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A Unique Way of Identifying High Quality Directors:

Evidence from a Seemingly Unrelated Annual Event in India

Sugato Chakravarty and Prasad Hegde

Purdue University West Lafayette, IN 47906

Abstract

We use a seemingly unrelated annual event prevalent in India for many decades to directly identify smart and driven (SD) directors ex ante. We then examine the potential relationship between proportion of such directors SD directors and firm performance. Employing a comprehensive data set of stocks trading in the National Stock Exchange in India between 2006 and 2015, we find that SD directors have more directorships relative to those directors who do not meet similar criterion. At the board level, there exists a positive relationship between firm performance and the fraction of SD directors on corporate boards. We also find that boards comprised of a higher proportion of SD directors are more likely to appoint an additional SD director. Upon classifying firms into simple and complex, using previous research, we show that complex firms have a greater proportion of SD directors as well as a larger boards. JEL Classification: G3 Keywords: Indian Corporate Governance; Director; Board advising; Firm performance; Engineering degrees; Firm complexity.

This Version: August 2018

________________________________________________ Acknowledgments: We thank John McConnell for many related discussions. We also thank the finance department seminar participants at Auckland University of Technology and, in particular, Bart Frijns, for comments and suggestions. The usual disclaimer holds.

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1. Introduction

In the current paper, we contribute to the board advising quality literature by appealing to a seemingly

unrelated annual event (SUAE) that has been in existence in India in one form or another since 1960. We use this

annual event to ex-ante identify those directors we label as innately smart/driven (SD) directors. We confirm that

our chosen ex ante instrument of quality directors is significantly positively correlated with firm performance after

controlling for all other significant determinants of firm performance as identified by related studies.

Specifically, our SD directors are those board members who happened to be successful many decades

prior (as teenagers/young adults) in an annually administered national level entrance examination (also known as

the Joint Entrance Examination or JEE) to post-secondary engineering institutions.1 If they were not successful in

the JEE we classify them as non-driven or NSD. Our rationale for why we call those individuals who successfully

passed the JEE as SD individuals is explained in detail in the next section but, in a nutshell, we argue that given the

astronomical odds of qualifying in this hypercompetitive entrance examination to select Engineering Colleges in

India, coupled with the elevated place accorded to education in middle class India, nothing but raw smarts and/or

an internal drive to succeed explains why/how these individuals succeed in this exam and how this same internal

drive carries them through the rest of their lives. We clarify that while not all successful JEE individuals succeed

later in life, the successful are more likely than not to have successfully passed the JEE decades earlier. We obtain

the educational qualifications for all the directors on a given board/firm that are publicly traded on NSE over the

period 2006 to 2015 from the National Stock Exchange’s (NSE) Indian Boards database. For each director we obtain

his undergraduate degree details (Engineering, Science, Arts, Commerce, Medicine, etc.) as well as details on his

postgraduate degrees if any. We also obtain details about a specific director’s gender, age, nationality,

appointment period, whether a director is part of civil Services and number of present directorships. Further, we

obtain information pertaining to each director’s appointment (and resignation) dates to and from a given

company’s board. In addition, we obtain stock specific details relevant to the current investigation.

Our main findings can be summarized as follows. SD directors have more directorships relative to NSD

directors. Further, a positive relationship exists between firm performance, measured by its market to book ratio,

and the fraction of SD directors on a given corporate board. We also find that boards comprised of a higher

proportion of SD directors are more likely to appoint an additional SD director. Following an identical classification

1An exhaustive scan on social media sites asking how non-Indians feel about the craze in India for taking the JEE exam can be summarized as such: Those who know about it are surprised as to how most (if not all) teenagers of a country with 1.25 billion population are so crazy about passing a single exam. Why almost everyone in the country might want to sit for an entrance test to pursue engineering?

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scheme of firms into complex and simple as in Coles, Daniel and Naveen (2008), we show that complex firms have

a greater proportion of SD directors as well as a larger board size.2 These findings survive a battery of robustness

checks.

While it is entirely reasonable to assume that some the future directors’ abilities may well originate from

what they learn in their respective colleges, what we hope we are able to (additionally) show here is that: It is the

(much earlier) act of successfully qualifying in these hyper-competitive qualifying exams to select undergraduate

engineering colleges that provides a (costly) signal in itself of an internal drive to succeed among these directors.3

It is this signal that is correlated with one’s (future) success as a corporate board member, which then is positively

correlated with firm performance. Our work is similar in spirit to the educational signaling model of Spence (1973)

where Spence argues that the high ability people distinguish themselves by sending a signal to potential employers

about their inherent abilities through the costly signal of educational attainment from an elite university (say).

Spence’s intuition is that the cost of acquiring the signal (i.e., a degree from Harvard) is relatively cheaper for a

high ability student than it is for a lesser ability student and therefore the signal cannot be easily mimicked by the

lesser able and then becomes a valid signal that over time becomes self-affirming as employers and others observe

the outcome. In a similar way, in our case, the act of qualifying in a hyper-competitive national level engineering

school entrance examination as an adolescent is a signal of inherent internal drive to succeed which cannot be

easily or cheaply mimicked by those lacking a similar internal drive to succeed. In section 5 we also work to rule

out competing alternative explanation, related to director’s ‘education effect’, as potentially driving our results.

For example, it can be argued that what we are picking up as SD effect (through our classification of directors with

engineering degrees) is really an ‘education effect’ that it is what such SD directors learn in engineering institutions

determining our findings. Although we do not disagree with this notion of SD directors learning in such engineering

institutions, we show that the ‘education effect’ is not significantly driving our results.

To put our study in perspective, there is no denying the prevalent interest among finance scholars,

practitioners and policy makers to better understand what drives corporate performance. Toward that end, a

large body of literature exists investigating the potential role played by the quality of corporate board monitoring

and advising in driving such performance. There is general agreement that high quality advising and effective

2 We compute a factor score on a firm-year basis as a linear combination of business segments, firm size and leverage. Other details are found in Coles et al. (2008). 3 Note that we do not mean to suggest that passing these entrance exams in itself guarantees success. There are many examples of people who succeeded in these entrance examinations but did not succeed (however measured) in later life. What we are suggesting however is that passing of these exams significantly increases the likelihood of success in later life.

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monitoring are both good for the firm, however measured. 4 The relatively recent research has focused on board

structures (large versus small boards; more versus less independent directors) or firm types (complex firms versus

simple firms) and their impact on firm performance as a way to isolate quality of advising.5 Thus, for example,

Coles et al. (2008) begin with the notion that that one board size does not fit all and go on to classify each US firm

in their data as “complex” or “simple”. They then show empirically that, in fact, the relationship between board

size and firm performance (captured by the firm’s Tobin’s Q) is positive for complex firms. However, our research

fits into the more recent literature that looks into a more direct approach to identify certain types of “valuable”

directors ex ante. So, for example, Daniel, McConnell and Naveen (2013) concluded that foreign directors

(dissimilar on the basis of legal regime, language, trust and religion) in US based company boards are chosen by

the multinational corporations to provide advice and that advice is valuable.6 In sum, the results of our study, and

the implications from it, can be applied to various parts of the world where similar examinations exist to channel

high school and/or undergraduate students into various competitive disciplines. We discuss this point in more

detail in the concluding section.

The remainder of this paper is organized in five sections. Section 2 discusses the background and relevant

literature that helps to build our hypotheses. The data sources and variables are described in Section 3. Section 4

presents main results of our study. Section 5 provides evidence for alternative explanation and examines the

results of endogeneity and robustness tests. Section 6 presents our concluding remarks. Appendix A1 provides a

listing and definitions of all the variables used in the analyses while Appendix A2 provides the details of our 3SLS

tests. Appendix A3 provides a background for any interested reader regarding the value of education among the

Indian middle class.

4 See, for example, Hermalin and Weisbach (2003); Adams Hermalin and Weisbach (2010); Borokhovich, Parrino and Trapani (1996); Denis, Denis and Sarin (1997); Coles, Daniel and Naveen (2008, 2010; 2013); Barnea and Guedj (2009); Fracassi and Tate (2010); Daniel, McConnell and Naveen (2011); Faleye, Hoitash and Hoitash (2011); Fracassi (2016) and Brickley and Zimmerman (2010). 5 See also Daniel, McConnell, and Naveen (2011) and Adams (2009). There is a general belief that the firms attempt to balance monitoring and advising functions by adjusting the proportion of inside directors and outside directors. Because previous literature suggests that inside directors contribute mainly to the advising role and outside directors contribute to monitoring (Duchin, Matsusaka and Ozbas ,2010; Lehn, Patro and Zhao , 2009; Linck, Netter and Yang , 2008. Such dichotomy results in firms choosing optimal number of inside versus outside directors (Coles, Daniel and Naveen ,2008; Armstrong, Guay and Weber ,2010; Hermalin and Weisbach ,2003; Kim, Mauldin and Patro ,2014). 6 Masulis, Wang and Xie (2012) find that firms with the presence of foreign directors have higher cumulative abnormal returns (CAR) around cross-border acquisitions. Kim, Mauldin and Patro (2014), using acquisition and the investment decisions of firms, report that as outside director tenure increases their advising performance improves.

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2. Background and Hypotheses

2.1. The SUAE - The Joint Entrance Examination (JEE)

The Indian secondary education system is capped by the administration of a critical national level entrance

examination to the major Engineering Colleges located in various corners of India. This annually conducted

examination is called the Joint Entrance Examination (JEE) and is currently taken by well over a million high school

graduates for a total of roughly 10,000 seats.7,8 The JEE, originally known as the CEE (Common Entrance

Examination) was first administered in 1961 and involved the four Indian Institutes of Technologies (IITs) located

at Kharagpur, Bombay, Kanpur and Madras. Since those early and relatively modest beginnings, the JEE has

mushroomed into a behemoth involving roughly about 140 prestigious engineering colleges and totaling around

10,000 seats and involving over 1.3 million students taking the exam annually and growing.9,10

2.2. Background Literature

In this section, we discuss the related literature and develop our hypotheses. We note that early literature,

beginning with Fama (1980) and Fama and Jensen (1983), documented the board’s role as monitors and advisors,

more recently, Dalziel (2003) has argued that the board of directors monitor management on behalf of the

shareholders and are able to provide valuable resources to the organization.11 However, studies like, Jenson

(1993), Mace (1971), Klein (1998); Booth and Deli (1999); Agrawal and Knoeber (2001) and Adams and Ferreira

(2007) suggest that the advisory role of the board involves formulating business strategy, providing expert advice

7 It is important to note that admission to one of these major engineering colleges is a mark of honor both for the student as well as for his or her family and, for the young men, can have far reaching consequences beyond getting a lucrative position upon graduation including impacting the amount of dowry he might command in a traditional arranged marriage. 8 According to the data available from the National Informatics Centre in India, for example, the number of students taking the JEE was about 1.2 million which has roughly been the average number the past five years. (See http://cbse.nic.in/newsite/prunit/Press%20Release%20-%20JEE%20(Main)%202017%20Result.pdf). 9 The demand to qualify for one of the engineering colleges is so huge that it has spawned a cottage industry of coaching classes scattered in all of the major Indian cities that charge a large premium to coach the students to be better test takers of the JEE and improve their chances of hitting the jackpot. In 2008 Associated Chambers of Commerce (ASSOCHAM) estimated that the coaching classes is 10 million rupees (about $200,000) business and growing. Also, ASSOCHAM estimated that about 600,000 students attend these class every year. (see https://timesofindia.indiatimes.com/india/IIT-coaching-classes-a-Rs-10k-cr-industry/articleshow/3190000.cms ). 10 Varma and Kapoor (2009) provide a detailed description of admission to engineering institutes, specifically IITs through JEE. 11 See, John, Byrd, Kent and Hickman (1992) and Subrahmanyam, Rangan and Rosenstien (1997).

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and valuable resources to top management through directorships in other firms.12

There is a rich literature exploring the role of board structure in explaining firm performance and, through

that channel, explain the effectiveness of monitoring that board members might provide. Some of these studies

have concluded that smaller boards are better boards and argue that larger boards are less effective in monitoring

because of higher distraction and co-ordination costs (see, for example, Brown and Maloney ,1998; Fich and

Shivdasani ,2006; Lipton and Lorsch ,1992 and Yermack , 1996).13 In contrast, few studies have concluded that it

is larger boards that add value to the firm (see, for example, Weisbach ,1999; Borokhovich, Parrino and Trapani

,1996, Brickley, Coles and Terry ,1994; Byrd and Hickman ,1992; Cotter, Shivdasani and Zenner ,1997 and Dalton,

Daily, Johnson and Ellstrand ,1999). Overall, the consensus seem to suggest that smaller boards are effective at

monitoring.

However, the advisory role of the boards has received relatively less attention. The early study by Mace

(1971) document that directors provide advice to the CEO. More recently, studies such as Boone , Field , Karpoff

and Raheja (2007) , Chen (2008) , Faleye , Hoitash and Hoitash (2013) , Kim , Mauldin and Patro (2014) discuss the

major importance of advisory functions of the boards. Specifically, Boone, Field, Karpoff and Raheja (2007) argue

that nature of the firm’s business environment and the degree of business complexity determines the board

composition. Coles et al.(2008) examine the advisory role of the board as a whole and show that complex firms

need greater advising. Adams (2009) argues that high quality advice from directors could contribute positively to

firm success. These studies imply that directors with more connections can be expected to have better access to

information and knowledge about the general functions of the organization. Therefore, such directors with

greater knowledge and skills as well as access to resources could be more effective in performing their advisory

duties (Kor and Sundaramurthy , 2009). In addition, a director’s exposure through multiple directorships could

lead to innovative cross-corporate ideas and the resulting strategic advising could improve a firm’s ability to create

long-term value (Faleye, Hoitash and Hoitash , 2013).

In a related stream of literature, several studies have documented the relationship between board of

director connectedness and firm performance (Larker, So and Wang , 2013; Omer, Shelley and Tice ,2014;

Haythornthwaite ,1996; Borgatti and Halgin ,2011; Phelps, Heidl and Wadhwa ,2012). Specifically, these studies

suggest that multiple directorships makes the directors better ‘connected’ and likely have larger information sets,

which can facilitate better advising. Within the board, advising literature a relatively less studied, approach is to

12 See also Dalton, Daily, Johnson and Ellstrand (1999); Adams and Mehran (2003) and Raheja (2005). 13 Additionally, Eisenberg, Sundgren, and Wells (1998); Bhagat and Black (2001) and Kini, Kracaw and Mian (1995).

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identify ex ante measures of good board advising. Thus, Coles, Daniel and Naveen (2012) develop and test two

such measures of good board advising namely a per-outside-director advising quality and an aggregate board

advising quality. The authors find that complex firms have greater demand for advising, complex firms are

correlated with both advising quality and total advising measures.14 We follow in the above footsteps and add to

the relatively sparse literature on ex ante measures of valuable board members and by extension a valuable board

by utilizing a seemingly unrelated annual event. The event is the annually held JEE for admission to the engineering

colleges in India for undergraduate education.

The above arguments suggest that identifying ex-ante measures of director’s quality can help to draw the

relationship between proportion of SD directors in a given board and performance. Specifically, our arguments

give rise to Hypotheses 1.

Hypothesis 1: SD directors will have more directorships relative to NSD directors consequently; boards with higher

fraction of SD directors will have a positive relationship with firm performance.

A related line of research, features studies (Bourdieu ,1985; Coleman ,1990; Putnam ,2000; Hillman,

Withers & Collins ,2009; Pfeffer & Salancik , 1978; Larcker and Tayan ,2010) that assess the role of networking and

attracting resources to achieve organizational goals. In his earlier work on social capital, Bourdieu (1985) argues

that network provides access to membership in a group through mutual acquaintance, recognition or credential.

