.00 0.00 0.00 0.00

Maximum 0.82 0.79 0.82 0.82 + + + 0.78 – – – 0.80

No. firms 717 175 130 223 100 89

This table shows board composition and characteristics for all 717 ASX-listed firms in 2014 and separated by corporate lifecycle. In case the mean for the

16

lifecycle is significantly higher (lower) than overall means using a one-sample t-test, the results are denoted to show significance at the 1% + + +, 5% + + and 10% + levels for higher means (1% – – –, 5% – –, 10% – for lower means). The Appendix includes detailed variable definitions.

17

4 Professional expertise diversity and firm value

This section will examine the link between various aspects of professional expertise on

boards and firm value using the sample of 717 ASX listed firms. Descriptive statistics

provided in Table 2 show that the mean (median) firm has total assets of $677 billion ($140

million), total equity of $820 million ($90 million), positive operating cash flows, negative

investing cash flows and negative (positive) financing cash flows. The average firm has a

market capitalisation of $1.6 billion ($112 million); a Tobin’s Q of 2.17 (1.15). It achieved a

return on assets of -28% (2%) and an asset growth of 15% (2%) in 2014. The average number

of the directors is 6.82 with an expertise count of 7.08 across 3.18 different types represented

on the board. The median industry adjusted number of expertise is 1.02 and the median

lifecycle adjusted number of expertise is 0.99.

As described in the previous section, board structure and composition differ across industries

and corporate lifecycles. Gaps in current knowledge as highlighted in the introduction

motivated this study to determine whether there is a relationship between expertise diversity,

the presence of specific professional expertise on the board and firm performance; it led to

forming the below hypotheses. Consistent with previous studies, the accounting measure of

firm performance will be return on assets (ROA) and the share market measure of firm value

will be Tobin’s Q; industry, firm and board level variables will be used to control for fixed

effects.

H0 : Firm performance is not related to expertise diversity or the presence of

professional expertise on corporate boards.

HA1 : When industry sector is considered, there is a relationship between firm

performance and expertise diversity on corporate boards.

HA2 : When stages of corporate lifecycle are considered, there is a relationship

between firm performance and expertise diversity on corporate boards.

HA3 : There is a relationship between firm performance and the presence of specific

professional expertise on the board of companies that are in a given lifecycle phase.

The hypotheses developed above are empirically tested using OLS regression analysis. The

first model is given in Equation 1:

18

Firm Performancei = 𝛽 0 + 𝛽 1 Expertise Diversity Measure i + 𝛽 p,2 [Board Factors i,p] + 𝛽 m,3 [Firm Fixed Effects i,m] + 𝛽 n,4 [Industry Factors i,n] + ℇ i

(1)

Where Firm Performancei is Tobin’s Q and ROA, the Expertise Diversity Measure i is either

the number of expertise types on the board, the sum of expertise types, the industry adjusted

number of expertise types, the lifecycle adjusted number of expertise types or an expertise

index for Firmi. Board Factorsi is a set of board specific variables including board size, the

percentage of females, percentage of independent directors, percentage of board members

with other directorships and a dummy variable indicating that the CEO is also the chairman

of the board. The Firm Fixed Effectsi control variable group includes the natural logarithm of

total assets, annual growth in total assets, debt-to-asset ratio, and ROA when the dependent

variable is Tobin’s Q. Industry Factorsi is a set of dummy variables indicating GICS industry

sector.

In the first stage, Gray and Nowland’s (2015) analysis using 2007 data was replicated using

the 2014 dataset. In unreported findings, the results of the OLS regression confirm the lack of

overall cross-sectional relationship between firm performance and either (a) the number of

expertise types on the board, measures of expertise diversity such as (b) the expertise index,

(c) the industry adjusted expertise index, or a (d) new lifecycle adjusted expertise index.

Allocating the sum of expertise across two subsets to include business specific expertise

(finance, accounting and legal) in one group and other expertise types in a second group

following the method suggested by Anderson et al (2011), no significant relationship was

found with shareholder wealth. While Gray and Nowland (2015) detected a negative

relationship between other types of expertise (HR, mining, engineering and others) and return

on assets, this study found no significant relationship.

In the second stage, industry control variables are replaced with corporate lifecycle indicators

and the hypotheses developed above are empirically tested using OLS regression analysis.

