CEO long-term orientation and elite university education

Abstract

Scholars have advocated the importance of a long-term orientation to the sustainable management of organizations, but few have examined its sources. We develop more fully the concept of long-term orientation, and from the perspective of upper echelons theory, argue and demonstrate that a CEO’s human capital and motivation, as signaled by graduation from a top-ranked university, relates positively to three aspects reflecting long-term orientation. These include strategy in the form of profit reinvestment, R&D expenditure, and honest reporting of earnings; greater exclusive personal commitments of CEO time and finances to the firm; and stewardship of stakeholders such as employees and the community. Our findings are derived from a study of the BoardEx database of 1459 CEOs of US corporations from 2003 to 2013, and are robust to endogeneity concerns and to more inclusive rosters of elite universities.

Keywords

elite education human capital long-term orientation upper echelons

Upper echelon theory argues that the personal characteristics of top executives and their teams can have a profound impact on the strategies and conduct of their firms, and consequently, on firm performance (Finkelstein and Hambrick, 1996; Hambrick, 2007; Hambrick and Mason, 1984). Years of scholarship and empirical analysis have borne out the predictive value of this perspective (e.g. Bromiley and Rau, 2016; Finkelstein et al., 2009; Hiebl, 2014; Yamak et al., 2014).). One core theme signaled by this view is the influence of a CEO’s attitudes, capabilities, and motivations on the strategies, conduct, and performance of their firms (Buyl et al., 2011; Finkelstein et al., 2009).

Indeed, research on executive human capital as it relates to the behavior of CEOs suggests its importance to organizational priorities and capabilities (Coff and Kryscynski, 2011; Crook et al., 2011). Some of that literature examines the role played by education in augmenting the effectiveness of business leaders (Bhagat et al., 2010; Martelli and Abels, 2010; Miller et al., 2015). For example, recent research demonstrates the positive impact that education at Ivy League schools has on sustainable firm performance (Miller et al., 2015), in part because that resource has been shown to be a source of economic rents due to both its relatively non-tradable nature (Lazear, 2009) and its resistance to adverse selection (Ahuja et al., 2005). Unfortunately, little is said about the behavior of these executives that may lead to such performance, nor the implications of education-related human capital for issues such as long-term sustainable conduct toward a firm and its stakeholders (Carroll, 1991; Carroll and Buchholtz, 2014; Freeman, 2010).

This research adopts the thesis that CEOs with elite educations—those from top-ranked universities—succeed because their talent, discipline, and social awareness give them the confidence and motivation to pursue a long-term orientation (LTO) in managing their companies and its stakeholders. Specifically, the selectivity and training associated with an excellent university education makes it more likely that executives will have the competency, confidence, and social capabilities needed to embrace an LTO for their businesses, their careers, and their communities. Indeed, the admission criteria of top schools select for candidates with the academic capabilities and social contributions that reflect the personal talent, motivation, and discipline that favor an LTO (Flanagan, 2001; Hernández, 1997; Karabel, 2006).

The contribution of our analysis is threefold. First, we develop a broader and more concrete characterization of LTO, a critical aspect of strategy and upper echelon leadership (Finkelstein et al., 2009). Certainly, there is a useful and growing literature on LTO that falls into several categories. Some studies focus on the statements and opinions of executives, isolating key characteristics such as futurity, continuity, and persistence (e.g. Brigham et al., 2014; Laverty, 1996). Other works concentrate on specific manifestations of LTO such as investment horizons or R&D expenditures (Block, 2009; Chrisman and Patel, 2012; James, 1999; Souder and Bromiley, 2012). Finally, there are studies that characterize LTO in specific contexts such as family businesses (Miller and Le Breton-Miller, 2005; Pieper, 2007; Zellweger, 2007) and national cultures (Bearden et al., 2006). We build on and differentiate our study from this work by adopting a broader characterization of LTO, embedding it within an upper echelons perspective (Hambrick, 2007), and examining it at the levels of strategy, CEO commitment behavior, and sustainable behavior toward employee and community stakeholders. We also focus on objective strategic, behavioral, stakeholder, and performance indicators of LTO, not parochial opinions. In addition, we develop several indexes for our more encompassing characterizations of LTO. Finally, we analyze an empirical context incorporating a large and diverse collection of public companies.

Our second contribution is to the upper echelons theory (Bromiley and Rau, 2016; Finkelstein et al., 2009; Hambrick, 2007; Yamak et al., 2014) by identifying a key driver of LTO in the form of the education-related human capital of the CEO. Some who have characterized LTO view it as a dominant logic—a core perspective embraced by the dominant coalition on how they conceptualize their firm and make key resource allocation decisions (Lumpkin and Brigham, 2011; Prahalad and Bettis, 1986). A critical actor in that coalition is the CEO, and, as noted, there is a considerable legacy establishing the major impact of CEO characteristics on strategic behavior and performance (e.g. Finkelstein et al., 2009; Hambrick and Mason, 1984; Miller et al., 1982). We build on that legacy by examining the educational selection and education experiences of CEOs as they relate to a potentially critical managerial orientation, namely, LTO.

A third contribution is to the literature relating human capital to firm performance (Coff and Kryscynski, 2011; Crook et al., 2011; Miller and Toulouse, 1986; Miller et al., 2015), which we develop in part by examining the relationship between selection and training for talent and discipline, and a long-term managerial orientation.