Such network acts as a ‘capital’ that provides each of the group members potential resources. In addition, studies

in finance such as Cohen, Frazzini, and Malloy (2008) and Hwang and Kim (2009) use social networks to identify

information transfer in investment markets. Specifically, Cohen, Frazzini, and Malloy (2008) find that mutual fund

managers place larger bets on stocks they are socially connected to firms through the managers’ education

affiliation. Similarly, Hwang and Kim (2009) argue that social ties have large impact on director’s monetary and

disciplinary capacity and find that out of the 87% conventionally independent boards only 62% are conventionally

and socially independent suggesting that significant social ties exists even among independent boards. We draw

inferences from the above-mentioned studies that competent boards link to networks of people with common

interests, complementary skills and educational qualifications to those of their own. Such directors build

competitive advantage for the companies they represent.

14 Daniel, McConnell and Naveen (2013) identify dissimilar directors based on differences in legal regime in the country of origin, language; trust and religion in the boards of US based companies and show that these individuals are able to provide valuable advice.

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Furthermore, according to Network Theory (Katz, Lazer, Arrow and Contractor ,2004) which formalizes

the relationships between a set of actors, Wellman (1988) argues that actors’ behavior are best predicted not by

examining their drives, attitudes or demographic characteristics, but rather through the web of relationships in

which they are embedded. That web of relationships presents opportunities and imposes constraints on people’s

behavior. Thus, for example, if any two (or more) existing directors of a corporate board were successful in the

JEE in their youth they are likely to feel a kinship with one another and be more inclined to have a fellow successful

JEE candidate join their ranks on the corporate board. And the larger the fraction of such directors on the board,

the larger the collective kinship impact is likely to be on the next director appointed on the board who then is

more than likely to be another successful JEE candidate. Thus, consistent with Network Theory, we should expect

to see a positive relationship between the likelihood that a newly appointed director has a higher likelihood of

being SD, conditional on a larger fraction of existing directors on the company’s board being SD. Formally stated

our next hypothesis reads as follows:

Hypothesis 2: Boards with higher (lower) fraction of SD directors are more(less) likely to appoint a SD director.

Another body of research starts with the premise that “one board size does not fit all” and that board

sizes might be driven by the nature of advice required by a given firm. For example, Klien (1998) argues that

complex firms require higher advising than other types of firms and those firms can be complex along various

dimensions. Similarly, Zahra and Pearce (1989) identify that corporate boards, depending on firm’s complexity

play two main roles: a) advisers of internal work processes b) providers of support functions. Further, the authors

argue that firms can be complex based on internal (such as high R&D, high technology etc.) or external functions

(such as number of business segments/diversified or industry leadership). In a similar vein, Rose and Shepard

(2007) claim that diversified firms tend to be more complex because such firms operate in various markets and

business segments. In addition, Field, Karpoff and Raheja (2007) argue that firms tend to hire more outside

directors as they grow larger and more diversified (i.e. complex). These studies provide evidence that complex

firms require a significantly different board composition than simple firms.

Our research appeals specifically to the body of work that examines the advising aspects of the corporate

board and what might make for good board advisors. One approach to addressing what constitutes good advising

is to focus on a specific aspect of the board (large size versus small) and its correlation to firm performance.

Another aspect of examining board advising is to focus on the relationship between the board size as a function

of firm type. In other words, studies have explored the role of firm type (for example, simple versus complex, high

R&D versus low R&D) in determining the board size. This is the approach followed by Coles et al. (2008) who show

that complex firms are associated with larger boards because complex firms have greater advising requirements

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due to the very nature of their business (see also footnote 5 for studies that discuss on board advising).

Utilizing the insights from the above papers, we build on the notion that complex firms, due to their

sophistication, higher number of business segments and larger size commands specific director attributes.

Specifically, we argue that smart and driven (SD) directors, with their high entrepreneurial ability play a positive

role within the board by providing skills, knowledge, experience and ties to the organization. We posit that such

director classification into SD/NSD provides a direct metric to identify a board composition in complex firms. In

fact, we expect complex firms (relative to simple firms) will have a higher proportion of SD directors to enhance a

firm’s capability-building functions.15 Formally stated, we have our final testable hypothesis as:

Hypothesis 3: Complex firms are more likely to have a higher fraction of SD directors (in addition to larger boards).

3. Data and Variables

3.1. Educational qualifications data and sample construction

We obtain board members’ educational qualifications data from the Indian Boards Database provided by

the National Stock Exchange (NSE), which makes available detailed information on the boards of companies listed

at the NSE. Specifically, the Indian Boards database provides different levels of educational qualifications (for

example Bachelor’s, Master’s, Post-graduation/ PhD and professional affiliation) and the respective degree

granting university/institution for each board member.16 In addition, the database also provides the board

member’s affiliations with professional organizations (for example, Chartered Accountants association, Medical

Associations etc.) along with other characteristics of board members such as director type, gender and age. We

obtain board member data for all available firms in the database between the years 2006 and 2015 (both years

inclusive). The reason we choose to begin our investigation from 2006 onwards is because it turns out that the

coverage on director level information is very sparse prior to 2006. In addition, we find that the database covers

about 81% (average) of all the listed firms in NSE during our sample period.17 Finally, we sort and code the data at

15 Anecdotal evidence suggests that SD directors (i.e. directors who have successfully cleared JEE) are more likely to perform well and display high entrepreneurial skills through their high ability and internal drive. See, “Imported from India”, (https://www.cbsnews.com/news/imported-from-india/). Not surprisingly, we find that, boards of many large Indian organizations such as Bharti Airtel, Reliance and Infosys comprise of more than 40% of SD directors. 16 Higher education system in India classifies Bachelor’s in science, Commerce and Arts as general degree programs and engineering, law and medicine as professional degree program. In addition, the University Grants Commission (India) awards the post-graduate degrees such as Master’s degree Postgraduate diploma, M.Phil etc and PhD. 17 National Stock Exchange is the largest stock exchange in India with a market share of 86% compared to that of Bombay Stock Exchange (the second-largest stock exchange in India).

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the individual director level and then aggregate the data at the firm/board level for each year for our empirical

analyses.

To capture the ‘director’s smartness and drive at the level of firm’s board we compute two measures: the

first measure is fraction of SD directors (FractionSD), which is defined as the total number of directors holding

engineering degrees scaled by the board size in a given firm every year. Our second measure is (natural) log of the

number of SD directors (LogSD) within a given board.18

3.2. Control variables

For the control variables in our regression specifications, we take guidance from previous studies on firm

performance and corporate boards. The regressions control for firm characteristics and corporate governance

variables that are likely to affect firm performance. To do so, we obtain various financial characteristics for those

firms that are in Indian boards database from Center for Monitoring the Indian Economy’s (CMIE) Prowess

database.19 Specifically, we obtain financial variables such as total Assets, total sales, share capital, preferred share

capital, current assets, current liabilities, total liabilities, net income, depreciation, EBIDTA, long term and short-

term debt, total debt and shares outstanding. Finally, we merge the director level database with Prowess financial

database. From our final sample, we exclude state and central government owned firms and financial firms from

our analyses since regulatory effects may lead to limited decision making from board in such firms and previous

studies have documented that financial firms have high leverage.20 For identifying industry affiliation, we classify

firms into industries at a level equivalent to two-digit standard industrial classification (SIC). Our classification

yields data on firms across 55 industries.21 Further, we obtain the consumer price index (CPI) prices from the Indian

18 Jagannathan and Yan (2007) also use the natural logarithm of independent directors on a given board. Similarly, Voordeckers, Steijvers and Mercken (2008) use the natural logarithm of number of directorships held by outside directors. Also, Coles et al.(2008) use the natural log of insiders and outsiders. 19 CMIE prowess database has been previously used by studies such as Khanna and Palepu (2000), Bertrand, Mehta and Mullainathan (2002) and Gopalan, Nanda and Seru (2007), among others. 20 Fama and French (1992) also exclude financial firms because as they argue that the high leverage that is normal for these firms probably does not have the same meaning as for the non-financial firms where high leverage more likely indicates distress. 21 Prowess classifies industries as per the National Industrial Classification provided by ‘Ministry of Statistics and Program Implementation’, Government of India at the website (http://mospi.nic.in). In the current paper, we consider the first two digits of the NIC code.

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Labor Bureau. All financial values are deflated as per the CPI values provided by the Indian Labor Bureau.22 These

criteria yield 9,916 firm-year observations over our sample period.

3.2.1. Firm characteristics

In our regressions, we control for firm size (Size) using the natural log of total sales because studies such

as Ferris, Jagannathan and Pritchard (2003) conjecture that firm size might act as a proxy for the intensity of board

monitoring. We include (Leverage), calculated as (Total Debt / Total Assets) as a control for capital structure, which

is used in previous studies such as Chen and Zhao (2006), Bhagat and Bolton (2013) and Fich and Shivdasani (2006).

We note that the relationship between firm’s leverage and performance is inconclusive, for example, earlier work

by Jenson (1986) argues that profitable firms are able to raise debts, indicating a positive relationship between

leverage and firm performance. Chen and Zhao (2006) find a positive relationship between leverage and market-

to-book ratio and show that such relationship is non-monotonic. In contrast, other studies (for example, Lang,

Ofek, and Stulz ,1996; Baker and Wurgler ,2002 and Frank and Goyal ,2009) have widely documented negative

relationship between performance and leverage under the intuition that high growth firms have lower optimal

leverage ratios. Previous literature such as Gillan, Hartzell and Starks (2003) and Core, Guay and Rusticus (2006),

Fich and Shivdasani (2006) and Bhagat and Bolton (2013) suggest that growth opportunities are important

determinants of firm performance. Therefore, as an additional control our regressions include ratio of

depreciation expenditures to sales (GrowthOpp) measuring firm’s investment opportunity. Similar to Khanna and

Palepu (2000), we compute the number of business segments (Segments) a firm operates in, to capture relative

firm diversification. The return on assets (ROA) is calculated as (Net Income / Total Assets). As in Coles et al. (2008)

we compute Free Cash Flow (FCF) as the ratio of operating cash flow less preferred dividend and equity dividend

to the book value of assets and Intangible assets (Intan) as one minus the ratio of net, property, plant and

equipment to the book value of assets. Additionally, we control for percentage of insider ownership

(Insider_Ownership), which includes the stake held by the family/group associated with the firm, in order to

determine the relationship with performance similar to previous studies (for example, Chakrabarti, Megginson

and Yadav ,2008 ; Sarkar and Sarkar ,2009 and Jackling and Johl ,2009) on Indian markets and Fich and Shivdasani

(2006), McConnell and Servaes ,1990 and Morck, Shleifer and Vishny ,1988 on US markets). Consistent with

studies on corporate governance such as Loderer and Waelchli (2010), Khanna and Palepu (2000) and Fich and

Shivdasani (2006), we also include the natural log of firm age (Age) since incorporation as a control variable in our

22 The CPI values are obtained from government of India website (www.goidirectory.nic.in). The base price is 100 as per year 2006 prices.

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regressions. However, the relationship between firm performance and age is inconclusive. For example, Ferris et

al. (2003) find a positive relationship between firm age and performance whereas Loderer and Waelchli (2010)

find a negative relationship under the intuition that older firms are more rigid resulting in lower performance.

Similarly, Fich and Shivdasani (2006) for top US firms show a negative correlation between firm age and market

to book ratio.

3.2.2. Board characteristics

For our analyses, we also use several variables to control for corporate governance and board

characteristics. We define an interlocking (Interlock) variable as taking the value of one when, in a given year for

a company, its board has at least two independent/inside/CEO overlapping with another company’s board. We

include the log of the board size (Log(Boardsize)), which is computed as log of total board members in a given firm

for a year as an independent variable in our tests because studies such as Yermack (1996) document a negative

and significant association between company valuation and board size. Board composition (Composition) is

controlled for by scaling the number of outside directors by board size. We also include the average Board age

(BoardAge), which is computed as mean of all the directors’ age in a given firm each year.

3.2.3. Measurement of firm Complexity

Coles et al. (2008) build upon the findings of literature that focus on board structure and firm

characteristics (for example Hermalin and Weishbach ,1988; Yermack ,1996 and Booth and Deli ,1996) in order to

identify that firm diversification, leverage and size can act as proxies for complexity. These previous studies

suggest that diversified firms need greater advice and larger boards. Further, the authors suggest that firm

complexity is a function of three variables namely, diversification (measured through number of business

segments a firm operates in), leverage (measured as ratio of debt to total assets) and firm size (computed as log

of sales). Moreover, as firm complexity increases along these dimensions the need for higher advising increases

too and firms need larger boards. To identify the complex firms, the authors implement a factor analysis and

compute a factor score for a given firm each year in their sample. The factor score is a linear combination of

number of segments, leverage and log of sales. The resulting score is positively related to the three variables (i.e.

business segments, leverage and log of sales). Further, they classify firms with above median factor score as

complex firms and as simple firms otherwise.

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Following a similar approach as above, as well as several other studies,23 we implement a factor analysis

on Indian firms to identify the relative complexity of firms. We compute the Complex_Factor score for each firm-

year observation in our sample as a linear combination of the number of business segments (Segments), Leverage

and natural log of sales (Size). For each year based on the factor score, we classify firms as complex (simple) if the

firm has an above (below) median factor score. Complexity takes the value of one for firms with above median

factor score and zero otherwise.24

4. Results

4.1. Director characteristics in the sample data

Table 1 presents the results at the director level rather than at the board/firm-level. Panel A reports

distribution of directors’ educational qualifications and Panel B reports the frequency of directorships in our

sample data. We observe that about 83% of all the directors in our sample have a bachelor’s degree out of which

about 22% of the directors hold bachelor’s degree in engineering. This translates to about 18% of the total

directors in our sample hold bachelor’s degree in engineering (i.e. SD directors). Furthermore, in Panel A about

45% of the total directors hold at least a master’s degree (includes directors with degrees beyond master’s/ post-

graduation)) out of which, about 15% hold a bachelor’s degree in engineering (i.e. SD). We find that the SD director

distribution in our sample is comparable to Banerjee and Muley (2008). These authors, in their article on

‘Engineering Education in India’ report that historically about 15% of the all undergraduate degree holders are

engineering degree holders.

4.2 Univariate Results

In Table 2, we report the board/firm-level descriptive statistics across our sample period (2006-2015).

Also, Appendix A provides more detailed variable definitions. We find that the firms in our dataset have about

eleven board members with five outsiders and six insiders. Table 2 also reports that among the firms in our sample

the average fraction of directors holding engineering degree (i.e. SD) is 0.21. On average, there are three SD

directors in our sample firms. We find that a typical board member in our sample has three overall directorships

and an outside director holds about two directorships. We find that the numbers in Table 2 are comparable to

other studies on Indian firms for example; Jackling and Johl (2009) report a mean board size of ten for their sample

23 Gaver and Gaver (1993) implement a factor analysis to identify firm’s investment opportunity set using six variables. Similarly, Guay (1999) employs factor analysis to capture the variation in firm’s investment opportunities using three variables. See also Baber, Janakiraman and Kang (1996). 24 We thank Lalitha Naveen for sharing her factor analysis STATA code to determine the complex versus simple firms.