The second model to be estimated is given in Equation 2:

19

Firm Performance i = 𝛽 0 + 𝛽 1 Expertise Diversity Measure i + 𝛽 p,2 [Board Factors i,p ]+ 𝛽 m,3 [Firm Fixed Effects i,m] + 𝛽 n,4 [Lifecycle Factors i,n] + ℇ i

(2)

Where Lifecycle Factorsi is a set of dummy variables indicating corporate lifecycle based on

Dickinson (2011). The OLS regression analysis concludes the lack of relationship between

firm performance or shareholder wealth and expertise diversity on boards when the industry

control variable is replaced with the corporate lifecycle control variable.

In the third stage a model is developed to test the link between firm performance and

expertise given the phase in the company’s lifecycle. The third model to be estimated is given

in Equation 3:

Firm Performance i = 𝛽 0 + 𝛽 m,1 [Director Expertise i,m × Corporate Lifecycle i,m] + 𝛽 p,2 [Firm Fixed Effects i,p] + ℇ i

(3)

Where Firm Performancei denotes Tobin’s Q and ROA, Director Expertisei is a set of dummy

variables indicating the presence of financial, accounting, legal, HR, mining, engineering or

other expertise on the board and Corporate Lifecyclei is a set of dummy variables indicating

that a firm is either in the early, growth, mature, shake-out or decline stage. The Firm Fixed

Effectsi control variable group includes the natural logarithm of total assets, annual growth in

total assets and debt-to-asset ratio.

Table 7 sets out the results. They suggest that the presence of specific types professional

expertise on the board of companies in a given lifecycle phase can be linked to firm

performance. In particular, there is a significant positive relationship between financial, HR

and engineering expertise on boards of early stage firms. Mining expertise is negatively

related to firm performance at early stage and shake-out firms and there is also a negative

relationship between firm performance and accounting expertise at companies in the decline

stage.

In addition to firm value as measured by Tobin’s Q, this study also investigated professional

expertise on boards in relation to firm performance as measured by return on assets and found

20

that it is positively related to engineering and other expertise on shake-out firm boards,

negatively related to financial and mining expertise of shakeout firms; mining and other

expertise on firms in the decline stage, and engineering expertise at early stage firms.

The results of the control variables suggest that firm value is positively related to leverage

and growth, and negatively related to firm size. Firm performance is positively related to size

and growth, while negatively related to leverage.

Table 7

Board expertise, corporate lifecycle and firm value

Explanatory Variables Tobin’s Q ROA

Intercept

Ln (Total assets)

Debt to total assets

Growth

Financial Expertise & Early Stage

Financial Expertise & Growth

Financial Expertise & Mature

Financial Expertise & Shakeout

Financial Expertise & Decline

Accounting Expertise & Early Stage

Accounting Expertise & Growth

6.94

(8.66)

-0.31

(-7.34)

1.73

(27.17)

0.56

(5.21)

0.87

(2.73)

-0.14

(-0.34)

0.24

(0.81)

-0.08

(-0.20)

-0.03

(-0.07)

0.35

(1.05)

-0.06

(-0.16)

***

***

***

***

***

-2.31

(-9.33)

0.13

(9.78)

-0.42

(-21.35)

0.15

(4.48)

0.02

(0.18)

-0.02

(-0.20)

-0.04

(-0.44)

-0.44

(-3.44)

-0.02

(-0.14)

-0.07

(-0.65)

0.03

(0.27)

***

***

***

***

***

21

Accounting Expertise & Mature

Accounting Expertise & Shakeout

Accounting Expertise & Decline

Legal Expertise & Early Stage

Legal Expertise & Growth

Legal Expertise & Mature

Legal Expertise & Shakeout

Legal Expertise & Decline

HR Expertise & Early Stage

HR Expertise & Growth

HR Expertise & Mature

Mining Expertise & Early Stage

Mining Expertise & Growth

Mining Expertise & Mature

Mining Expertise & Shakeout

Mining Expertise & Decline

Engineering Expertise & Early Stage

0.35

(0.95)

0.19

(0.43)

-0.95

(-2.09)

-0.45

(-1.30)

-0.22

(-0.56)

0.23

(0.79)

-0.11

(-0.25)

-0.50

(-0.91)

4.59

(2.08)

-0.36

(-0.22)

1.24

(1.56)

-1.01

(-3.15)

-0.08

(-0.19)