We conceive the notion of LTO under three classes of manifestations. First, are an executive’s strategic business decisions—specifically, investment in business renewal initiatives and a focus on the future capabilities of the organization (Block, 2009; Lumpkin et al., 2010; Wang et al., 2009). Such a far-sighted approach is more demanding than one fixed upon short-term targets or the status quo (Laverty, 1996), and therefore is more apt to call on the talent and discipline associated with an elite education. Second, LTO demands a top executive’s commitment and devotion to their specific business, as reflected by the intensity of their personal time and financial investments in their firm (James, 1999; Laverty, 1996). Whereas short-term results can be achieved via simple expedients such as cost-cutting, an LTO requires an enduring and focused application of skill and devotion. Again, the talent and confidence associated with an elite education may facilitate such commitment. Third, LTO requires a firm to cater to sustaining various stakeholder groups who must support it over the long run, specifically, employees and the community (Bearden et al., 2006; Miller and Le Breton-Miller, 2005; 2014; Nevins et al., 2007). It will be virtually impossible to pursue far-sighted and ambitious objectives without the help of a motivated and talented group of employees, and also stakeholders from outside the firm, including customers and the public. We will argue that serving and inspiring these stakeholders will benefit from the talent, social skills, and social awareness selected and trained for in many elite schools (Karabel, 2006; Lottes and Kuriloff, 1994; Perry, 1999).

In a study of 1459 CEOs of publicly listed US firms from 2003 to 2013, we find that elite education for a CEO relates to all of the above manifestations of an LTO. This is true regardless of the degrees pursued by the executives, and irrespective of the years in which the CEO obtained his or her degree.

Our analysis proceeds as follows: we first discuss how selection and training by elite universities relates to personal characteristics such as intelligence, talent, discipline, motivation, and ethical social awareness. Then, in the hypothesis section, we argue how the three different aspects of an LTO are positively associated with these personal characteristics of a CEO. We proceed to describe our method and findings, and conclude with a discussion.

Education, human capital, and LTO

Elite university education and managerial talent

How elite¹ schools selected their students: back in the 1960s, Ivy League schools—and similar highly selective universities—transformed themselves from institutions that favored students from privileged socioeconomic backgrounds (Farnum, 1990; Kingston and Lewis, 1990; Newcomer, 1955) into meritocratic institutions that selected candidates on the basis of academic achievement and intellect, proven social and communication skills, discipline, and leadership potential (Gladwell, 2005; Hernández, 1997; Karabel, 2006). Their prestige, job market influence, and socioeconomic benefits enabled these institutions to have their pick of the top candidates for admission (Dale and Krueger, 2002). Elite university acceptance rates have for many years hovered around 5%–15%, which are conservative numbers given that less promising candidates rarely bother to apply (Hernández, 1997). More importantly, elite criteria for admission are very broad: for example, Harvard evaluates candidates according to personal, academic, extracurricular, and athletic achievements using interviews, references, and essays as well as the usual transcripts (Karabel, 2006). Certainly, elite schools insist on superior scholarly achievement and cognitive intelligence as assessed by conventional measures (Dale and Krueger, 2002; Gladwell, 2005; Karabel, 2006). However, they also favor candidates who communicate and interact well, think clearly about their life objectives, and have demonstrated social leadership at the state or national level—all signs of confidence, socio-emotional maturity, and capability (Flanagan, 2001; Gladwell, 2005; Karabel, 2006; Zhang, 2012). One Harvard recruiter wrote, “[we want] students who are going to change the world.” “[We ask] will they create value in the world; can we publicize the world-changing attributions of [our] graduates” (blog-prepscholar.com).

In addition, elite schools going back to the 1970s have been shown to look especially favorably on candidates who have performed or organized various types of charitable or community service—clear indicators of sustained devotion, competency, and social consciousness (Flanagan, 2001; Hernández, 1997). These qualities needed to enter an elite school take years of disciplined conduct, and very likely an LTO, to develop. Success in developing talent and building social achievements requires sustained and disciplined effort, patience, and sacrifice.² Finally, given the costs and selectivity of an elite education, there has always been a bias toward those coming from families that encourage learning and achievement (Lillard and Gerner, 1999). In short, for decades now, elite schools have selected for admission to their programs those who have nurtured a broad range of talents, developed superior social skills, and made important community contributions—all aspects of an LTO.

The benefits from an elite education

Elite universities are able to attract the best professorial talent because of their prestige, the capabilities of their existing staff, and their financial endowments, thereby enhancing the education they provide (Gladwell, 2005; Judge et al., 1995; Zhang, 2012). In fact, elite universities spend eight times as much per student on education as other schools (investopia0811). Also, in the course of their education, students get to benefit from working with peers with ample potential (Zhang, 2008).³

In addition, Karabel (2006) has argued that since the 1960s, elite universities have worked consistently to ensure a balanced racial, social, and economic mix and have avidly promoted the social consciousness this policy reflects. Indeed, a number of studies have linked sound university educations to greater social consciousness, which in a business setting may translate into more solicitous attention to stakeholders—particularly employees and the community. For example, Lottes and Kuriloff (1994) found that at a prestigious east coast institution, “students as seniors scored higher on measures of … social conscience, homosexuality tolerance and feminist attitudes and lower on male-dominant attitudes than they did as first year students.” According to Marens (2008), the teaching of social responsibility was initiated at the elite schools during the 1960s by scholars such as McGregor, Dunlop, and Melman. All of these factors contribute to a sound educational experience, which can have a positive effect on the capability, confidence, and social consciousness contributing to an LTO.