14

period (2004-2005), 48% of whom are outside directors. Sarkar and Sarkar (2009) in their 2003 sample report

about nine directors per board and about five average directorships per each board member. Whereas, Jackling

and Johl (2009) report about three directorships per board member. A typical age of a board member in our

dataset is about 59 years. Similarly, Chakrabarti, Subramanian and Tung (2011) report average board age of 56

years in their sample of Indian firms. 25

With regard to firm characteristics, we find that a firm in our sample is about 32 years old since

incorporation. The mean market to book ratio among our sample firms is two. We find that the ‘insiders’ hold

about 54% of common shares compared to 50% reported by Chakrabarti et al.(2011) on Bombay Stock Exchange

(BSE) listed firm sample. The firms in our sample have a mean leverage of 0.18, compared to 0.26 reported by

Gopalan, Nanda and Seru (2006) using Indian sample of firms between the periods 1989 and 2001. Table 2 also

reports that firms on average have two business segments and the overall average operating margin of firms in

our sample is 10%. In addition, we report average free cash flow and intangible assets as 0.05 and 0.64

respectively, compared to 0.08 and 0.68 reported by Coles et.al (2008) on US firms. Overall, we find that the board

characteristics and governance structure are consistent with previous studies on India companies and other

studies on US firms.

In Table 3, we present the univariate results to provide an assessment of our hypotheses. To do so, in

table 3 we classify a SD board as taking the value of one if a majority of the board members are SD (i.e. have an

engineering degree) and classified as NSD boards otherwise. In Panel A, we find that directors in SD boards, hold

significantly higher fraction of directorships (0.198 versus 0.189, two tailed p-value = 0.000) relative to NSD boards.

In addition, we also find that SD boards have more average directorships and more directorships per outside

director as compared to NSD boards. The average number of directorships is significantly higher for SD boards

(2.12 versus 1.608, two tailed t-statistics = 26.03, p-value = 0.000) relative to NSD boards. Similarly, the average

number of outside directorships is 2.682 and 1.78 in SD boards and NSD boards (t-value = 29, p-value = 0.000)

respectively. The results in Panel A suggests that SD directors are more likely to have multiple directorships than

NSD directors are. In sum, the numbers are consistent with our first hypothesis.

In Panel B we find that the market-to-book ratio is 28% higher (i.e. 1.520 versus 1.232, p-value = 0.00) in

firms associated with SD boards compared to those with NSD boards. We also find that the ROA for SD boards is

25 These numbers are comparable to studies on US firms. For example, Fich and Shivdasani (2006) report an average board size of 12 for their Forbes 500 sample firms, where 55% of the board is comprised of outside directors. Similarly, Bhagat and Black (2001) report a median board size of 11 members. In addition, similar to us Ferris et al. (2003) and Fich and Shivdasani (2006) report, about 3.1 and 1.6 directorships held by outside directors, respectively. A typical US firm in Fich and Shivdasani (2006) sample is 23 years old. Coles, Daniel and Naveen (2008) using US data report an average of 2.6 business segments.

15

greater than those firms with NSD boards (i.e. 0.59 versus 0.55, p-value = 0.03). The results in Panel B imply that

boards with majority of directors with engineering degrees contribute positively to the firm’s profitability. This

result is consistent with our first hypothesis that firms with higher fraction of SD directors have significantly better

performance.

Table 3 also reports results related to firm complexity. Our third hypothesis is that complex firms will have

larger boards and are more likely to have SD directors. To identify whether or not a firm is complex we first

compute a factor score for each firm in a given year based on the number of business segments, leverage and firm

size. Panel C in Table 3 shows that the fraction of SD directors in SD boards is significantly higher than in NSD

boards (22.07% and 19.54%, respectively p-value = 0.000). Also, in Panel C, we find that complex firms have a

significantly larger board size consistent with previous studies discussed in sub-section 3.2.3.

Overall, the results in Table 3 appear to make our point that SD boards are associated with more

directorships and the corresponding firms display better performance compared to the NSD boards. Also, complex

firms have larger boards and a large fraction of SD directors.

4.3 Multivariate results

4.3.1 Smart and Driven(SD) directors and average directorships – Director-level matched sample analysis

So far, in the univariate results in Table 2 (Panel B), we have shown that, overall, SD directors hold more

directorships than the NSD directors. In this section, we use multivariate analyses including a propensity score

matching technique, where we create a matched pair of a SD director (the treatment group) and a NSD director

(the control group) based on director age. In this matched sample (of size 5,000), we compute the average

directorships held by the SD directors and the NSD directors and compare the average directorships using a two-

tailed t test. In unreported results, we find that the SD directors hold a significantly more (mean = 1.3 versus 1.1,

p-value = 0.000) directorships compared to the NSD directors. This result is consistent with our first hypothesis

that SD directors hold more board seats relative to the NSD directors.26

4.3.2 Firm performance and Smart and Driven (SD) directors – Board-level regression analysis

In this sub-section, we further test the relationship between the presence of SD directors on a given board

with firm performance by employing multivariate regression analyses, where we control for the determinants of

26 We also create a matched sample based on director age and the number industries he currently holds directorships in. Since we use an additional criterion, our matched sample size drops to 2,667. In this case, we see that the number of directorships held by SDs is three versus 1.5 (statistically distinct at the 0.01 level). Also, note that we do not use gender as a matching criterion since in India the overwhelming majority of corporate board directors are male.

16

firm performance by including specific control variables drawn from the prior literature (as discussed in section

3.2). For our firm performance tests, we use the firm’s market to book ratio (MTB) as our dependent variable

similar to other studies such as Fich and Shivdasani (2006), Smith and Watts (1992) and Baker and Wurgler (2002).

Specifically, we compute the market to book ratio as the firm’s assets minus book equity plus market equity

divided by its assets. Additionally, in our robustness tests, we use the firm’s return on assets (ROA) as an

alternative performance measure consistent with other studies.

All our multivariate analyses are at the firm/board level. To mitigate any potentially confounding effects

associated with outliers in the data we winsorize all variables at 1st and 99th percentile values. To address the

concern of serial correlation between the residuals across the firms in various industries we use firm fixed effects,

industry dummy (classified based on the stock’s two-digit NIC code) and year dummies.

To test our first hypothesis, in Table 4 we turn to panel data estimates relating the market-to-book ratio

to fraction of SD directors and other board characteristics and financial attributes. The main explanatory variable

in Model (1) and Model (2) that captures the innate ability of the directors are , FractionSD, defined as the sum of

SD directors divided by board size and, LogSD, defined as the natural log of the number of directors with

engineering degrees for a given firm in a given year respectively, in order to. In addition, we include the relevant

controls capturing firm and board characteristics as follows:

( 1) ( 2)

{ , , , , ,

, , , , , ,t t

BoardCharacteristics

MTB f FractionSD LogBoardsize Interlock Composition InsiderOwnership

ROA ROA ROA Age Size GrowthOpp S

, , , tan, }

FirmCharacteristics

egments Leverage FCF In Yeardummies

We estimate regressions using the MTB as the dependent variable with either FractionSD or LogSD as the

main explanatory variable and the controls. In Model (1) of Table 5, we find that the estimated coefficient on

FractionSD is positive and significant at 0.01 level (coefficient estimate = 0.650). The parameter estimate implies

that an increase in the FractionSD directors has a positive relationship with the MTB. Similarly, in Model (2), we

find that the coefficient estimate on LogSD is significantly positive at the 0.01 level (coefficient estimate = 0.441).

The results from Models (1) and (2) are consistent with our first hypothesis that firms with higher a fraction of SD

directors perform better.

With respect to control variables, we find that Boardsize has a positive and significant relationship with

the MTB at 0.01 level, consistent with other studies (for example, Hillman and Dalziel ,2003 and Dalton, Daily,

Johnson, and Ellstrand ,1999) that argue that larger boards provide the necessary resources to the organization

17

and, as the organizations’ complexity increases, firms need larger boards. Similar to studies that show a negative

relationship between insider ownership and firm performance, such as Fama and Jenson (1996), Cho (1998), Short,

and Keasey (1999), we find that the coefficient for Insider_Ownership has a negative and statistically significant

relationship with firm performance at the 0.05 level. Furthermore, we see a negative relationship between

Composition and the MTB at the 0.01 level, similar to other studies on board independence and firm performance

such as Bhagat and Black (2001) and Barnhart and Rosenstein (1994). We find that Age has negative and

significant relationship with the MTB consistent with the notion that as firms get older organizational rigidities

and rent seeking could result in lower firm performance (see Fich and Shivdasani, 2006; Loderer and Waelchli,

2010). Growth opportunities is positive and significant at the 0.01 level. Overall, the coefficients on the control

variables are consistent with previous studies.

Furthermore, an interesting wrinkle within those who qualify for engineering schools through the JEE is

that not all qualifiers of the JEE are considered as equals. Namely those who qualify at the top of the list of

qualifiers are eligible to join the prestigious Indian Institute of Technologies (IITs) while those who finish lower

down in the list of qualifiers are able to join the less prestigious, but prominent nevertheless, engineering

institutions. Here the testable intuition is that those in our list who qualified for the IITs and thereby finished

relatively higher up in the JEE exam should be a relatively stronger proxy for our SD effect relative to those who

qualify relatively lower in the hierarchy. 27 Accordingly, we partition our SD dummy variable into IIT-SD directors

and Non-IIT SD directors. Next, we create SD_IIT, a binary variable that takes the value of one if a firm in a given

year has at least one SD director from the IITs and zero otherwise.28 The results are presented in Table 5, where

we re-estimate our main (firm performance) specification (i.e. Table 4) with the MTB as a dependent variable and

the SD_IIT dummy as the key independent variable. In Model (1) we find that the SD_IIT dummy variable has a

positive and significant relationship with firm performance at 0.01 level with a coefficient estimate of 0.182,

implying that firms with atleast one IIT based SD director have higher performance than firms with no IIT based

SD director. Alternatively, we also re-estimate the above specification by replacing the SD_IIT dummy variable

with the raw number of SD directors from the IITs (NUM_IIT) within a given board in a given year. We find that

the coefficient estimate for NUM_IIT is positive and significant at the 0.05 level (coefficient estimate = 0.106).29

The coefficients and significance for other control variables are consistent with those of our original model (i.e.

27 We thank one of our anonymous referees for suggesting this test. 28 In our sample data, we find that about 29% of firm-years have atleast one SD director with a IIT bachelor’s degree, out of which about 86% of the firm-years have at least two SD directors from IIT. 29 Also, if we replace NUM_IIT with natural log of (NUM_IIT) for the specification in Model (2) we find similar signs and significance (i.e. positive and significant).

18

Table 4). Thus, from this additional test we see that firms with at least one IIT based SD directors display a greater

correlation to firm performance than firms with no IIT based SD directors. In sum, we find strong support for our

idea that the act of passing the JEE entrance examination as a young adult is a strong signal of quality/drive for

the (future) corporate board members and that this proxy for ex ante director quality is in fact positively correlated

with a positive firm performance.

4.3.3 Networking effect and additional Smart and Driven (SD) director - Board-level regression analysis

To test for the ‘networking effect’ implied in our second hypothesis, we estimate an OLS regression on a

sub-sample of firms that appoint new directors in any given year within our sample period. From the subsample,

we compute Fraction_hire, defined as the ratio of number of newly appointed SD director(s) and the total number

of appointed directors on a given board. We estimate the regression with Fraction_hiret as a dependent variable

and FractionSD(t-1) and LogSD(t-1) as the key independent variables in Models (1) and (2), respectively. If the

networking effect is present we should see that the Fraction_hiret should be positively and significantly correlated

with FractionSD(t-1). That is, having more current SD directors on a board will improve the likelihood that the next

new director appointment will be a SD.

Table 7 reports the results for our regression estimates. In Model (1) we find that the coefficient estimate

for FractionSD(t-1) is positive and significant at 0.01 level (coefficient = 0.127). Thus we have evidence of a

significant networking effect, where a board hires a SD director conditional upon the existing (t-1) fraction of SD

directors (i.e. FractionSD(t-1)). In Model (2), we replace the FractionSD(t-1) with t-1 natural log of number of SD

directors (LogSD(t-1)). The coefficient estimate for LogSD(t-1) is positive and significant at 0.01 level (coefficient

estimate = 0.040). Overall, the results from Table 6 suggest that boards with a higher representation of SD

directors prefer to appoint a new SD director.30

4.3.4 Firm complexity and Smart Driven (SD) directors: Board-level regression analysis

We begin by exploring the potential relationship between board size and complexity for Indian firms. To

do so, in Model (1) of Table 8 we estimate an OLS regression with Log(Boardsize) as a dependent variable and

Complexity as the key independent variable along with other controls attributing to firm and board characteristics

as. In Model 1 we find that Complex_Factor has a positive and statistically significant relationship with Boardsize

at the 0.01 level (coefficient estimate = 0.15) implying that relatively complex Indian firms have larger boards.31

30 We further obtain the predicted probabilities estimated through the logistic regression described above and use it as a new RHS control variable in our model depicted in Table 4 as a networking proxy in our main regression model presented in Table 4. In results not formally reported, we see that even after controlling for director networking, the FractionSD and LogSD variables are strongly significant as in our original findings. 31 We also find strong positive and significant relationship between firm complexity and board size when we re-estimate the Model (1) of Table 8 by replacing Complex_Factor with the Complexity dummy variable.

19

Similarly, we find that the coefficients of the other control variables to be consistent with that of Coles et al.

(2008).

Next, we build on the above to formally test our third hypothesis. Specifically, we estimate an OLS

regression with the FractionSD as dependent variable in Models (2) and (4) and LogSD as dependent variable in

Model (3). We also include Complex_Factor as the independent variable along with other control variables that

can be determinants of FractionSD. Formally, our empirical specification to test the third hypothesis is as follows:

( 1) ( 2)

{ _ , , ,

, , , ,t t

BoardCharacteristics

FractionSD f Complex Factor LogBoardsize Interlock Composition

ROA ROA ROA Age GrowthOpp

, }

FirmCharacteristics

Yeardummies

In Model (2) we find that the coefficient estimate for Complex_Factor is positive and significant at the 0.01

level (coefficient estimate = 0.027). The parameter estimate indicates that FractionSD is about 2.7% higher for a

10% increase in Complex_Factor, suggesting that the relatively more complex firms have a higher fraction of SD

directors. In Model (3), we replace FractionSD with the log of the number of SD directors (LogSD) as the dependent

variable. Again, the coefficient estimate for Complex_Factor is positive and significant at the 0.01 level (coefficient

estimate = 0.112), suggesting that a 10% increase in firm complexity is associated with an 11% increase in SD

directors on corporate boards.32

Further, in Model (4) of Table 8 we interact Boardsize with Complex_Factor to assess their joint effect on

FractionSD. As per Hypothesis 3, the interaction variable (i.e. Complex_Factor X Boardsize) should be positive

because we argue that complex firms require greater advising through larger fraction of SD directors and larger

boards. We find that the coefficient for Complex_Factor X Boardsize is positive and significant at the 0.01 level

(coefficient estimate = 0.012). This result suggests that complex firms in addition to having higher Boardsize but

also have higher FractionSD relative to simple firms. In Table 8 we also find that, the coefficient for ROA is positive

and significant, suggesting that complex firms are more profitable than simple firms. These results are consistent

with previous studies that have looked at firm complexity and board size such as Boone, Field, Karpoff and Raheja

(2007) and Coles et al. (2008). The results from Table 8 should be placed in the context of board advising literature

(discussed in third hypothesis) that have focused on specific aspects of board (Fama and Jenson ;1983; Vance

,1978; Kesner ,1988 and Terry ,1992). Overall, the results from Table 8 provide evidence that SD directors, with

32 The signs and significance do not change when we estimate Models (2), (3) and (4) of Table 8 by replacing Complex_Factor with the Complexity dummy variable.