-0.44

(-1.19)

-0.87

(-1.82)

0.54

(1.15)

1.23

**

**

***

*

***

0.11

(0.99)

-0.20

(-1.44)

0.10

(0.74)

-0.17

(-1.56)

-0.07

(-0.54)

-0.06

(-0.64)

0.08

(0.56)

0.18

(1.10)

0.27

(0.39)

0.09

(0.19)

-0.06

(-0.26)

-0.13

(-1.35)

-0.09

(-0.66)

-0.09

(-0.74)

-0.73

(-4.95)

-0.40

(-2.76)

-0.32

***

***

**

22

Engineering Expertise & Growth

Engineering Expertise & Mature

Engineering Expertise & Shakeout

Engineering Expertise & Decline

Other Expertise & Early Stage

Other Expertise & Growth

Other Expertise & Mature

Other Expertise & Shakeout

Other Expertise & Decline

R2

Adjusted R2

F

n

(2.83)

-0.25

(-0.57)

-0.06

(-0.18)

-0.04

(-0.06)

-0.09

(-0.16)

-0.52

(-1.53)

0.27

(0.62)

-0.28

(-0.73)

0.04

(0.08)

0.79

(1.62)

0.644

0.625

34.2

717

***

(-2.40)

0.04

(0.32)

-0.15

(-1.49)

0.36

(1.88)

0.19

(1.03)

-0.03

(-0.28)

-0.10

(-0.72)

-0.01

(-0.11)

0.39

(2.77)

-0.38

(-2.54)

0.631

0.611

32.3

717

*

***

**

***

This table shows OLS regression models that relate director professional expertise and

corporate lifecycle to firm value (Tobin’s Q) and firm performance (return on assets) for a

sample of 717 ASX-listed firms in 2014. Professional expertise of directors were obtained

from the SIRCA Corporate Governance database and financial data was obtained from the

Morningstar DatAnalysis Premium database. t-statistics are provided in parentheses and

asterisks denote significance at 1% (***), 5% (**) and 10% (*) levels. Table 7 in the

Appendix includes detailed variable definitions.

23

5 Conclusions

The relationship between composition of the board of directors and firm performance has

been the subject of numerous studies over the past three decades and the only consistent

conclusion has been the lack of compelling evidence for any overall or cross-sectional link.

This suggested taking a more granular view of directorships and examining the role that

directors may play given their professional background. A more in-depth analysis was

undertaken by examining the expertise of board members taking into account the company’s

industry and its lifecycle stage.

This study provided evidence that accounting and finance were the most dominant types of

expertise on ASX boards in 2014. Expertise diversity was shown to be highest for growth

stage and mature firms and companies in the energy and utilities sectors. In contrast, health

care firms and companies in the shake-out lifecycle stage had the most homogenous boards.

The analysis also indicated that shareholders benefit when early stage firms have finance and

engineering expertise among their directors. It was shown, however, that mere diversity of

expertise is not related to either firm value or return on assets; it is the presence of specific

expertise that matters.

This study extends the academic literature and provides practical advice to investors and

company directors. It builds on previous research on professional expertise at the board level

and extends the literature by introducing the concept of corporate lifecycles to deepen our

understanding of how boards add value. In order to fulfil their key roles of providing counsel

and offering external connections, boards need to have the appropriate mix of expertise. An

insight into the composition of ASX company boards was offered with a dual purpose. It

highlighted best practice for private companies contemplating public listing and for public

companies it offered an analysis of board structures that lead to enhanced firm outcomes.

Having identified some of the structures that lead to desired results; further research is

warranted to uncover how these directors add value in their capacity as mentors and

connectors given the benefit of their professional background.

24

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7 Appendix

Table 8

Variable Definitions

Variable Definition

Board VariablesDualityFinancial ExpertiseAccounting ExpertiseLegal ExpertiseHR ExpertiseMining ExpertiseEngineering ExpertiseOther ExpertiseP Financial Expertise