Ambiguous causality

As CEO LTO may be related to both exacting university selection processes and outstanding educational experiences, it is difficult to know the relative impact of each, and so we remain agnostic in that regard (Dale and Krueger, 2002; Gottesman and Morey, 2006). However, we shall take pains in our analyses to minimize endogeneity concerns that the relationships between LTO and elite education are caused by established LTO-oriented firms favoring CEOs from elite schools.

Elite university education and LTO

We have suggested that LTO may be reflected by three different categories: far-sighted business strategy, CEO personal commitment in time and financial investment, and sustaining service to stakeholders. These behaviors demand executive intelligence, discipline and confidence, and a willingness to serve the organizational community.

Certainly, it is easier to achieve short- versus long-run performance, the latter requiring more talent, persistence, and devotion than, say, simple expedients such as cost-cutting and earnings management (Laverty, 1996; Lumpkin and Brigham, 2011). Given these elevated challenges, executives with less talent and confidence would be less likely to pursue a long-term strategy. As noted, elite universities select and train for such qualities. Furthermore, an LTO benefits when CEOs demonstrate devotion to the company by allocating a major share of their time and personal assets to their firms (James, 1999; Laverty, 1996). Again, elite school selection and training may give CEOs the courage to make such investments because these executives believe they can do so successfully. Finally, as noted, long-term sustainability requires getting all stakeholders onboard—these include employees and the broader community (Bearden et al., 2006; Miller and Le Breton-Miller, 2005; Nevins et al., 2007). To that end, and as noted, elite schools have been shown to select candidates with greater social awareness and social abilities—and also the talent to be able to serve multiple stakeholder constituencies (Flanagan, 2001; Karabel, 2006). It would be difficult for unethical or short-sighted executives to sustain the support of stakeholders inside and outside the firm to pursue an LTO.

Hypotheses

LTO in business strategy: talent and confidence

LTO entails a strategy of generous long-term investment in a firm and its future. Previous work on LTO indicates that it may be reflected at the business strategy level by more generous investment in the future—in research and development expenses (Block, 2009), reinvestment of profits (Laverty, 1996), and conservative accounting practices relating to the valuation and amortization of assets (Dechow et al., 1995). All of these characteristics reflect a pursuit of sustained long-term performance and a refusal to use short-term expedients that may compromise future success.

Investments in research and development do suggest long time horizons, as they generally require years to pay off (Barker and Mueller, 2002; Block, 2009; Chan et al., 2001; Laverty, 1996). By definition, research and development investments are about the future—about the new processes, discoveries, and product designs that will allow an organization to thrive under changing conditions. Conversely, it has been argued that short-term-driven opportunistic managers will reduce R&D expenditures when they takeover a company; they are more motivated by short-term profits that can be increased simply by cutting R&D (Marginson and McAulay, 2008). Indeed, reducing R&D expenditures is one way short-term-oriented executives can enhance the bottom line, sacrificing the future prospects of the firm for immediate gains (Miller and Xu, 2019; Rappaport and Bogle, 2011).

Looser accounting practices aimed at increasing annual earnings in lockstep fashion are another indicator of a short-term orientation versus LTO (Bergstresser and Philippon, 2006). CEOs, in reacting to shareholder pressures or to boost their incentive pay, may decide to manage corporate earnings via various accrual and valuation practices, attempting to succeed by managing the numbers instead of the business (Cornett et al., 2008; Jensen, 1988; Miller and Xu, 2019; Rangan, 1998). Avoiding such practices by providing investors with an accurate reflection of firm performance might be another indicator of an LTO. It also helps ensure that investors and shareholders who do stick with the company will be of the patient variety and not wed to lockstep earnings increases.

Finally, consistent reinvestment of profits in firm operations is a third indicator of LTO. Firms deciding to distribute profits via dividends to shareholders rather than reinvesting their earnings to build and renew the business is an inverse-indicator of LTO (Block, 2009; Miller and Le Breton-Miller, 2005; Pieper, 2007; Porter, 1992).

Certainly, these three aspects of an LTO can be demanding on a CEO. Business and product line renewal requires more skill and discipline than preserving the status quo (Laverty, 1996; Wiersema and Bantel, 1992). Investing in building a business requires more talent than simply paying out profits. And showing results from actual performance rather than accounting manipulation demands executive capability (Marginson and McAulay, 2008). We believe that the exacting selection and superior training afforded by elite education will correlate with personal cognitive ability and discipline, and therefore with the capacity to address the challenges inherent in an LTO. It may also be supported by the confidence bestowed upon a CEO by admission to a top school and by the education and socialization afforded by that experience (Bandura, 1993). Consistent with this view, Wiersema and Bantel (1992) and Bertrand and Schoar (2003) argued that better trained CEOs were more likely to sustain superior levels of investment in renewing their firms.

Hypothesis 1: Companies with CEOs from elite schools are more likely to demonstrate superior levels of R&D, more reinvestment of profits in the business, and less earnings management than companies with other CEOs.