20

their high entrepreneurial ability play a positive role within the board by providing skills, knowledge, experience

and ties to the organization and such directors are more in the boards of complex firms.

4.3.5 Firm complexity and Smart Driven (SD) directors: Director level Propensity Score Analysis

In this sub-section, we further test our third hypothesis through a matched sample analysis. We show

that a larger proportion of complex firms appoint a SD director relative to simple firms. We first isolate those

firms where there was an increase in board size relative to the year before over our sample period. From this sub-

sample of firms, we create a matched pair of complex (i.e. treatment group) and simple firms (i.e. control group)

based on market value of equity, Size and NIC industry classification and compute a propensity score for each

complex/simple firm. The propensity score is the predicted value from a logit regression with complexity dummy

variable (takes the value one for firms with above median factor score (Complexity_Factor) and zero otherwise)

as the dependent variable and the market value of equity, Size and NIC industry classification as independent

variables. Next, we adopt a nearest neighbor match to arrive at the complex-simple pair. Specifically each complex

firm (i.e. treatment group) is matched to a simple firm (i.e. control group) with the closest propensity score. If a

firm in the control group is matched to more than one firm in the treatment group then we retain the pair with

the closest propensity scores. Further, within this matched pair of firms, we identify whether the newly appointed

director(s) is SD or NSD. Finally, we calculate the proportion of firms that appointed at least one SD director under

both treatment (i.e. complex) and control (i.e. simple) groups and perform a chi-square test of equality to identify

whether a larger proportion of complex firms appointed SD directors relative to the proportion of simple firms.

Table 9 reports the proportions for treatment and control firms. In the matched sample, we find that 61%

of the complex firms appointed at least one SD director relative to 39% of the simple firms. The proportion of

complex firms that appointed at least one SD director is significantly higher (p-value = 0.000) than the proportion

of simple firms that appointed at least one SD director. The results indicate that a significantly larger proportion

of complex firms appoint SD directors. In sum, we find that the results in table 8 and table 9 support our third

hypothesis that complex firms require a larger fraction of SD directors and that a significantly higher proportion

of complex firms appoint SD directors relative to simple firms.

5 Robustness tests

5.1 Ruling out a potentially alternative explanation of our findings

It is possible that one can interpret our findings simply as a reflection of the quality of the engineering

training (the “education effect”) that these directors received in their respective engineering schools, rather than

the individual specific effect that we are arguing for. While we do not fundamentally disagree with the notion

21

that the education effect might play a (varying) role in the future success of these individuals, our point is that the

act of passing the JEE is by itself the signal of personal quality over and above any systemic education effect.

Stated differently, these individuals could do anything else in life (in place of engineering) and still likely be

successful in life. To ensure that our analyses primarily capture this individual effect, and not the education effect,

we perform the following tests: the first at the board level and the second at an individual director level.

First, we test whether the SD directors who have earned additional degrees beyond their undergraduate

engineering degrees contribute more at the margins to increased firm performance. For example, one can argue

that SD directors learn through the formal degrees they have earned and are thereby better advisors, resulting in

those firms performing better. Indeed, if that were the case, we would expect to see that SD directors with

incremental education (like obtaining a Master’s, or a Master’s and PhD) would provide more value to the firm

(measured through incremental firm performance) relative to those SD board members who terminate their

education after obtaining their undergraduate engineering degrees. If, on the other hand, we see no significant

difference in on firm performance between the two groups of SD directors, we would reject the idea that it is

really an education effect in favor of the individual effect.

We estimate, in Table 10, fixed effect regressions using the MTB as the dependent variable and the

number of SD directors in a given company-year with only engineering degree scaled by Boardsize

(Fraction_Bachelors) and the number of SD directors who have earned advanced degrees beyond engineering

degrees scaled by Boardsize (Fraction_Advanced) as the key independent variables. We expect that

Fraction_Advanced variable should not have a marginally positive effect when Fraction_Bachelors is included if

our individual effect story is right. In Model (1) of Table 10 we find that the coefficient estimate is not significant

for Fraction Advanced (estimate = 0.149, p-value = 0.69), whereas the Fraction_Bachelors (i.e. fraction of SD

directors with terminal bachelor’s degree) is positive and significant at the 0.01 level (coefficient estimate = 1.107).

Second, we define a new ex-ante measure of SD individuals in India: identifying those directors in our

database who took (and qualified in) the Indian Civil Services examination (CSE). The CSE is conducted by the

Government of India for recruitment into the various branches of the Civil Services such as the Indian Foreign

Service (IFS); the Indian Administrative Service (IAS); the Indian Police Service (IPS), etc.33 The CSE is a standardized

exam open to any undergraduate degree holder (unlike the JEE where the examinees are typically high school

graduates) should provide another opportunity to capture personal capability/drive. For this check, we match the

33 The Civil Services Examinations are conducted in India by the Union Public Service Commission for appointment to the various civil services of the Government of India (http://www.upsc.gov.in/).

22

directors who have passed both the JEE and the CSE exams with those who have qualified in only the CSE exam.

Our point is that these two groups will be correlated with similar firm performance if our individual ability/drive

story is correct. NSD directors who have cleared the CSE (the control group) should have similar board seats

(statistically) relative to the SD directors who have also cleared the CSE (treatment group) since they are both

assumed to be SD individuals. To do so, in Table 11 from our full sample we first isolate the directors who have

cleared the CSE. Next, we create a matched sample of directors who are SD and have cleared CSE (i.e. treatment

group) with NSD directors who have also cleared CSE (i.e. control group). To match the treatment and control

groups we first estimate the probability that a director is SD. This probability (i.e. the propensity score) is the

predicted value from a logit regression with SD dummy variable (takes the value of one for directors with

engineering undergraduate degree and zero otherwise) as the dependent variable and director’s age and gender

as independent variables. Finally, we adopt a nearest neighbor match to arrive at the SD-CSE/NSD-CSE pair. We

then compare the number of board seats held by the SD-CSE directors (i.e. treatment) with those of NSD-CSE

directors (i.e. control). Table 11 presents the results for propensity score analysis. Using a two-tailed t test, we

compare the average directorships between our treatment and control groups. We find that SD directors who

have cleared CSE have on average 1.84 directorships compared to NSD directors who have cleared CSE have 1.63

directorships (p-value =0.16). From the test, we are unable to reject the null of equality at conventional levels of

significance, however the higher average board seats held by SD-CSE directors, support for our theory of drive

over the learning effect.

Overall, from the alternative tests we do not find support for incremental board member education

providing additional benefit to firm performance. We further believe that we are able to rule out any significant

educational effect and networking effect by the board members in favor of our SD directors/boards idea that is

ultimately correlated with firm performance.

5.2 Addressing Endogeneity Concerns

Prior literature in empirical corporate finance has strongly argued the presence of endogeneity issues (for

example, see Roberts and Whited ,2013; Hermalin and Weisbach ,2003; Bhagat and Black ,2001; Adams and

Ferreira ,2007). In this section, we use instrumental variable regression to mitigate any potential endogeneity

concerns. First, we attempt to find an appropriate instrument that is correlated with the endogenous variable (i.e.

fraction of SD directors) but uncorrelated with performance except through variables that are already included in

our regressions. Toward that end, we follow Cumming and Leung (2018), who argue that regional demographic

characteristics can act as a powerful proxy to predict the diversity on a board in their sample of Chinese firms.

23

Thus, we propose a measure of enrollment ratio in engineering undergraduate degrees in the cities in

which our sample firms are headquartered in, as an instrument for FactionSD. The regional enrollment ratio in

engineering undergraduate degree reflects the appointment of SD directors, because it affects the supply of SD

directors. However, the enrollment ratio is independent of firm performance except through board

characteristics. Therefore, to estimate the instrumental variable regression we first identify the enrollment ratio

for each firm by collecting the total number of enrollments in engineering undergraduate degrees and the total

number of enrollments in other undergraduate degrees in each state in India from the Government of India’s

Ministry of Human Resource Development (MHRD) over the sample period 2007 to 2011.34 Next, we compute the

fraction of engineering undergraduate enrollment to the total number of undergraduate enrollment in a given

state each year, namely Fraction_enrol. We also match the state-wise statistics with our firm level data by

identifying the city/state a firm is headquartered in. Finally, in Table 12 we replace FractionSD with Fraction_enrol

and re-estimate our firm performance specification (i.e. Table 4). 35

In Table 12, we find that the coefficient estimate for the instrumental variable (Fraction_enroll) is positive

and significant at the 0.01 level (coefficient estimate = 1.70). The signs and significance of the remaining control

variables are consistent with previous discussion (under sub-section 4.3.3).

In addition to instrumental variable regression, we note that previous studies (for example, Bhagat and

Bolton ,2008; 2013 and Bhagat and Black ,2001) that have used firm performance and board size have argued that

these two measures are driving each other. Additionally, our specific innovation in this paper, FractionSD, could

easily be considered as contributing to the same endogeneity concerns. Therefore, to ensure that our main

findings survive any potential endogeneity challenges, we follow Bhagat and Black (2001);Coles at al. (2006; 2008)

and Bhagat and Bolton (2013) by estimating a system of simultaneous equations (3SLS) with the variables MTB,

FractionSD and Boardsize as the dependent variables in a three-equation model. This procedure explicitly

accounts for the fact that firm performance, board size and the fraction of SD directors maybe endogenously

34 MHRD provides regional statistics from the fiscal year 2006-07 until 2010-11. The state-wise statistics is available in

MDRD’s website under the tab “Statistics of Higher and Technical Education” (see,

http://mhrd.gov.in/statist?field_statistics_category_tid=32&page=1 ). 35 To test whether our instrument satisfies the relevance condition we compute the partial correlation (by estimating an OLS with FractionSD as dependent variable and Fraction_enrol as a key independent variable along with other controls used in Table 4) between our endogenous variable (i.e. FractionSD) and our instrumental variable (i.e. Fraction_enrol) and find that the coefficient estimate for Fraction_enrol is positive and significant (coefficient = 0.20 , p-value = 0.000). Similarly, to test the exclusion condition we compute the covariance between Fraction_enroll and the error term in the Table 4 specification. We find that the Cov(Fraction_enroll,error term) is zero.

24

determined. We also follow a similar 3SLS approach to test our third hypothesis. We present those details in

Appendix A2. In short, our support for the first and third hypotheses survives the 3SLS estimations.

5.3 Alternative performance measure

We further supplement our finding by replacing the MTB variable with ROA as an alternative measure of

firm performance, consistent with other studies that use ROA as a dependent variable (for example, Fich and

Shaivdasani ,2006; Chen and Zhao , 2006 and Chen , 2004) .36 Specifically, in Table 13 we replace MTB with ROA

as the dependent variable and FractionSD and LogSD as a key independent variable in Models (1) and (2)

respectively. We perform the following specification as a robustness test to our original findings.

( 1) ( 2)

{ , , , , ,

, , , , ,t t

BoardCharacteristics

ROA f FractionSD LogBoardsize Interlock Composition InsiderOwnership

ROA ROA Age Size GrowthOpp Segme

, , , tan, }

FirmCharacteristics

nts Leverage FCF In Yeardummies

The results in Table 13 is consistent with those of Table 4. For example, in Model (1) we re-estimate the

main regression model using FractionSD as a key independent variable along with other control variables.

Consistent with our main results we find that the FractionSD variable has a positive and significant relationship

with ROA at 0.01 level with co-efficient estimate of 0.34. Similarly, In Model (2) we replace the FractionSD variable

with LogSD, here too we find that the coefficient has a positive and significant relationship with ROA at 0.01 level

(co-efficient estimate = 0.093). Regarding other control variables, we find that the coefficient estimates for

Boardsize is positive and significant. As in Table 4, we find that the coefficient for Composition is negative and

significant. Also, Growthopp and FCF are consistent with our firm performance specification in Table 4. Overall,

the results in Table 13 are consistent with our results in Table 4 and support our hypothesis that boards with

higher fraction of SD directors have positive association with firm performance.

36 We notice that Fich and Shivdasani (2006) define ROA as the sum of operating income before depreciation, the decrease in receivables, the decrease in inventory, the increase in current liabilities scaled over average book value of assets. Although, their ROA measure is different from our ROA measure (i.e. Net Income / Book value of assets) we find similar results when we replace our ROA variable with Fich and Shivdasani (2006) ROA measure with in the alternative specification.

25

6 Conclusion

We investigate the relationship between internal drive of directors and firm performance by utilizing a

SUAE that has been in existence in India since 1960. There is a large body of literature investigating the quality of

corporate board monitoring and advising as drivers of firm performance. We use the SUAE to ex-ante identify the

SD directors and then examine the relationship between such directors and firm performance using a large dataset

on Indian firms.

The management literature focusing on the potential relationship between corporate executives’

educational qualifications and organizational outcomes have reported that a higher educational qualification is

associated with better information processing (see, for example Hambrick and Mason ,1984; Bantel and Jackson

,1989). In addition, these studies suggest that the educational qualification of the top executives of an organization

acts as a proxy for intellectual competence. However, the important distinction that we draw from the previous

literature on educational qualifications is that we are not talking about the quality of education that the successful

might receive in these institutions. Rather, it is the fact that such directors display their inherent ability and

internal drive to outperform by successfully clearing these entrance exams that make them high quality corporate

directors later in life. Also, Indian culture with a high emphasis on education provides an interesting setting to test

the role of signaling inherent ability and drive. In addition, education in India is considered as an avenue to

economic well-being and unlike in the United States where entry to Ivy League institutions is only affordable to

the upper economic echelon, in India, when someone successfully enters one of these post-secondary educational

institutions, their tuition fees are relatively nominal and affordable by most middle-class standards since they are

almost completely subsidized by the government.

We test our ideas using a large sample of Indian board composition data consisting of firms listed on the

National Stock Exchange in India for the sample period 2006 to 2015. Overall, we find that the SD directors have

higher directorships. Further, we find a positive correlation between firm performance and the proportion of SD

directors after controlling for all relevant determinants of firm performance from related studies. We also

document that boards comprised of higher proportion of SD directors are more likely to appoint an additional SD

director (relative to a NSD director). Furthermore, we report that complex (simple) firms have higher (lower)

proportion of SD directors (NSD directors) as well as larger (smaller) board sizes.

We conclude that Indian educational setting provides a case to address the relatively less studied

approaches of measuring good board advising. In a similar spirit as Coles et al.(2008; 2013) and in Daniel

McConnell, and Naveen (2013), we add to the literature on ex ante measures of valuable board members and

26

valuable board by utilizing the annually held JEE admission to the major engineering colleges in India. Our findings

highlight the importance of using seemingly unrelated indicators of personal ability and drive to succeed (that

may have occurred much earlier in one’s life cycle) in order to identify efficient board advisors. While India

represents a somewhat extreme example of identifying SD young men and women through a unique set of cultural

norms and social values, there are other countries like Malaysia and Singapore that annually hold national level

final exams in order to determine whether a student is allowed to get into the science stream (the preferred

option and open only to those who perform at the high end of the graduation scale) or the arts stream (for those

who are below the cutoff for the science stream) in the final two years of high school prior to college. Post high

school students take another national entrance examination, which determines entrance to university and who

gets to go to which university. We find that such system is very similar to our natural experiment in India.

27

REFERENCES

Adams, R. B., & Ferreira, D. (2007). A theory of friendly boards. The Journal of Finance, 62(1), 217-250.

Adams, R., & Mehran, H. (2003). Is corporate governance different for bank holding companies?