P Accounting Expertise

P Legal Expertise

P HR Expertise

P Mining Expertise

P Engineering Expertise

P Other Expertise

Dummy variable equal to one if the CEO is also the chairman of the boardTotal number of directors with financial expertise on the boardTotal number of directors with accounting expertise on the boardTotal number of directors with legal expertise on the boardTotal number of directors with human resources expertise on the boardTotal number of directors with mining expertise on the boardTotal number of directors with engineering expertise on the boardTotal number of directors with other expertise on the boardTotal number of directors with financial expertise on the board divided by the number of different types of expertise on the boardTotal number of directors with accounting expertise on the board divided by the number of different types of expertise on the boardTotal number of directors with legal expertise on the board divided by the number of different types of expertise on the boardTotal number of directors with human resources expertise on the board divided by the number of different types of expertise on the boardTotal number of directors with mining expertise on the board divided by the number of different types of expertise on the boardTotal number of directors with engineering expertise on the board divided by the number of different types of expertise on the boardTotal number of directors with other expertise on the board divided by the number of different types of expertise on the board

29

No IndependentNo Non-independentBoardSizeNo OtherBoardsPC DirectorshipsNoExpertiseIAdjNoExpertise

SAdjNoExpertise

SUMExpNo Expertise FALNo Expertsie MEOExpertise FAL

Expertise MEO

Expertise Index

PC IndependentIndependentBoardTotal AssetsLn Total AssetsTotal EquityTotal LiabilitiesDebt-To-AssetsNet Financing Cash FlowNet Investing Cash Flow

Total number of independent board membersTotal number of non-independent board membersTotal number of board membersTotal number of external directorships held by all directorsTotal number of external directorships held by all directors divided by the total number of board membersThe number of different types of expertise held by board membersThe number of different types of expertise held by board members divided by the average number of expertise for the industry sectorThe number of different types of expertise held by board members divided by the average number of expertise for the corporate lifecycleTotal number of expertise held across all directorsTotal number of finance, accounting or legal expertise held across all directorsTotal number of HR, mining, engineering or other expertise held across all directorsTotal number of finance, accounting or legal expertise held across all board directors divided by the total number of expertise held across all directorsTotal number of HR, mining, engineering or other expertise held across all directors divided by the total number of expertise held across all directors1 – the sum of the squared ratios of each type of professional expertise and the total number of expertise held across directorsThe number of independent directors divided by the total number of directorsDummy variable equal to one if the percentage of independent directors on the board is larger than 50%Total assetsNatural logarithm of total assetsTotal equityTotal liabilitiesTotal assets divided by total liabilitiesNet financing cash flowNet investing cash flow

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Net Operating Cash Flow1YR Growth-Total AssetsMarket CapitalisationNet DebtPrice-BookTobin’s QROAROEIntroductionGrowthMatureShake-OutDeclineConsumer DiscretionaryInformation TechnologyHealth CareEnergyFinancialsConsumer StaplesMaterialsUtilitiesIndustrialsTelecommunication Services

Interaction VariablesA × B

Net operating cash flowAnnual growth in total assets in the last financial yearMarket capitalisation plus total liabilities all divided by total assetsNet debtPrice-to-book ratioMarket capitalisation plusReturn on Assets – annual net income divided by the book value of total assets at the end of the periodReturn on Equity – annual net income divided by the book value of total equity at the end of the periodDummy variable equal to one if the firm is in the introduction corporate lifecycle stageDummy variable equal to one if the firm is in the growth corporate lifecycle stageDummy variable equal to one if the firm is in the mature corporate lifecycle stageDummy variable equal to one if the firm is in the shake-out corporate lifecycle stageDummy variable equal to one if the firm is in the decline corporate lifecycle stageDummy variable equal to one if the firm is in the Consumer Discretionary GICS sectorDummy variable equal to one if the firm is in the Information Technology GICS sectorDummy variable equal to one if the firm is in the Health Care GICS sectorDummy variable equal to one if the firm is in the Energy GICS sectorDummy variable equal to one if the firm is in the Financials GICS sectorDummy variable equal to one if the firm is in the Consumer Staples GICS sectorDummy variable equal to one if the firm is in the Materials GICS sectorDummy variable equal to one if the firm is in the Utilities GICS sectorDummy variable equal to one if the firm is in the Industrials GICS sectorDummy variable equal to one if the firm is in the Telecommunication Services GICS sector

Dummy variables equal to one for 35 combinations of expertise and corporate lifecycle or GICS industry sector, where A (Financial Expertise, Accounting Expertise, Legal Expertise, HR Expertise, Mining Expertise, Engineering Expertise, Other Expertise) and B (Introduction, Growth, Mature, Shake-Out, Decline)

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