LTO and personal time and financial commitment to the firm

An LTO is expected to be associated with a CEO’s investing more of his or her time and money in the ultimate success of the company—a hitherto neglected manifestation of LTO (James, 1999; Miller and Le Breton-Miller, 2005). Given the greater challenge of an LTO, high levels of personal commitment are required of a top executive to realize such an ambitious mission. We have argued that the selection processes and educational benefits of an elite education will be associated with more talented and confident managers who are willing to commit fully and more exclusively to the success of their firm and its stakeholders.

Two aspects of such commitment are the personal investment of time and wealth. Time commitment is reflected by the degree to which a CEO devotes his or her entire energy to the organization, as opposed to serving numerous boards or functions external to the company. Full devotion of time to one’s own company reflects a depth of commitment that is likely to be enduring (Weaver et al., 1999). By contrast, where a CEO serves many outside boards and functions, that can take valuable time and effort away from the job, and may even suggest an openness to moving on to other organizations. Such distractions have been shown to erode long-run returns (Malmendier and Tate, 2009).

Financial investment in one’s company is another measure of personal commitment, which can help to align CEO and firm interests. Such commitment is reflected by the degree to which the personal financial fortune of the CEO is tied to that of his or her company (Dechow and Sloan, 1991). It has been shown that such investments correlate with a CEO’s firm-specific knowledge, which has been shown to lead to better long-term performance (Wang et al., 2009). More importantly, it suggests that the CEO is confident of his or her ability to enhance the performance of the company, and motivates behavior to ensure long-term firm robustness.

Again, we believe that an elite education will be associated with two qualities that correlate with these personal aspects of LTO—competency and confidence in one’s ability to achieve long-run success. Admission to and completion of an elite university program represent victories in a rigorous competition; these can enhance a CEO’s confidence and sense of self-efficacy (Bandura, 1993). In addition, because it reflects and develops abilities and bestows prestige, such an educational experience may bolster that confidence. Finally, as noted, the elite school selection process seeks out via interviews, references, and essays, evidence of personal qualities such as confidence and leadership skills (Dale and Krueger, 2002; Gladwell, 2005; Karabel, 2006).

Hypothesis 2: CEOs from elite universities are more likely to demonstrate superior levels of time commitment to their firms as well as a greater financial stake in the future of their companies.

LTO and ethical treatment of stakeholders

An organization can be profitable in the short-run by behaving opportunistically toward its stakeholders. It can exploit employees via layoffs and by minimizing compensation and benefits. It can engage in poor citizenship by skirting environmental protection guidelines and polluting needlessly, all in the interest of the bottom line. But to do well in the long run, a firm needs to have the enthusiastic cooperation of a motivated workforce as well as a responsive and supportive community. In other words, an important set of indicators of an LTO have to do with how the company treats its employees and the community at large. In fact, Nevins et al., 2007; Ryu et al. (2007) and Wang and Bansal (2012) find that ethical behavior is central to LTO because it creates the trust from stakeholders that can aid long-term performance. Bearden et al. (2006) and Wang et al. (2008) also argue that ethical behavior, because it is imbued with social tradition and builds better and more enduring relationships with stakeholders, is a core requirement of LTO. We have argued that an education from an elite university selects for and fosters an ethical orientation among its students that will favor the socially responsible treatment of stakeholders such as employees and the community (e.g. Karabel, 2006; Lottes and Kuriloff, 1994).

Long-term oriented human resources practices encourage employee involvement in the company through financial and decision-making participation (McClean and Collins, 2011; Sheridan, 1992; Shore and Tetrick, 1994). This gives employees a voice in the issues that concern them and provides them with more interesting jobs that address social and self-actualization needs. Long-term stewardship of employees may also be reflected by their health and safety records (Hillman and Keim, 2001; Shore and Tetrick, 1994). Where there are few accidents and sick days, this is an indication that the firm takes care of its people. Only by enlisting the sustained cooperation, motivation, and initiative of its employees in these ways will a firm be able to pursue its far-sighted objectives (Miller and Le Breton-Miller, 2005).

Another key stakeholder is the community at large. LTO may be reflected by adherence to best practices regarding the environment and pollution control: Does the company have a good record in sustainable practices regarding the physical environment? Another indicator of care for the broader community are charitable contributions within and beyond the local community (Carroll and Buchholtz, 2014; Freeman, 2010). These are discretionary expenditures that signal true concern of a business for people in broader society. Firm reputation may benefit from such expenditures, thereby promoting support from stakeholders outside the firm, such as customers, suppliers, and local authorities.

Such solicitous attention to stakeholders requires talent in nurturing and obtaining the best from scarce resources. However, talent alone does not guarantee care for stakeholders, which may involve foregoing short-term economies and profits and therefore sacrifices and risk to a CEO’s compensation. So CEOs motivated by quick rewards are unlikely to pursue LTO, even if they have the talent to do so. Also demanded from them is the social awareness and courage to do the right thing for their stakeholders, who are so vital to the attainment of long-term objectives (Rappaport and Bogle, 2011; Weaver et al., 1999).

As noted, elite schools have selected candidates in part on the basis of both social skills and awareness, and community engagement and service (Flanagan, 2001; Hernández, 1997; Karabel, 2006; Lottes and Kuriloff, 1994). Moreover, it requires more talent to address the needs of multifaceted stakeholder groups than to work only for short-term economic outcomes. Elite schools select and train for such talent.