Adams, R. B., Hermalin, B. E., & Weisbach, M. S. (2010). The role of boards of directors in corporate governance:

A conceptual framework and survey. Journal of economic literature, 48(1), 58-107.

Agrawal, A., & Knoeber, C. R. (2001). Do some outside directors play a political role? The Journal of Law and

Economics, 44(1), 179-198.

Baber, W. R., Janakiraman, S. N., & Kang, S. H. (1996). Investment opportunities and the structure of executive

compensation. Journal of Accounting and Economics, 21(3), 297-318.

Baker, M., & Wurgler, J. (2002). Market timing and capital structure. the Journal of finance, 57(1), 1-32.

Banerjee, R., & Muley, V. (2008). Report on Engineering Education in India Observer Research Foundation.

Bantel, K. A., & Jackson, S. E. (1989). Top management and innovations in banking: Does the composition of the

top team make a difference?. Strategic management journal, 10(S1), 107-124.

Barnhart, S. W., & Rosenstein, S. (1998). Board composition, managerial ownership, and firm performance: An

empirical analysis. Financial review, 33(4), 1-16.

Basu, K. (2017). Discrimination as focal point: markets and group identity. Paper presented at the Forum for

Social Economics.

Bertrand, M., & Schoar, A. (2003). Managing with style: The effect of managers on firm policies. The Quarterly

Journal of Economics, 118(4), 1169-1208.

Bhagat, S., & Black, B. (1999). The uncertain relationship between board composition and firm performance. The

Business Lawyer, 921-963.

Bhagat, S., Bolton, B., & Subramanian, A. (2010). CEO education, CEO turnover, and firm performance.

Bhagat, S., & Bolton, B. (2013). Director ownership, governance, and performance. Journal of Financial and

Quantitative Analysis, 48(1), 105-135.

Bhagat, S., & Jefferis, R. H. (2005). The econometrics of corporate governance studies. Mit Press.

Boone, A. L., Field, L. C., Karpoff, J. M., & Raheja, C. G. (2007). The determinants of corporate board size and

composition: An empirical analysis. Journal of Financial Economics, 85(1), 66-101.

Booth, J. R., & Deli, D. N. (1996). Factors affecting the number of outside directorships held by CEOs. Journal of

Financial Economics, 40(1), 81-104.

Borgatti, S. P., & Halgin, D. S. (2011). Analyzing affiliation networks. The Sage handbook of social network

analysis, 1, 417-433.

Borokhovich, K. A., Parrino, R., & Trapani, T. (1996). Outside directors and CEO selection. Journal of Financial and

28

Quantitative Analysis, 31(3), 337-355.

Bourdieu, P. (1985). The social space and the genesis of groups. Information (International Social Science

Council), 24(2), 195-220.

Brickley, J. A., Coles, J. L., & Terry, R. L. (1994). Outside directors and the adoption of poison pills. Journal of

Financial Economics, 35(3), 371-390.

Brickley, J. A., Linck, J. S., & Coles, J. L. (1999). What happens to CEOs after they retire? New evidence on career

concerns, horizon problems, and CEO incentives. Journal of Financial Economics, 52(3), 341-377.

Brickley, J. A., & Zimmerman, J. L. (2010). Corporate governance myths: comments on Armstrong, Guay, and

Weber. Journal of Accounting and Economics, 50(2-3), 235-245.

Brown, W., & Maloney, M. (1999). Exit, voice, and the role of corporate directors: Evidence from acquisition

performance.

Business Roundtable, 1997, Statement on corporate governance, Washington, DC.

Byrd, J., & Hickman, K. (1992). The case for independent outside directors. Journal of Applied Corporate Finance,

5(3), 78-82.

Cashman, G. D., Gillan, S. L., & Jun, C. (2012). Going overboard? On busy directors and firm value. Journal of

Banking & Finance, 36(12), 3248-3259.

Chakrabarti, R., Megginson, W., & Yadav, P. K. (2008). Corporate governance in India. Journal of Applied

Corporate Finance, 20(1), 59-72.

Chakrabarti, R., Subramanian, K., & Tung, F. (2012). The Firm-value of Information possessed by Independent

Directors: Evidence from a Natural Experiment. Boston University Working paper No. 12-21

Chen, L., & Zhao, X. (2006). On the relation between the market-to-book ratio, growth opportunity, and leverage

ratio. Finance Research Letters, 3(4), 253-266.

Chen, J., Leung, W. S., & Goergen, M. (2017). The impact of board gender composition on dividend

payouts. Journal of Corporate Finance, 43, 86-105.

Cho, M. H. (1998). Ownership structure, investment, and the corporate value: an empirical analysis. Journal of

financial economics, 47(1), 103-121.

Cohen, L., Frazzini, A., & Malloy, C. (2008). The small world of investing: Board connections and mutual fund

returns. Journal of Political Economy, 116(5), 951-979.

Coles, J. L., Daniel, N. D., & Naveen, L. (2008). Boards: Does one size fit all? Journal of Financial Economics, 87(2),

329-356.

Coles, J. L., Daniel, N. D., & Naveen, L. (2015). Board groupthink. Unpublished working paper. Drexel University.

Coleman, J. (1990). Foundations of Social Theory. Cambridge: Belknap Press.

29

Core, J., & Guay, W. (1999). The use of equity grants to manage optimal equity incentive levels. Journal of

Accounting and Economics, 28(2), 151-184.

Core, J. E., Holthausen, R. W., & Larcker, D. F. (1999). Corporate governance, chief executive officer

compensation, and firm performance1. Journal of Financial Economics, 51(3), 371-406.

Cotter, J. F., Shivdasani, A., & Zenner, M. (1997). Do independent directors enhance target shareholder wealth

during tender offers? Journal of Financial Economics, 43(2), 195-218.

Cumming, D. J., & Leung, T. Y. (2018). Diversity and Innovation: The Differential Role of Scope Versus Levels.

Available at SSRN

Dalton, D. R., Daily, C. M., Johnson, J. L., & Ellstrand, A. E. (1999). Number of directors and financial

performance: A meta-analysis. Academy of Management journal, 42(6), 674-686.

Dalziel, J. (2007). Implementing learning design: the Learning Activity Management System (LAMS).

Daniel, N., McConnell, J., & Naveen, L. (2013). The advisory role of foreign directors in US firms.

Denis, D. J., Denis, D. K., & Sarin, A. (1997). Agency problems, equity ownership, and corporate diversification.

the Journal of finance, 52(1), 135-160.

Duchin, R., Matsusaka, J. G., & Ozbas, O. (2010). When are outside directors effective?. Journal of financial

economics, 96(2), 195-214.

Eisenberg, T., Sundgren, S., & Wells, M. T. (1998). Larger board size and decreasing firm value in small firms1.

Journal of financial economics, 48(1), 35-54.

Falato, A., Kadyrzhanova, D., & Lel, U. (2014). Distracted directors: Does board busyness hurt shareholder

value?. Journal of Financial Economics, 113(3), 404-426.Faleye, O., Hoitash, R., & Hoitash, U. (2011). The

costs of intense board monitoring. Journal of Financial Economics, 101(1), 160-181.

Fama, E., & Jensen, M. C. (1983). Separation of ownership and control, Journal of Law and Economics. v. 26:

1983a.

Fama, E., & Jensen, M. (1996). Organizational forms and investment decisions. The economic nature of the firm,

336-344.

Fama, E. F. (1980). Agency problems and the theory of the firm. Journal of political economy, 88(2), 288-307.

Ferreira, D., Ferreira, M. A., & Raposo, C. C. (2011). Board structure and price informativeness. Journal of

Financial Economics, 99(3), 523-545.

Ferris, S. P., Jagannathan, M., & Pritchard, A. C. (2003). Too busy to mind the business? Monitoring by directors

with multiple board appointments. the Journal of finance, 58(3), 1087-1111.

Ferris, S. P., & Yan, X. S. (2007). Do independent directors and chairmen matter? The role of boards of directors

in mutual fund governance. Journal of Corporate Finance, 13(2-3), 392-420.

30

Fich, E. M. (2005). Are some outside directors better than others? Evidence from director appointments by

Fortune 1000 firms. The Journal of Business, 78(5), 1943-1972.

Fich, E. M., & Shivdasani, A. (2005). The impact of stock‐option compensation for outside directors on firm value.

The Journal of Business, 78(6), 2229-2254.

Fich, E. M., & Shivdasani, A. (2006). Are busy boards effective monitors? the Journal of finance, 61(2), 689-724.

Field, L., Lowry, M., & Mkrtchyan, A. (2013). Are busy boards detrimental? Journal of Financial Economics,

109(1), 63-82.

Fracassi, C. (2016). Corporate finance policies and social networks. Management Science, 63(8), 2420-2438.

Fracassi, C., & Tate, G. (2012). External networking and internal firm governance. the Journal of finance, 67(1),

153-194.

Frank, M. Z., & Goyal, V. K. (2009). Capital structure decisions: which factors are reliably important?. Financial

management, 38(1), 1-37.

Gaver, J. J., & Gaver, K. M. (1993). Additional evidence on the association between the investment opportunity

set and corporate financing, dividend, and compensation policies. Journal of Accounting and Economics,

16(1-3), 125-160.

Gopalan, R., Nanda, V., & Seru, A. (2007). Affiliated firms and financial support: Evidence from Indian business

groups. Journal of Financial Economics, 86(3), 759-795.

Guay, W. R. (1999). The sensitivity of CEO wealth to equity risk: an analysis of the magnitude and determinants.

Journal of Financial Economics, 53(1), 43-71.

Guedj, I., & Barnea, A. (2009). Director networks. Available on SSRN

Hambrick, D. C., Cho, T. S., & Chen, M.-J. (1996). The influence of top management team heterogeneity on firms'

competitive moves. Administrative science quarterly, 659-684.

Hambrick, D. C., & Mason, P. A. (1984). Upper echelons: The organization as a reflection of its top managers.

Academy of management review, 9(2), 193-206.

Hartzell, J. C., & Starks, L. T. (2003). Institutional investors and executive compensation. The journal of

finance, 58(6), 2351-2374.

Hausman, J. A. (1978). Specification tests in econometrics. Econometrica: Journal of the econometric society,

1251-1271.

Haythornthwaite, C. (1996). Social network analysis: An approach and technique for the study of information

exchange. Library & information science research, 18(4), 323-342.

Hillman, A. J., & Dalziel, T. (2003). Boards of directors and firm performance: Integrating agency and resource

dependence perspectives. Academy of management review, 28(3), 383-396.

31

Hillman, A. J., Withers, M. C., & Collins, B. J. (2009). Resource dependence theory: A review. Journal of

management, 35(6), 1404-1427.

Hwang, B. H., & Kim, S. (2009). It pays to have friends. Journal of financial economics, 93(1), 138-158.

Jackling, B., & Johl, S. (2009). Board structure and firm performance: Evidence from India's top companies.

Corporate Governance: An International Review, 17(4), 492-509.

Jensen, M. C. (1993). The modern industrial revolution, exit, and the failure of internal control systems. the

Journal of finance, 48(3), 831-880.

Katz, N., Lazer, D., Arrow, H., & Contractor, N. (2004). Network theory and small groups. Small group

research, 35(3), 307-332.

Kesner, I. F. (1988). Directors' characteristics and committee membership: An investigation of type, occupation,

tenure, and gender. Academy of Management journal, 31(1), 66-84.

Kim, K., Mauldin, E., & Patro, S. (2014). Outside directors and board advising and monitoring performance.

Journal of Accounting and Economics, 57(2), 110-131.

Kini, O., Kracaw, W., & Mian, S. (1995). Corporate takeovers, firm performance, and board composition. Journal

of Corporate Finance, 1(3-4), 383-412.

Khanna, T., & Palepu, K. (2000). Is group affiliation profitable in emerging markets? An analysis of diversified

Indian business groups. The journal of finance, 55(2), 867-891.

Klein, A. (1998). Firm performance and board committee structure. The Journal of Law and Economics, 41(1),

275-304.

Kor, Y. Y., & Sundaramurthy, C. (2009). Experience-based human capital and social capital of outside

directors. Journal of Management, 35(4), 981-1006.

Lang, L., Ofek, E., & Stulz, R. M. (1995). Leverage, investment, and firm growth (No. w5165). National Bureau of

Economic Research.

Larcker, D., & Tayan, B. (2015). Corporate governance matters: A closer look at organizational choices and their

consequences: Pearson Education.

Larcker, D. F., So, E. C., & Wang, C. C. (2011). Boardroom Centrality and Stock Returns [Z].

Lehn, K. M., Patro, S., & Zhao, M. (2009). Determinants of the size and composition of US corporate boards:

1935‐2000. Financial Management, 38(4), 747-780.

Linck, J. S., Netter, J. M., & Yang, T. (2008). The determinants of board structure. Journal of financial economics,

87(2), 308-328.

Lipton, M., & Lorsch, J. W. (1992). A modest proposal for improved corporate governance. The Business Lawyer,

32

59-77.

Ma, M. S. (2014). Are We Really Doing What We Think We Are Doing? A Note on Finite-Sample Estimates of

Two-Way Cluster-Robust Standard Errors.

Masulis, R. W., Wang, C., & Xie, F. (2012). Globalizing the boardroom—The effects of foreign directors on

corporate governance and firm performance. Journal of Accounting and Economics, 53(3), 527-554.

McConnell, J. J., & Servaes, H. (1990). Additional evidence on equity ownership and corporate value. Journal of

Financial economics, 27(2), 595-612.

Morck, R., Shleifer, A., & Vishny, R. W. (1988). Management ownership and market valuation: An empirical

analysis. Journal of Financial Economics, 20, 293-315.

Naveen, L., Daniel, N., & McConnell, J. (2013). The advisory role of foreign directors in US firms.

Omer, T., Shelley, M., & Tice, F. (2016). Do director networks matter for financial reporting quality? Evidence

from restatements. Available at SSRN.

Petersen, M. A. (2009). Estimating standard errors in finance panel data sets: Comparing approaches. The

Review of Financial Studies, 22(1), 435-480.

Pfeffer, J. (1972). Size and composition of corporate boards of directors: The organization and its environment.

Administrative science quarterly, 218-228.

Phelps, C., Heidl, R., & Wadhwa, A. (2012). Knowledge, networks, and knowledge networks: A review and

research agenda. Journal of management, 38(4), 1115-1166.

Putnam, R. D. (2001). Bowling alone: The collapse and revival of American community. Simon and Schuster.

Raheja, C. G. (2005). Determinants of board size and composition: A theory of corporate boards. Journal of

Financial and Quantitative Analysis, 40(2), 283-306.

Roberts, M. R., & Whited, T. M. (2013). Endogeneity in empirical corporate finance1. In Handbook of the

Economics of Finance (Vol. 2, pp. 493-572). Elsevier.

Salancik, G. R., & Pfeffer, J. (1978). A social information processing approach to job attitudes and task

design. Administrative science quarterly, 224-253.

Sarkar, J., & Sarkar, S. (2009). Multiple board appointments and firm performance in emerging economies:

Evidence from India. Pacific-Basin Finance Journal, 17(2), 271-293.

Short, H., & Keasey, K. (1999). Managerial Ownership and the Performance of Firms: Evidence from the

UK. Journal of corporate finance, 5(1), 79-101.

Smith Jr, C. W., & Watts, R. L. (1992). The investment opportunity set and corporate financing, dividend, and

compensation policies. Journal of Financial Economics, 32(3), 263-292.

Smith, K. G., Smith, K. A., Olian, J. D., Sims Jr, H. P., O'Bannon, D. P., & Scully, J. A. (1994). Top management team

33

demography and process: The role of social integration and communication. Administrative science

quarterly, 412-438.

Spence, A. (1973). Job Market Signaling. The Quarterly Journal of Economics, 87(3), 355-374.

Subrahmanyam, V., Rangan, N., & Rosenstein, S. (1997). The role of outside directors in bank acquisitions.

Financial Management, 23-36.

Tangri, S. S. (1961). Intellectuals and Society in Nineteenth-Century India. Comparative Studies in Society and

History, 3(4), 368-394.

Thompson, S. B. (2011). Simple formulas for standard errors that cluster by both firm and time. Journal of

Financial Economics, 99(1), 1-10.

Vance, S. C. (1964). Boards of directors: Structure and performance: School of Business Administration,

University of Oregon.

Varma, R., & Kapur, D. (2013). Comparative analysis of brain drain, brain circulation and brain retain: A case

study of Indian institutes of technology. Journal of Comparative Policy Analysis: Research and Practice,

15(4), 315-330.

Voordeckers, W., Steijvers, T., & Mercken, R. (2008). Multiple directorships in unlisted medium-sized firms.

Weisbach, M. (1999). 'Outside Directors and CEO Turnover', Journal of Financial Economics, 20, 431-60.

INTERNATIONAL LIBRARY OF CRITICAL WRITINGS IN ECONOMICS, 103, 347-376.

Wellman, B., & Berkowitz, S. D. (Eds.). (1988). Social structures: A network approach (Vol. 2). CUP Archive.

Wiersema, M. F., & Bantel, K. A. (1992). Top management team demography and corporate strategic change.

Academy of Management journal, 35(1), 91-121.

Yermack, D. (1996). Higher market valuation of companies with a small board of directors. Journal of Financial

Economics, 40(2), 185-211.

Yermack, D. (2004). Remuneration, retention, and reputation incentives for outside directors. the Journal of

finance, 59(5), 2281-2308.

Zahra, S. A., & Pearce, J. A. (1989). Boards of directors and corporate financial performance: A review and

integrative model. Journal of management, 15(2), 291-334.

34

Table 1: Distribution of director education (director-level) (Sample period: 2006-2015)

This table presents director level distribution of educational qualification in our sample data. The sample consists of those firms available on Indian Boards database from National Stock Exchange (NSE) during the sample period 2006 to 2015.

Distribution of educational qualifications of directors

Educational Qualification Total number of directors

Percentage of total

directors

Total number of independent

directors

Percentage of total independent directors

Total Directors 15,924 7,115 Directors with bachelor's degree 13,230 83% 6,253 88% Directors with bachelor’s degree in engineering (SD)

2,923 18 % 1,171 16%

Masters or Post Graduation 7,157 45% 3,571 50% Engineering undergraduate with Master’s degree

1,122 7% 446 6%

35

Table 2: Summary statistics (firm – level)

(Sample period: 2006-2015) This table provides board/firm-level descriptive statistics for characteristics of our sample firms. The sample consists of 9,916 observations on all Indian firms that are publicly traded on the National Stock Exchange between 2006 and 2015. The data is obtained from the Indian Boards Database, Centre for Monitoring the Indian Economy (CMIE) and COMPUSTAT. The sample excludes financial and government owned firms. The table presents the mean, median, 25th percentile, 75th percentile and standard deviation (SD) for each variable. FractionSD is computed as number of directors with engineering degree scaled over board size in a given firm each year. Independent directors is computed as total outside directors scaled over the board size. Operating margin is the ratio of Operating Income before Depreciation and total assets. Market to book ratio is computed as assets minus book equity plus market equity all divided by assets. Leverage is defined as total debt scaled over total assets. Growth opportunities is defined as the ratio of depreciation expenditures to sales. Number of business segments a firm operates in based on National Industry Classification (NIC) code. Free Cash Flow is defined as the ratio of operating cash flow less preferred dividend and equity dividend to the book value of assets and Intangible assets is defined as one minus the ratio of net, property, plant and equipment to book value of assets.

Mean Median 25th percentile 75th percentile SD

Board characteristics

Boardsize 11 11 8 14 5 FractionSD 0.21 0.18 0.08 0.32 0.17 Number of SD directors 3 2 1 4 2 Outsider directors 5 5 4 7 3 Independent directors (%) 48 50 40 58 17 Avgdir 3 2 1 4 3 AvgOutdir 2 2 1 3 1 BoardAge 59 59 56 63 6 Firm characteristics

Complex_Factor -0.01 -0.07 -0.27 0.17 0.45 Age 32 25 18 40 21 MTB 2 1 1 1 1.31 Assets (in millions of rupees) 28,064 5,070 1,862 15,218 61,068 Sales (in millions of rupees) 18,691 3,961 1,210 11,478 34,407 Segn 2 1 1 2 2 Leverage 0.18 0.13 0.02 0.28 0.18 ROA 0.03 0.03 0 0.08 0.1 Operating margin (%) 10 10 5 15 0.09 Insiders holding (as % of common) 53.23 54.1 43.16 65.3 15 GrowthOpp 0.19 0.03 0.02 0.06 0.09 FCF 0.05 0.05 0 0.1 0.09 Intan 0.64 0.66 0.49 0.81 0.21

36

Table 3: Univariate results (firm – level)

(Sample period: 2006-2015) This table presents board/firm-level univariate results for our hypotheses. Panel A reports the means of performance measures such as Market to book ratio and Excess returns between SD boards and NSD boards. We classify a board as SD if majority of the board members are SD. In Panel B we report fraction of SD director; computed as number of directors with engineering degree scaled over board size in a given firm each year, average number of directorships and the average number of directorships held by independent directors for firms based on whether they have SD boards or NSD boards. In Panel C, we report the means of fraction of SD directors, board size, total assets and leverage between complex and simple firms. To classify firms as complex we compute a factor score based on Segments, Size and Leverage and classify firms with above median factor score as complex. The significance is based on two-sided t-tests of difference in means. ∗, ∗∗, and ∗∗∗ denote statistical significance at the 10%, 5%, and 1% levels respectively.

Panel A: Do SD boards have higher directorships and higher fraction of directorships than NSD boards SD Boards Non-SD Boards

Avgdir 2.123*** 1.608

Panel B: Do SD boards have higher performance? SD Boards Non-SD Boards

MTB 1.520*** 1.232

ROA 0.59** 0.55

Panel C: Do complex firms have higher SD directors? Complex Firms Simple Firms

FractionSD 0.220*** 0.195

Total Assets (in millions of rupees) 45,795*** 5,449

Leverage 0.227*** 0.132

37

Table 4: Firm performance and SD directors (firm – level) (Sample period: 2006-2015)

This table presents firm-level fixed effects regressions between MTB and FractionSD. Both Models (1) and (2) use MTB as a dependent

variable. Model (1) and Model (2) use FractionSD and LogSD for a given firm as a key independent variable respectively. Interlock is

defined as two companies’ boards are interlocked in each year when the second company’s board has at least two board members

overlapping with the first company’s board in each. Composition is defined as number of independent directors scaled by total board

size for a given firm. ROA is defined as net income over total assets. GrowthOpp is defined as the ratio of depreciation expenditures

to sales. Segments is defined as number of business segments a firm operates in based on National Industry Classification(NIC) code.

Size is defined as log of total sales. Leverage is defined as total debt scaled over total assets. FCF is defined as the ratio of operating

cash flow less preferred dividend and equity dividend to the book value of assets and Intan is defined as one minus the ratio of net,

property, plant and equipment to book value of assets. The standard errors are heteroscedasticity-adjusted. ∗, ∗∗, and ∗∗∗ denote

statistical significance at the 10%, 5%, and 1% levels, respectively. Both dependent and independent variables are winsorized at 1%

and 99%.

Dependent Variable MTB

Model (1) Model (2)

Parameter Estimate (p-value)

Estimate (p-value)

FractionSD 0.650**

(0.017)

LogSD 0.441*** (0.000)

Interlock -0.082 -0.092 (0.178) (0.130)

Log(Boardsize) 0.477*** 0.187 (0.000) (0.101)

Insider_Ownshership -0.005** -0.005** (0.034) (0.027)

Composition -0.958*** -0.952***

(0.000) (0.000)

ROA 0.036 0.017

(0.823) (0.913)

ROA (t-1) 0.286 0.284

(0.173) (0.175)

ROA (t-2) 0.101 0.087

(0.621) (0.670)

Age -0.668*** -0.590***

(0.001) (0.004)

GrowthOpp 0.026*** 0.026*** (0.000) (0.000)

Segments -0.111* -0.108* (0.058) (0.064)

Size 0.025 0.019

(0.340) (0.457)

Leverage 0.389*** 0.395*** (0.001) (0.001)

FCF 0.110 0.109

(0.398) (0.401)

Intan 0.213* 0.203 (0.090) (0.106)

Intercept, industry, and year dummies Yes Yes Firm Fixed effects Yes Yes Number of observations 6,141 6,141 R2 (Pseudo-R2) 0.780 0.782

38

Table 5: Firm performance and SD directors – supplementary test (firm – level) (Sample period: 2006-2015)

This table presents firm-level fixed effects regressions of MTB and SD_IIT dummy variable, which takes the value of one if a firm has atleast one SD director with an undergraduate degree from IIT and zero otherwise. Both Models (1) and (2) use MTB as a dependent variable. Model (1) use SD_IIT and Model (2) uses NUM_IIT directors for a given firm as key independent variables respectively. Interlock is defined as two companies’ boards are interlocked in each year when the second company’s board has at least two board members overlapping with the first company’s board in each. Composition is defined as number of independent directors scaled by total board size for a given firm. ROA is defined as net income over total assets. GrowthOpp is defined as the ratio of depreciation expenditures to sales. Segments is defined as number of business segments a firm operates in based on National Industry Classification(NIC) code. Size is defined as log of total sales. Leverage is defined as total debt scaled over total assets. FCF is defined as the ratio of operating cash flow less preferred dividend and equity dividend to the book value of assets and Intan is defined as one minus the ratio of net, property, plant and equipment to book value of assets. The regression standard errors are heteroscedasticity-adjusted. ∗, ∗∗, and ∗∗∗ denote statistical significance at the 10%, 5%, and 1% levels, respectively. Both dependent and independent variables are winsorized at 1% and 99%.Dependent variable

Dependent variable: MTB

Parameter

Model (1) Model (2)

Estimate Estimate

(p-value) (p-value)

SD_IIT 0.180***

(0.016)

NUM_IIT 0.302*** (0.001)

Interlock -0.076 -0.076 (0.210) (0.212)

Log(Boardsize) 0.428*** 0.386*** (0.000) (0.000)

Insider_Ownshership -0.005** -0.005**

(0.032) (0.030)

Composition -0.957*** -0.968***

(0.000) (0.000)

ROA 0.036 0.037

(0.822) (0.814)

ROA (t-1) 0.292 0.299

(0.164) (0.154)

ROA (t-2) 0.128 0.130

(0.534) (0.527)

Age -0.670*** -0.655***

(0.001) (0.001)

GrowthOpp 0.026*** 0.026*** (0.000) (0.000)

Segments -0.118** -0.122** (0.044) (0.038)

Size 0.027 0.023

(0.307) (0.383)

Leverage 0.388*** 0.383*** (0.001) (0.001)

FCF 0.114 0.122

(0.382) (0.347)

Intan 0.202 0.200

(0.107) (0.112) Intercept, industry, and year dummies Yes Yes Firm Fixed effects Yes Yes Number of observations 6,141 6,141 R2 (Pseudo-R2) 0.84 0.78

39

Table 6 – Networking hypothesis (firm – level) (Sample period: 2006-2015) This table presents the relationship between a newly appointed director being a SD director and a board’s existing FractionSD(t-1). The

dependent variable is the ratio of newly appointed SD directors and total number of newly appointed directors. Model (1) and Model

(2) use FractionSD(t-1) and LogSD (t-1) for a given firm as a key independent variable respectively. Interlock is defined as two companies’

boards are interlocked in each year when the second company’s board has at least two board members overlapping with the first

company’s board in each. Composition is defined as number of independent directors scaled by total board size for a given firm. ROA

is defined as net income over total assets. GrowthOpp is defined as the ratio of depreciation expenditures to sales. Segments is defined

as number of business segments a firm operates in based on National Industry Classification(NIC) code. Size is defined as log of total

sales. Leverage is defined as total debt scaled over total assets. FCF is defined as the ratio of operating cash flow less preferred dividend

and equity dividend to the book value of assets and Intan is defined as one minus the ratio of net, property, plant and equipment to

book value of assets. The regression standard errors are heteroscedasticity-adjusted and include industry and year dummies. Also the

errors are clustered by firm. ∗, ∗∗, and ∗∗∗ denote statistical significance at the 10%, 5%, and 1% levels, respectively. Both dependent

and independent variables are winsorized at 1% and 99%.

Dependent Variable Fraction of new SD Model (1) Model (2)

Parameter Estimate Estimate (p-value) (p-value)

Intercept -0.228*** -0.168**

(0.001) (0.018)

FractionSD(t-1) 0.127***

(0.003)

LogSD (t-1) 0.040*** (0.000)

Interlock 0.006 0.006

(0.664) (0.690)

Log(Boardsize) 0.012 -0.016

(0.505) (0.437)

Insider_Ownshership 0.000 0.000

(0.347) (0.336)

Composition 0.013 0.017

(0.732) (0.668)

ROA 0.115** 0.112** (0.029) (0.033)

ROA (t-1) -0.050 -0.051 (0.665) (0.660)

ROA (t-2) 0.084 0.084

(0.491) (0.494)

Age 0.011 0.011

(0.306) (0.298)

GrowthOpp 0.000 0.000

(0.918) (0.901)

Segments 0.005** 0.005** (0.022) (0.023)

Size 0.016*** 0.015*** (0.000) (0.000)

Leverage 0.017 0.015

(0.666) (0.694)

FCF 0.049 0.047

(0.483) (0.504)

Intan 0.040 0.040

(0.265) (0.270) Intercept, industry, and year dummies Yes Yes Number of observations 2,947 2,947 R2 (Pseudo-R2) 0.78 0.782

40

Table 7: Firm performance and SD directors after controlling for networking (firm – level) (Sample period: 2006-2015) This table presents the results from regression for MTB on FractionSD directors after controlling for networking. In this table, we estimate firm fixed effect regressions with MTB as a dependent variable. In Model (1) we estimate the relation between FractionSD as key independent variable. In Model (2), we use LogSD as key independent variable. Interlock is defined as two companies’ boards are interlocked in each year when the second company’s board has at least two board members overlapping with the first company’s board in each. Composition is defined as number of independent directors scaled by total board size for a given firm. ROA is defined as net income over total assets. GrowthOpp is defined as the ratio of depreciation expenditures to sales. Segments is defined as number of business segments a firm operates in based on National Industry Classification(NIC) code. Size is defined as log of total sales. Leverage is defined as total debt scaled over total assets. FCF is defined as the ratio of operating cash flow less preferred dividend and equity dividend to the book value of assets and Intan is defined as one minus the ratio of net, property, plant and equipment to book value of assets. The regression standard errors are heteroscedasticity-adjusted and include year dummies. ∗, ∗∗, and ∗∗∗ denote statistical significance at the 10%, 5%, and 1% levels, respectively. Both dependent and independent variables are winsorized at 1% and 99%.