Hypothesis 3: Companies with CEOs from elite universities are more likely to demonstrate superior commitment to stakeholders, to employees via financial and decision-making involvement and attention to health and safety, and to the community via care for the environment and charitable contributions.

Method

Sample

To determine whether CEOs with elite educations embrace an LTO in managing their businesses, we employ a large sample of US CEOs from 2003⁴ to 2013. Executive education information was drawn from the BoardEx Database and includes CEOs’ university and degree information. We also draw information related to CEO employment history, network size, and prior board service from BoardEx. We combine this data with the Execucomp Database containing information on variables such as executive age, tenure, gender and compensation, and with Compustat data on corporate financial information. This results in a final sample of 1459 unique CEOs of US corporations over 11 years, for a total of 7063 CEO-year observations.

Variables

Independent variable

Our primary variable of interest is binary—elite university education—that equals to 1 if the CEO has a degree from an elite university. Specifically, our list of universities includes the traditional eight Ivy League schools, namely, Harvard, Yale, Columbia, Brown, Princeton, Cornell, Dartmouth, and University of Pennsylvania; we included MIT, Stanford, University of Chicago, Duke, and UC Berkeley to the list, because the latter too are considered top universities in terms of education quality and selecting for talent.⁵ This definition yielded 365 CEOs with an elite university degree. Our findings were robust to whether we included only the eight Ivy schools or used the significantly expanded list of elite schools, as defined in our robustness tests (see below).

Dependent variables—measures of LTO

Strategic business decisions

To test our first hypothesis—that CEOs from elite schools are less likely to engage in short-term initiatives and more focused on the long term, we measure short-term initiatives according to the amount spent on R&D activities, the extent of earnings management, and the practice of dividend distribution. Elevated R&D expenditure, the reinvestment of earnings in the firm, and avoiding earnings management are all reflections of a long-term strategic orientation. Cutting R&D and investment, and managing earnings would reflect the opposite (Miller and Xu, 2019).

All our firm-level financial variables were obtained from Compustat, unless otherwise indicated. Research and development is calculated as R&D expenditures scaled by total assets. Because R&D expenditures are not required to be reported when the amount is small, we replace firms not reporting such expenses with a 0 figure, an accepted practice among researchers (e.g. Bizjak et al., 1993; Edmans et al., 2013; Himmelberg et al., 1999).

Earnings management is measured using the modified Jones model (Dechow et al., 1995), which captures discretionary accruals by isolating the portion of total accruals that is unexpected. Discretionary accruals measures the discretion component of reported earnings, and is intended to reflect opportunistic accounting policies that manipulate the timing of revenues and expenses to temporarily increase or decrease accounting income and thereby impress investors and analysts (e.g. Cohen and Zarowin, 2008; Dechow et al., 1995). To calculate these accruals, we run the following regression for each SIC-year grouping

\frac{{TA}_{it}}{{Assets}_{i, t - 1}} = k_{1 t} \frac{1}{{Assets}_{i, t - 1}} + k_{2} \frac{Δ {REV}_{it}}{{Assets}_{i, t - 1}} + k_{3} \frac{{PPE}_{it}}{{Assets}_{i, t - 1}} + ε_{it}

(1)

TA represents total accruals calculated as the difference between earnings before extraordinary items and operating cash flows, scaled by total assets at the end of the previous fiscal year. D REV reflects the change in sales from year t-1 to year t. PPE represents the gross value of property, plant, and equipment. The coefficients from equation (1) are then applied to the following equation to obtain estimates of firm-year specific non-discretionary accruals (NA)

{NA}_{it} = {\hat{k}}_{it} \frac{1}{{Assets}_{i, t - 1}} + {\hat{k}}_{2} \frac{Δ {REV}_{it} - Δ {AR}_{it}}{{Assets}_{i, t - 1}} + {\hat{k}}_{3} \frac{{PPE}_{it}}{{Assets}_{i, t - 1}}

(2)

where ΔAR is the change in accounts receivable from the previous year. Finally, the discretionary accruals are computed as the difference between total accruals and non-discretionary accruals $({TA}_{it} {/ Assets}_{it - 1}) - {NA}_{it}$ .

Finally, we define a firm as dividend paying if the total dividend paid out is greater than 0 during a given year. Hence, dividend paying is a binary variable that equals to 1 if a firm pays dividend and 0 otherwise. Around 52% of the firms in our sample paid a dividend in a given year.

Personal financial and time commitment

Our second hypothesis conjectures that CEOs with elite university educations are personally more committed to their firms in both their time and financial commitments. To measure time commitment, we examine the number of outside roles the CEO holds in addition to his or her role as the CEO. This is done by counting the number of outside boards the CEO sits on during the year using the board membership data from BoardEx. More roles suggest less time commitment to the focal firm (Cashman et al., 2012; Fich and Shivdasani, 2006).⁶

We assess a CEOs’ financial commitment to the firm using two measures of CEO compensation following Core and Guay (2002) and Coles et al. (2006). The first measure, Delta, captures pay-performance sensitivity, and is the dollar change in wealth associated with a 1% change in the firm’s stock value. This measure captures the change in compensation package value due to both the options the CEOs have (both current year and previously granted options) and the shares they hold.⁷ The second measure, Firm Specific Wealth, is the sum of stock and option value held by the CEO. Option values are calculated using the Black–Scholes model. We consider a CEO’s financial commitment to the firm to be stronger when he or she has more personal wealth invested within the firm. Because these variables are skewed we log-transformed them in our regressions.

Commitment to stakeholders

Our third hypothesis relates CEO elite education to their commitment to stakeholders, specifically, employees and communities. We obtain data on firms’ commitment to employees and communities from the KLD database that provides annual company level ratings on specific firm-related issues.

We use two variables: employee involvement, and health and safety concerns for employees, to measure firms’ commitment to employees. These two measures reflect stewardship of human capital. Employee involvement reflects the extent to which companies encourage worker involvement and ownership within the corporation. High employee involvement firms are given a score of 1, which indicates significant employee participation in decision-making procedures, a high level of employee ownership, and an open sharing with employees of financial information (McClean and Collins, 2011). Low employee involvement firms receive a score of 0. Concern for employee health and safety is another dummy variable that equals 1 if the firm has been involved in major health and safety controversies or violations of employee health and safety standards, both of which indicate a low level of concern for employees. About 14% of our sample demonstrates high employment involvement, whereas 16% of the sample is affected by elevated employee health and safety concerns.

Firms’ commitment to communities is also gauged by two measures: negative economic impact on the community, and charitable contributions of the firm to the community. KLD defines negative economic impact as a dummy variable that equals to 1 if a firm’s actions have resulted in major community controversies: including environmental contamination, water rights disputes, plant closings, “put-or-pay” contracts with trash incinerators, or other company actions that adversely affect the quality of life, tax base, or property values in the community. A second contrasting variable was used to capture a firm’s influence on the broader global versus local community. Specifically, the global charitable contribution variable equals to 1 if a firm has made a substantial effort to make charitable contributions abroad, as well as in the United States. About 6% of our firms have confronted major economic controversies and 4% have made significant global charitable contributions.

The aggregate LTO index

In order to present a more encompassing and aggregate reflection of our findings, we have computed three formative sub-indexes for LTO in strategy, personal commitment, and stakeholders, and an aggregate formative index for LTO (Diamantopoulos and Winklhofer, 2001). As our variables are each believed to contribute to LTO and we have no reason for weighting these differently, our index is summative (Bollen and Lennox, 1991). Moreover, given that our analysis encompasses interval, count, and binary scales we were required to transform our variables so as to be able to combine them in a meaningful way. Thus, to ensure robustness we compute all four indexes in three different ways. The first bifurcates variables into high and low scores and sums them. The second standardizes all variables to have mean 0 and standard deviation 1, and then adds them. The last method scales each variable to have a range of 0–1 so that they can be summed. To arrive at the aggregate LTO index, sub-indexes are standardized prior to summation. Hence, LTO1 is the composite index constructed by first standardizing the three sub-indexes into high/low dummies and then aggregating them. Similarly, LTO2 aggregates the three sub-indexes (mean 0 and standard deviation 1), and LTO3 aggregates the three sub-indexes (range 0, 1). In all instances, we reverse-score variables with a negative impact on LTOs to align all subscales in the LTO direction.

Control variables

Our control variables include both CEO- and firm-level variables that may influence LTO. At the CEO personal level, we control for the size of CEO network, age, gender, tenure, and whether the CEO has an MBA degree. All variables are obtained from BoardEx and Execucomp. We control for the size of CEO network which is measured as the number of BoardEx listed individuals with whom the selected individual overlaps while in employment, other activities, or education roles at the same company, organization, or institution. Prior studies have argued that an elite degree may signal superior social connections rather than talent (e.g. Useem and Karabel, 1986). To isolate talent from social connections, the size of the CEO network is included to proxy for social connections. Also, because the MBA degree has been shown to be associated with short-termism (Miller and Xu, 2019), we include an MBA degree dummy that equals to 1 if the CEO has an MBA. We also include controls for age, tenure, and gender because these variables have been shown to influence CEO capabilities, strategy, and firm performance (Hambrick and Fukutomi, 1991; Martelli and Abels, 2010; Yim, 2013).

We include a number of controls for the career experiences of the CEO, each of which can have an impact on his or her breadth of knowledge and expertise and thus influence LTO. These include the number of past boards served, number of firms the CEO has worked for, number of sectors the CEO has worked in, and number of years the CEO has worked in the sector (Finkelstein et al., 2009).

At the firm level, in order to control for the advantages of firm scale and past performance, all of which may facilitate LTO, we control for total assets, leverage, book to market, and revenue growth (e.g. Martelli and Abels, 2010; Miller and Xu, 2019), all obtained from Compustat. Compared with other companies, large, low-leverage, and high-growth firms may have more resources to pursue an LTO. We use log of total assets to proxy for firms’ size, leverage (total long-term and short-term debt divided by total assets) for firms’ capital structure, book to market (book value of equity/market value of equity) for growth potential, and revenue growth for past year’s growth. In addition, in order to take into account the impact of governance conditions on CEO behavior and firm strategy, we control for the size of the board (number of the directors), percentage of independent directors, and CEO duality. Better monitoring may encourage LTO of the CEOs and limit short-termism. CEO duality equals to 1 if a CEO is also serving as the chair of the board (La Porta et al., 2000).