Dependent variable MTB

Parameter Model (1) Estimate (p-value)

Model (2) Estimate (p-value)

FractionSD 9.016***

(0.000)

LogSD 0.821*** (0.000)

Networking -27.414*** -5.428***

(0.000) (0.000)

Interlock -0.085 -0.083 (0.163) (0.172)

Log(Boardsize) 0.395*** -0.080

(0.000) (0.548)

Insider_Ownshership -0.005** -0.005**

(0.020) (0.022)

Composition -0.974*** -0.950***

(0.000) (0.000)

ROA 0.035 0.011

(0.825) (0.946)

ROA (t-1) 0.299 0.291

(0.154) (0.164)

ROA (t-2) 0.103 0.108

(0.617) (0.598)

Age -0.643*** -0.562***

(0.002) (0.006)

GrowthOpp 0.026*** 0.026*** (0.000) (0.000)

Segments -0.108* -0.101*

(0.066) (0.084)

Size 0.022 0.018

(0.396) (0.499)

Leverage 0.383*** 0.396*** (0.001) (0.001)

FCF 0.101 0.105

(0.436) (0.419)

Intan 0.207* 0.186 (0.099) (0.139)

Intercept, industry, and year dummies Yes Yes Firm Fixed effects Yes Yes Number of observations 2,947 2,947 R2 (Pseudo-R2) 0.781 0.783

41

Table 8 - Firm complexity and SD directors – regression analysis (firm – level) Sample period: 2006-2015

This table presents regressions to test or third hypothesis. In this table, we estimate the relationship between Boardsize and

Complex_Factor in Model (1). In Models (2-4) we test the relationship between SD directors and firm complexity. In Model (1) we estimate

an OLS regression with Log (Boardsize) as a dependent variable and Complex_Factor as a key independent variable. In Models (2, 4) and

Model (3), we estimate using FractionSD and LogSD as dependent variables respectively. In addition, we include Complex_Factor as a key

independent variable in Models (2) and (3) and an interaction variable between Complex_Factor and Log (Boardsize) in Model (4). Interlock

is defined as two companies’ boards are interlocked in each year when the second company’s board has at least two board members

overlapping with the first company’s board in each. Composition is defined as number of independent directors scaled by total board size

for a given firm. ROA is defined as net income over total assets. GrowthOpp is defined as the ratio of depreciation expenditures to sales.

Segments is defined as number of business segments a firm operates in based on National Industry Classification(NIC) code. FCF is defined

as the ratio of operating cash flow less preferred dividend and equity dividend to the book value of assets and Intan is defined as one minus

the ratio of net, property, plant and equipment to book value of assets. The regression standard errors are heteroscedasticity-adjusted

and include industry and year dummies. Also the errors are clustered by firm. ∗, ∗∗, and ∗∗∗ denote statistical significance at the 10%, 5%,

and 1% levels, respectively. Both dependent and independent variables are winsorized at 1% and 99%.

Dependent Variable Log(Boardsize) FractionSD LogSD FactionSD Model (1) Model (2) Model (3) Model (4)

Parameter Estimate Estimate Estimate Estimate (p-value) (p-value) (p-value) (p-value)

Intercept 2.177*** 0.005 -1.280*** 0.002 (0.000) (0.917) (0.000) (0.969) Complex_Factor 0.145*** 0.027** 0.112*** (0.000) (0.018) (0.006)

Complex_Factor X Log (Boardsize) 0.011*** (0.010) Interlock (0, 1) 0.190*** 0.007 0.039 0.024* (0.000) (0.478) (0.254) (0.077) Log (Boardsize) 0.024* 0.763*** 0.007 (0.082) (0.000) (0.477) Composition -0.035 0.041 0.080 0.041 (0.660) (0.116) (0.378) (0.110) ROA 0.045 0.069** 0.309*** 0.069** (0.544) (0.042) (0.008) (0.042) ROA (t-1) -0.043 0.080*** 0.265** 0.080*** (0.59) (0.015) (0.023) (0.015) ROA (t-2) 0.219*** 0.112*** 0.370*** 0.112*** (0.006) (0.001) (0.001) (0.001) Age 0.076*** -0.012 -0.044 -0.012 (0.000) (0.159) (0.147) (0.149) GrowthOpp -0.001** 0.000 -0.001 0.000 (0.035) (0.784) (0.766) (0.724) FCF 0.081 (0.179)

Intan -0.126** (0.03)

R-Square 0.321 0.234 0.435 0.234 Year and Industry dummies Yes Yes Yes Yes Number of Observations 6,141 6,141 6,141 6,141

42

Table 9: Firm complexity and SD directors – propensity score matching (director – Level) Sample period: 2006-2015

In this table, we test whether a complex firm has higher propensity to add a board member with engineering degree relative to a simple firm. This table presents result for chi-square test of equality. We first create a sub-sample of firms that had an increase in number of directors compared to previous year. Next, we create a matched pair of complex versus simple firms using propensity score analysis. We define complex(simple) firms as those firms that have above(below) median factor score computed using number of business segments, leverage and firm size consistent with Coles, Daniel and Naveen (2008). A firm is considered part of the control group if it is classified as complex. Similarly, the firm is considered in a treatment group if it is classified as simple. We match the treatment and control group firms based on the propensity scores computed using the firm characteristics such as Market value of equity, calculated by multiplying the firms’ year-end stock price by its number of shares outstanding and the industry classification identified through NIC two-digit code. We perform the chi-square test of equality between the proportions of firms with additional IS director in complex and simple firms. ∗, ∗∗, and ∗∗∗ denote statistical significance at the 10%, 5%, and 1% levels, respectively.

Number of observations FractionSD p-value

Complex

659

0.608***

0.000

Simple firms 0.393

43

Table 10: Alternative explanation – regression analysis (firm – level)

Sample period: 2006-2015 This table presents result for ruling out alternative explanation. In this table, we estimate firm fixed effect regressions MTB as a dependent variable. In Model (1), we estimate the relation between Fraction_Advanced and MTB. Interlock is defined as two companies’ boards are interlocked in each year when the second company’s board has at least two board members overlapping with the first company’s board in each. Composition is defined as number of independent directors scaled by total board size for a given firm. ROA is defined as net income over total assets. GrowthOpp is defined as the ratio of depreciation expenditures to sales. Segments is defined as number of business segments a firm operates in based on National Industry Classification(NIC) code. Size is defined as log of total sales. Leverage is defined as total debt scaled over total assets. FCF is defined as the ratio of operating cash flow less preferred dividend and equity dividend to the book value of assets and Intan is defined as one minus the ratio of net, property, plant and equipment to book value of assets.The regression standard errors are heteroscedasticity-adjusted and include industry and year dummies. ∗, ∗∗, and ∗∗∗ denote statistical significance at the 10%, 5%, and 1% levels, respectively. Both dependent and independent variables are winsorized at 1% and 99%.

Dependent variable MTB

Parameter Estimate (p-value)

Fraction_Bachelors 1.107*** (0.002)

Fraction_Advanced 0.149

(0.690)

Interlock -0.080 (0.189)

Insider_Ownshership -0.005** (0.037)

Log(Boardsize) 0.493*** (0.000)

Composition -0.931***

(0.000)

ROA 0.029

(0.855)

ROA (t-1) 0.267

(0.204)

ROA (t-2) 0.096

(0.639)

Age -0.683***

(0.001)

GrowthOpp 0.026*** (0.000)

Segments -0.112* (0.056)

Size 0.026

(0.325)

Leverage 0.389*** (0.001)

FCF 0.104

(0.424)

Intan 0.220* (0.081)

Intercept, industry, and year dummies Yes Firm Fixed effects Yes Number of observations 6,141 R2 (Pseudo-R2) 0.781

44

Table 11: Alternative explanation – matched sample analysis (firm – level) Sample period: 2006-2015

In this table, we test our alternative explanation, that rules out the ‘learning effect’ in favor of ‘SD effect’. This table presents the results for propensity score matched sample of directors. We match the SD directors who have qualified CSE compared to NSD directors who also have qualified the CSE. This table tests whether an SD director holds higher board seats compared to NSD director. A director is part of the treatment group if he/she has an engineering bachelor’s degree and has cleared the Civil Services Examination (CSE). While a director is in control group if he/she has non-engineering bachelor’s degree and has cleared the Civil Services Examination (CSE). We match the treatment and control groups based on the propensity scores computed using the characteristics such as director’s age and gender. ∗, ∗∗, and ∗∗∗ denote statistical significance at the 10%, 5%, and 1% levels, respectively.

Comparison of SD directors with NSD directors

Number of

observations Average board

seats p-value

Average directorships of SD directors 293

2.16*** 0.000

Average directorships of NSD directors 1.29

45

Table 12: Engineering enrollment ratio and firm performance: instrumental variable regression (firm – level) (Sample period: 2007-2011)

This table presents the results from the instrumental variable regression for MTB on Fraction_enroll. To compute the Fraction_enroll measure we obtain the total number of enrollments in engineering undergraduate degrees and total number of enrollments in all undergraduate degrees in each state in India from Government of India’s Ministry of Human Resource Development (MHRD) for the sample period 2007 to 2011. We compute the fraction of engineering undergraduate enrollment to total number of undergraduate enrollment in a given state each year and we match the state-wise statistics with our firm level data by identifying the city/state a firm is headquartered in. Interlock is defined as two companies’ boards are interlocked in each year when the second company’s board has at least two board members overlapping with the first company’s board in each. Composition is defined as number of independent

directors scaled by total board size for a given firm. ROA is defined as net income over total assets. GrowthOpp is defined as the ratio

of depreciation expenditures to sales. Segments is defined as number of business segments a firm operates in based on National Industry Classification(NIC) code. Size is defined as log of total sales. Leverage is defined as total debt scaled over total assets. FCF is defined as the ratio of operating cash flow less preferred dividend and equity dividend to the book value of assets and Intan is defined as one minus the ratio of net, property, plant and equipment to book value of assets. The regression standard errors are heteroscedasticity-adjusted and include industry and year dummies. ∗, ∗∗, and ∗∗∗ denote statistical significance at the 10%, 5%, and 1% levels, respectively. Both dependent and independent variables are winsorized at 1% and 99%.

Dependent Variable MTB

Parameter Estimate

(p-value)

Intercept -0.655***

(0.000)

Fraction_Enrol 1.703*** (0.000)

Interlock 0.042

(0.317)

Log(Boardsize) 0.133*** (0.000)

Insider_Ownshership 0.007*** (0.000)

Composition -0.266***

(0.000)

ROA 3.363*** (0.000)

ROA (t-1) 1.143*** (0.001)

ROA (t-2) 0.713*** (0.004)

Age 0.015

(0.393)

GrowthOpp 0.690*** (0.000)

Segments -0.026***

(0.000)

Size 0.037*** (0.000)

Leverage -0.059 (0.576)

FCF 0.449*** (0.000)

Intan 0.451*** (0.000)

Industry, and year dummies Yes Firm Fixed effects Yes Number of observations 1,773 R2 (Pseudo-R2) 0.156

46

Table 13: Alternative measure for firm performance (Sample period : 2006-2015)

This table presents the regression results to test the relationship between FractionSD and ROA. In Model (1), we estimate with ROA as a dependent variable and FractionSD as key independent variable. In Model (2), we replace FractionSD with LOGSD for a given firm each year as a key independent variable. Interlock is defined as two companies’ boards are interlocked in each year when the second company’s board has at least two board members overlapping with the first company’s board in each. Composition is defined as number of independent directors scaled by total board size for a given firm. ROA is defined as net income over total assets. GrowthOpp is defined as the ratio of depreciation expenditures to sales. Segments is defined as number of business segments a firm operates in based on National Industry Classification(NIC) code. Size is defined as log of total sales. Leverage is defined as total debt scaled over total assets. FCF is defined as the ratio of operating cash flow less preferred dividend and equity dividend to the book value of assets and Intan is defined as one minus the ratio of net, property, plant and equipment to book value of assets. The standard errors are heteroscedasticity-adjusted. ∗, ∗∗, and ∗∗∗ denote statistical significance at the 10%, 5%, and 1% levels, respectively. Both dependent and independent variables are winsorized at 1% and 99%.

Dependent Variable ROA Model (1) Model (2)

Parameter Estimate Estimate

(p-value) (p-value)

FractionSD 0.470*** (0.000)

LogSD 0.145***

(0.000)

Interlock -0.030 -0.029

(0.128) (0.140)

Log(Boardsize) 0.084*** -0.012

(0.012) (0.746)

Insider_Ownshership 0.001 0.001

(0.423) (0.423)

Composition -0.277*** -0.275***

(0.000) (0.000)

ROA (t-1) -0.073 -0.072

(0.291) (0.295)

ROA (t-2) -0.031 -0.027

(0.648) (0.685)

Age -0.069 -0.053

(0.302) (0.424)

GrowthOpp 0.000 -0.001

(0.549) (0.546)

Segments -0.021 -0.019

(0.267) (0.312)

Size 0.018** 0.017**

(0.025) (0.037)

Leverage -0.247*** -0.245***

(0.000) (0.000)

FCF -0.063 -0.063

(0.142) (0.137)

Intan -0.187*** -0.192***

(0.000) (0.000)

Intercept, industry, and year dummies Yes Yes

Firm Fixed effects Yes Yes

Number of observations 6141 6141

Adjusted-R2 0.617 0.617

47

Appendix A1: Variable Descriptions

Variables (variable names in parentheses)

Variable Type

Computation Data Source

Firms with majority directors holding engineering degree (SD BOARDS)

Independent Takes the value of 1 if more than 50% of the directors have engineering degree.

‘Indian Boards’ database

Fraction of SD directors (FractionSD)

Independent Number of directors with engineering degree scaled over total number of directors in a given firm each year.

Log of directors with engineering degree (LogSD)

Independent Log of number of directors with engineering degree in a given firm each year.

State wise enrollment ratio (Fraction_Enrol)

Independent (Instrument)

Ratio of total number of enrollment in engineering undergraduate degrees in a given state in India and total undergraduate enrollment in all disciplines.

Average Board age (BoardAge) Mean of all the directors’ age in a given firm each year.

Average Directorships(Avgdir) Independent Average directorships of all the board members of a given firm each year.

Average Directorships for outside directors (AvgOutdir)

Independent Average directorships of independent board members of a given firm each year.

Board interlock (Interlock) Independent When, in a given year for a company, its board has at least two independent/inside/CEO overlapping with another company’s board.

Log of board size(log(Boardsize)) Independent Log of total number of board members for a given firm each year

Board composition (Composition)

Independent Number of independent directors scaled by total board size for a given firm

Market to book ratio (MTB) Dependent (Total Assets - Book Equity + Market Equity)/ Total Assets

Prowess

Complex firms (Complex) Dependent

Compute a factor score for each firm in a given year based on the number of business segments, leverage and firm size. Firms with factor score above median score are classified as complex and others as simple firms.

Prowess

Return on Assets (ROA) Independent Net Income / Total Assets Prowess

Firm size (Size) Independent Log of Total Sales Prowess

Insider ownership as % of common(Insider_Ownshership)

Independent % of ownership held by insider Prowess

Firm age (Age) Independent Years since incorporation Prowess

Growth opportunities(GrowthOpp)

Independent Depreciation expense / Sales Prowess

Business segments (Bussegments)

Independent Number of business segments a firm operates in based on National Industry classification code

Prowess

Leverage(Leverage) Independent Total debt / Total assets Prowess

Free Cash Flow (FCF) Independent (Operating cash flow - preferred dividend - equity dividend) / (book value of assets)

Prowess

Intangible assets (Intan) Independent 1- (net, property, plant and equipment / book value of assets)

Prowess

48

Appendix A2: 3SLS estimations as a robustness check for the first and third hypotheses

I. Robustness check for the first hypothesis

As pointed in sub-section 5.2 of the paper, in this appendix we present the findings of our 3SLS estimations to

account for the presence of endogeneity in our first and third hypotheses. In the first test we account for the fact that

firm performance (MTB), board size (Log (Boardsize)) and the fraction of SD directors (FractionSD) maybe endogenously

determined. Thus, we estimate the following system of equations.