Analyses

Table 1 reports the summary statistics and correlation matrix of our main variables. Tables 2 to 8 demonstrate the impact of a CEO’s elite university degree on LTO. We use pooled ordinary least squares (OLS) regressions with firm-level clustering⁸ in Table 2 (for R&D and EM) and Tables 3, 5 and 6 (formative indexes), where the dependent variables are continuous. We use probit regressions with firm-level clustering in Table 2 (for dividends) and Table 4, where the dependent variables are dummy variables. In all regressions, the dummy variable, CEO elite university education, is our independent variable of interest and we control for all firm and CEO-level controls listed above, as well as industry (at the 2-digit SIC level) and year dummies. All variables are winsorized at 1% and standard errors are clustered by firm to account for within firm covariances (Peterson, 2009). In Tables 7 and 8, we report results incorporating the propensity score matching (PSM) technique to evaluate the possibility of endogeneity.

Table 1.

Summary statistics and correlation matrix.

	Variable	N	Mean	Median	SD	[1]	[2]	[3]	[4]	[5]	[6]	[7]	[8]	[9]	[10]	[11]	[12]	[13]	[14]	[15]	[16]	[17]	[18]	[19]	[20]	[21]	[22]	[23]	[24]	[25]	[26]	[27]	[28]	[29]	[30]	[31]
[1]	CEO elite school degree	7063	0.27	0.00	0.45
[2]	R&D/assets	7063	0.04	0.01	0.07	0.08
[3]	Earnings mgt	6555	0.00	0.00	0.09	−0.01	−0.23
[4]	Dividend paying	7059	0.52	1.00	0.50	−0.04	−0.27	0.14
[5]	Outside role	7063	1.29	1.00	0.59	−0.03	−0.03	0.06	0.04
[6]	Delta	7063	0.54	0.22	0.98	0.15	−0.04	−0.01	0.11	−0.04
[7]	Firm-specific wealth	7063	41105	16600	75650	0.15	−0.04	−0.02	0.09	−0.03	0.98
[8]	Empl involvement	5088	0.14	0.00	0.35	0.08	0.18	−0.07	−0.02	−0.03	0.11	0.09
[9]	Empl health and safety concern	6387	0.16	0.00	0.37	−0.04	−0.18	0.07	0.25	0.06	0.07	0.05	−0.05
[10]	Neg community econ impact	6387	0.06	0.00	0.23	−0.04	−0.08	0.08	0.15	0.05	0.06	0.05	0.00	0.25
[11]	Global charitable giving	4058	0.04	0.00	0.20	0.09	0.06	0.03	0.14	−0.02	0.13	0.11	0.15	0.09	0.18
[12]	Num year survive (firm)	7063	7.24	7.00	3.21	0.06	−0.04	0.01	0.07	0.01	0.10	0.10	−0.05	−0.01	−0.01	0.06
[13]	Change in Q	7062	−0.02	0.02	0.70	0.01	0.00	−0.03	0.03	0.00	0.05	0.06	−0.02	−0.01	0.00	−0.02	0.00
[14]	MBA	7063	0.42	0.00	0.49	0.27	−0.04	0.04	0.07	0.02	0.02	0.10	0.01	0.03	0.05	0.07	−0.01	0.03
[15]	Network Size	7063	637	450	606	0.24	0.00	0.01	0.15	0.01	0.32	0.28	0.16	0.15	0.24	0.33	0.08	0.00	0.12
[16]	Director #	7063	2.30	2.30	0.21	0.03	−0.23	0.06	0.37	−0.03	0.18	0.14	0.07	0.26	0.18	0.21	0.08	0.00	0.04	0.34
[17]	% Independent directors	7063	0.80	0.83	0.10	−0.02	0.00	0.04	0.12	0.16	−0.01	−0.03	0.05	0.13	0.11	0.08	−0.17	−0.01	0.06	0.16	0.17
[18]	CEO duality	7063	0.54	1.00	0.50	0.04	−0.11	0.07	0.17	0.46	0.16	0.16	−0.03	0.14	0.10	0.09	0.09	0.00	−0.03	0.17	0.13	0.19
[19]	Female CEO	7063	0.04	0.00	0.19	−0.01	−0.04	−0.01	−0.02	−0.03	−0.01	−0.02	0.01	−0.01	−0.01	0.05	−0.06	−0.01	−0.05	0.08	0.00	0.01	−0.01
[20]	Age	7063	54.95	55.00	6.55	−0.04	−0.08	0.07	0.18	0.06	0.10	0.10	−0.03	0.09	0.05	0.04	−0.08	0.02	0.01	−0.02	0.08	0.05	0.27	−0.05
[21]	Tenure	7063	6.62	5.40	4.36	0.09	0.02	0.02	0.00	0.13	0.23	0.25	0.01	0.00	0.00	−0.02	−0.07	0.02	−0.01	−0.01	−0.13	−0.02	0.16	−0.01	0.29
[22]	Assets	7063	8323	1760	34582	0.08	−0.05	0.04	0.16	−0.02	0.14	0.12	0.07	0.19	0.33	0.28	0.01	−0.01	0.04	0.38	0.28	0.04	0.10	0.02	0.03	0.00
[23]	Leverage	7063	0.20	0.19	0.17	−0.04	−0.17	0.04	0.12	0.03	−0.01	−0.01	−0.08	0.13	0.04	0.00	0.02	0.06	0.00	0.06	0.23	0.08	0.12	0.01	0.09	0.00	0.10
[24]	Bk/mkt	7063	0.49	0.41	0.39	−0.06	−0.11	−0.10	−0.09	−0.02	−0.17	−0.16	−0.08	−0.01	−0.01	−0.09	−0.11	0.16	−0.01	−0.12	−0.11	−0.06	−0.05	0.02	0.03	−0.01	−0.04	−0.11
[25]	Rev growth	7063	0.08	0.08	0.22	0.02	0.04	−0.01	−0.07	−0.01	0.08	0.09	0.02	−0.07	−0.03	0.01	0.04	−0.20	−0.02	−0.02	−0.03	−0.03	−0.01	0.00	−0.03	0.02	−0.02	−0.07	−0.21
[26]	Past # of boards	7063	2.43	1.00	3.10	0.07	0.04	−0.03	0.00	0.01	0.09	0.09	0.02	0.02	0.05	0.10	0.09	0.00	0.01	0.20	0.04	−0.01	0.11	0.01	0.25	0.06	0.05	0.07	0.00	0.00
[27]	Past # of sectors	7063	0.40	0.00	0.67	0.09	0.11	−0.03	−0.05	−0.03	−0.01	−0.02	0.08	−0.04	0.00	−0.03	−0.03	0.01	0.05	0.20	−0.03	0.05	−0.05	0.05	−0.05	−0.08	0.06	−0.03	−0.03	−0.01	0.01
[28]	Past # of companies	7063	2.63	2.00	2.05	0.05	0.12	−0.05	−0.10	−0.05	0.01	0.00	0.05	−0.05	−0.01	0.03	−0.08	−0.02	0.04	0.16	−0.07	0.03	−0.09	0.09	−0.01	−0.09	0.05	−0.03	−0.03	0.01	0.19	0.41
[29]	# of years in sector	7063	11.84	9.80	8.87	−0.01	−0.04	0.08	0.20	0.09	0.15	0.14	0.05	0.16	0.07	0.12	0.01	0.01	−0.02	0.05	0.17	0.06	0.13	0.00	0.19	0.42	0.12	0.09	−0.08	−0.02	−0.11	−0.20	−0.31
[30]	LTO1	1300	1.03	1.00	0.88	0.17	0.31	−0.25	−0.24	−0.30	0.31	0.32	0.40	−0.22	−0.10	0.17	0.03	0.00	0.02	0.18	0.00	−0.07	−0.03	0.03	−0.02	0.11	0.15	−0.13	−0.15	0.17	0.06	0.00	0.03	0.12
[31]	LTO2	1300	0.21	0.09	1.90	0.25	0.47	−0.37	−0.38	−0.32	0.43	0.44	0.50	−0.34	−0.27	0.38	0.11	0.04	−0.01	0.27	−0.03	−0.06	−0.03	0.04	−0.04	0.08	0.16	−0.13	−0.16	0.18	0.07	0.00	0.05	0.11	0.73
[32]	LTO3	1300	1.34	1.38	0.30	0.23	0.41	−0.24	−0.70	−0.29	0.38	0.39	0.41	−0.36	−0.19	0.23	0.07	0.03	−0.04	0.23	−0.13	−0.06	−0.05	0.05	−0.07	0.05	0.07	−0.16	−0.14	0.17	0.10	0.03	0.10	0.03	0.65	0.90