Equation 1:

{ , , , , ,

, , , , , ,

BoardCharacteristics

MTB f FractionSD LogBoardsize Interlock Composition InsiderOwnership

ROA Age Size GrowthOpp Segments Leverage F

, tan, }

FirmCharacteristics

CF In Yeardummies

Equation 2:

( 1) ( 2)

{ , , , ,

, , , , , , , , tant t

BoardCharacteristics

LogBoardsize f FractionSD Interlock Composition InsiderOwnership

MTB ROA ROA ROA Age Size GrowthOpp SegmentsFCF In

, }

FirmCharacteristics

Yeardummies

Equation 3:

( 1) ( 2){ , , , , , , , ,t t

BoardCharacteristics

FractionSD f LogBoardsize Interlock Composition MTB ROA ROA ROA Age Segments , }

FirmCharacteristics

Yeardummies

To estimate the 3SLS procedure, in Table A2.1 we use the endogenous variables such as MTB, Log (Boardsize) and

FractionSD as the dependent variables in Equations, 1, 2 and 3 respectively. In Equation 1, we estimate our original

specification (i.e. Table 4) to determine the relationship between MTB and FractionSD. The firm performance results (i.e.

Equation 1) confirm that higher fraction of SD directors have positive relationship with market to book ratio. In Equation

2, we estimate the board size specification, using Log (Boardsize) as dependent variable and use controls that are known

to be the determinants of board size based on earlier studies. For example, Bhagat and Black (2001) document that board

size is related to firm performance, insider ownership, firm size and board independence. Similarly, Coles et al. (2008) in

their board size specification use firm characteristics such as return on assets, free cash flow and intangible assets as

determinants of board size. Taking guidance from these two studies, we include controls that are related to board size. In

addition, we also allow FractionSD to be endogenously estimated with board size and firm performance. Because it can

be argued, on one hand that larger boards have more SD directors and on the other hand, SD directors might choose to

sit on larger boards to increase their business networks. Thus, SD directors may presumably join profitable firms and larger

boards rather than the other way around. Finally, in Equation 3 we estimate the determinants of fraction of SD directors.

To do so, we include board characteristics such as interlocking as a proxy for director networking because previous studies

(Katz, Lazer, Arrow and Contractor ,2004; Wellman ,1988 and Cohen, Frazzini, and Malloy ,2008) argue that directors that

are connected (socially or through common board seats) have complementary skills and similar educational qualifications.

49

Similarly, we argue that board independence is related to fraction of SD directors because director ties might play an

important role in the appointment of SD directors, which, in turn, can influence the board composition. Also, in Equation

3 we include controls for firm characteristics that might be related to the firm’s fraction of SD directors. For example, Fich

and Shivdasani (2006) document that return on assets is an accounting measure for current performance of firms and

growth opportunities is a proxy for firm’s investment opportunities. As argued earlier that if there is an endogenous

relationship between FractionSD and ROA then the SD directors should choose to be in the boards of firms with higher

ROA. In addition, Equation 3 also includes a control for number of business segments because larger and diversified firms

require higher advising by the board (Klien ,1998).

We provide the 3SLS results in Table A2.1, where we find that the coefficient estimate for FractionSD in equation

1 is positive and significant at 0.01 level, suggesting that firms benefit from higher fraction of SD directors on its boards,

consistent with our OLS result in Table 4. In equation 1, we also find that other controls have similar signs and significance

as in our firm performance specification. Similarly, in equation 2 we find FractionSD is positive and significant at 0.01 level.

The interpretation for the relationship between board size and firm performance in equation 2 is consistent with our

original model. In equation 3, we test the relationship between FractionSD and MTB, where we find that the coefficient

for MTB has a positive and significant relationship with FractionSD at 0.01 level. Overall, we find that the results from 3SLS

regressions are consistent with our first hypothesis and show that our original findings are robust.

50

Table A2.1: Effect of board structure on firm performance (3SLS)

This table presents the results from the simultaneous equations (3SLS) regression for MTB on Log(Boardsize) and FractionSD. In this table, we estimate three equations using a system of equations for our endogenous variables. First, in equation 1 we use MTB as a dependent variable. In equation 2 we use Log (Boardsize) as a dependent variable. In equation 3, we use FractionSD as a dependent variable. Interlock is defined as two companies’ boards are interlocked in each year when the second company’s board has at least two board members overlapping with the first company’s board in each. Composition is defined as number of independent directors scaled by total board size for a given firm. ROA is defined as net income over total assets. GrowthOpp is defined as the ratio of depreciation expenditures to sales. Segments is defined as number of business segments a firm operates in based on National Industry Classification(NIC) code. Size is defined as log of total sales. Leverage is defined as total debt scaled over total assets. FCF is defined as the ratio of operating cash flow less preferred dividend and equity dividend to the book value of assets and Intan is defined as one minus the ratio of net, property, plant and equipment to book value of assets. The regression standard errors are heteroscedasticity-adjusted. All the models include year dummies. ∗, ∗∗, and ∗∗∗ denote statistical significance at the 10%, 5%, and 1% levels, respectively. Both dependent and independent variables are winsorized at 1% and 99%.

Dependent Variable MTB Log(BoardSize) FactionSD Equation 1 Equation 2 Equation 3

Parameter Estimate Estimate Estimate (p-value) (p-value) (p-value)

Intercept 2.322 1.367*** 0.094***

(0.730) (0.000) (0.011)

MTB 0.716*** 0.041*** (0.004) (0.000)

FractionSD 14.645*** 3.171***

(0.001) (0.001)

Interlock 0.431

(0.558)

Log of board size -3.321 0.048*** (0.494) (0.010)

Insider_Ownshership -0.002 -0.004***

(0.714) (0.014)

Composition -1.167*** 0.260 0.003

(0.015) (0.123) (0.603)

ROA 0.456 -1.719*** 0.051***

(0.457) (0.001) (0.000)

ROA (t-1) -1.624*** 0.039 (0.001) (0.386)

ROA (t-2) -1.358*** 0.084** (0.001) (0.043)

Age 0.479 0.126*** -0.022***

(0.206) (0.000) (0.000)

GrowthOpp 0.029*** -0.015** -0.001*** (0.000) (0.024) (0.004)

Segments -0.072*** 0.006***

(0.001) (0.000)

Leverage 0.078 0.007

(0.738) (0.703)

Size 0.213 0.007

(0.499) (0.703)

FCF 0.759** -0.873***

(0.031) (0.002)

Intan 0.016 -0.926***

(0.988) (0.000)

Number of Observations 6,141 6,141 6,141 Year dummies Yes Yes Yes

51

II. Robustness check for the third hypothesis

Our third hypothesis is that complex firms should have more SD directors and larger boards

relative to simple firms. In the second test using the definition in Coles et al. (2008) to classify firms as

simple/complex we allow for the possibility that there is likely endogeneity between the fraction of SD

directors, board size and firm complexity and use the robust 3SLS estimation with FractionSD,

Complexity_Factor and Log (Boardsize) as the three dependent variables as follows:

Equation 1:

( 1) ( 2)

_ { , , , , ,

, , , ,t t

BoardCharacteristics

Complex Factor f FractionSD LogBoardsize Interlock InsiderOwnership Composition

Age ROA ROA ROA Growt

, , tan, }

FirmCharacteristics

hOpp FCF In Yeardummies

Equation 2:

{ , , , _ , , , ,

FirmCharacteristicsBoardCharacteristics

LogBoardsize f FractionSD Interlock Composition Complex Factor Age ROA GrowthOpp Ye }ardummies

Equation 3:

( 1) ( 2)

{ _ , , , ,

, , ,t t

BoardCharacteristics

FractionSD f Complex Factor LogBoardsize Interlock Composition

Age ROA ROA GrowthOpp

, }

FirmCharacteristics

Yeardummies

In Equation 1, we estimate the complexity specification with the Complexity_Factor (i.e. linear

combination of firm size, leverage and number of business segments) as a dependent variable to

determine the joint relationship between Complexity_Factor, FractionSD and Boardsize. The coefficient

estimate for FractionSD in equation 1 is insignificant, suggesting that the FractionSD does not drive firm

complexity. In our third hypothesis we argue that complex firms require higher fraction of SD directors. In

Equations 2 and 3 we estimate specifications corresponding to board size and fraction of SD directors

respectively. In our third hypothesis, we argue that complex firms require a greater fraction of SD

directors. For the board size and fraction of SD directors we use similar controls as used in Equations 2

and 3 in A2.1. Note that since we compute complexity as a linear combination of leverage, firm size and

number of business segments we do not include these three variables as controls for our 3SLS equations.

52

We provide the 3SLS estimates in Table A2.2 and find that the coefficient estimates are consistent

with our hypothesis on firm complexity. Specifically, we find that the Complex_Factor variable in equation

2 is positive and significant at 0.01 level suggesting that complex firms have larger boards. Similarly, in

equation 3 the coefficient estimate for Complex_Factor is positive and significant implying that complex

firms are correlated with a higher fraction of SDs on their boards. In sum, we find that complex firms have

a higher fraction of SD directors and larger boards consistent with our third hypothesis.

53

Table A2.2: Firm complexity, board size and fraction of SD directors (3SLS) This table presents the results from the simultaneous equations (3SLS) regression for Complex_Factor on Log(Boardsize) and FractionSD. In this table, we estimate three equations using a system of equations for our endogenous variables. First, in equation 1 we use Complex_Factor as a dependent variable. In equation 2, we use Log(Boardsize) as a dependent variable. In equation 3, we use FractionSD as a dependent variable. Interlock is defined as two companies’ boards are interlocked in each year when the second company’s board has at least two board members overlapping with the first company’s board in each. Composition is defined as number of independent directors scaled by total board size for a given firm. ROA is defined as net income over total assets. GrowthOpp is defined as the ratio of depreciation expenditures to sales. Segments is defined as number of business segments a firm operates in based on National Industry Classification(NIC) code. Size is defined as log of total sales. Leverage is defined as total debt scaled over total assets. FCF is defined as the ratio of operating cash flow less preferred dividend and equity dividend to the book value of assets and Intan is defined as one minus the ratio of net, property, plant and equipment to book value of assets. All the models include year dummies. ∗, ∗∗, and ∗∗∗ denote statistical significance at the 10%, 5%, and 1% levels, respectively. Both dependent and independent variables are winsorized at 1% and 99%.

Dependent Variable Complex_Factor Log (BoardSize) FactionSD Equation 1 Equation 2 Equation 3

Parameter Estimate Estimate Estimate

(p-value) (p-value) (p-value)

Intercept -1.912*** 2.435*** 0.812***

(0.008) (0.000) (0.003)

Complex_Factor 1.068*** 0.143

(0.000) (0.10)*

Log(Boardsize) 0.749* -0.284**

(0.062) (0.020)

FractionSD 0.059 0.015

(0.910) (0.944)

Interlock 0.178** -0.146*** 0.046***

(0.028) (0.006) (0.007)

Insider_Ownshership 0.000

(0.628)

Composition 0.106*** -0.124*** 0.003

(0.014) (0.004) (0.881)

ROA 0.279*** -0.274***

(0.000) (0.002)

ROA (t-1) 0.160***

(0.000)

ROA (t-2) 0.237***

(0.000)

Age 0.056 -0.042** -0.007

(0.178) (0.031) (0.266)

GrowthOpp -0.009*** 0.009*** 0.000

(0.000) (0.000) (0.695)

FCF 0.027

(0.504)

Intan -0.044

(0.704)

Year and Industry dummies Yes Yes Yes

Number of Observations 6,102 6,102 6,102

54

Appendix A3: The Indian middle class and the value of education

Almost any person with Indian heritage knows well the value of education, and especially through

the accumulation of academic degrees, that has existed in the Indian Middle Class society for well over

two hundred years stretching back to the country’s colonial past. According to Misra (1963), the factors

responsible for the emergence of the Indian middle class are different from those responsible for the

emergence of the middle class in the West. While the Western middle class came into existence due to

the Industrial Revolution of the eighteenth century, the Indian middle class emerged due to over two

hundred years of British colonialism when the British imposed land and legal policies (that changed

customs into law) and introduced Western education and technology on the native Indian population.

Those among the population who embraced Western education and the British way of life in those early

days were responsible for creating the first middle class families in India. Their successors all over the

country are now still considered the middle-class society in India and those early values attributed toward

Western Education, which is interpreted to mean English Medium Instruction, in today’s vernacular

continue to form the bedrock of middle class values in India.37 Tangri (1961) argues that while the prime

mover of revolutionary change in the UK and other Western countries has been technology, in India, it

has been education. Education (as opposed to sports in the US or in Western Europe) is the prime channel

through which the children of the vast middle class in India dream upon improving their socioeconomic

standing in society. In particular, the high demand in education in India has led to the fueling of a large

number of colleges mostly in medicine and engineering the two most sought-after degrees the

undergraduate level.

India has also had a dominant caste system in place going back many thousand years.38 Experts

argue that such a caste system impeded occupational mobility and technological change in Indian society

up until the British arrived on Indian shores in the seventeenth century. In this prevailing cast system, the

King (usually from the highest caste) and the warriors (second highest caste) had a monopoly on wealth

and power. The rest of the population involved themselves in trade, farming, artisan work, and industry,

37 In fact, the founding Indian middle class families did not look upon capitalism favorably and espoused the values inherent in education and the arts inherent in painting, music, poetry and dance. 38 Historically, the caste system in India divided Hindus into four main categories - Brahmins, Kshatriyas, Vaishyas and the Shudras. These classifications or caste determined every aspect of religious and social life of a Hindu. According to ‘Manusmriti’ (Ancient legal text on Hindu Law) the division of groups based on the individual’s work is known to date back more than 3000 years old.

55

and were looked down upon and relegated to marginal status.39 Then came the British in the seventeenth

century and upended the existing system in short order. The higher caste families were the first to realize

the value of the potentially new opportunities presented by the arrival of the British. They moved their

families to the urban centers and focused on obtaining British style education for themselves and their

family members.40 41

While historically, Indian education and, in particular, western style education was elitist, the trend is

alive and well in modern day India, where education serves as a gatekeeper permitting an avenue of

upward mobility to those who can successfully navigate the hurdles of exhaustive national level entrance

examinations to select English medium secondary and high schools, as well as for admission to select post-

secondary institutions of Engineering, Medicine and Management. However, unlike in the United States

where entry to Ivy League institutions often comes with a hefty tuition bill for the student and his/her

family, in India, when someone successfully enters one of these elite post-secondary educational

institutions, their tuition fees are relatively nominal and affordable by most middle-class families.

Therefore, unlike in the US, (lack of) family wealth is not a constraint to entering an elite educational

institution. By the same token, innate intelligence is a constraint to entry and it is this innate intelligence

that we focus on in this paper.

39 BBC News provides a brief description of the privileges bestowed by the caste system (http://www.bbc.com/news/world-asia-india-35650616). Also see Basu (2017). 40 In those early days, it was common for the Indian landed gentry to send their sons to England for education in the elite British institutions like Eton, Harrow, Oxford and Cambridge. 41 In 1997 a survey carried out by the publication India Today reported that about a third of the Indian population (well over 400 million people and greater than the entire population of the United States) could carry on a conversation in English.