Correlations with p < 0.05 are in bold.

Table 2.

CEO elite school degree and strategic business decisions. Pooled OLS regression results of an elite school degree on R&D, earnings management, and probit regression results of dividend payment.

Variables	R&D expenditures	Earnings management	Dividends
Elite school degree	0.011***	−0.004*	−0.185**
	(3.12)	(−1.67)	(−1.99)
Network (log)	0.004**	−0.001	−0.064*
	(2.55)	(−0.61)	(−1.81)
MBA	−0.008**	0.006**	0.208**
	(−2.39)	(2.57)	(2.48)
# director	−0.012	0.003	1.174***
	(−1.53)	(0.42)	(4.81)
% of independent director	0.003	0.004	0.265
	(0.21)	(0.33)	(0.74)
Dual role	−0.009***	0.007***	0.170**
	(−2.88)	(2.72)	(2.27)
Female	−0.007	0.002	0.007
	(−1.05)	(0.30)	(0.04)
Age	−0.017	0.047***	1.478***
	(−1.07)	(3.83)	(4.02)
Tenure	−0.002	−0.003	−0.093*
	(−0.98)	(−1.43)	(−1.66)
Total asset (log) t-1	−0.010***	0.001	0.228***
	(−4.61)	(0.56)	(6.53)
Leverage t-1	−0.021	−0.006	−0.555**
	(−1.30)	(−0.76)	(−2.51)
Book to mkt t-1	−0.020***	−0.021***	−0.376***
	(−5.63)	(−4.76)	(−4.03)
Rev growth t-1	−0.003	−0.015	−0.525***
	(−0.48)	(−1.38)	(−5.49)
Past # of boards (log)	0.007***	−0.006***	−0.046
	(3.11)	(−3.29)	(−0.78)
Past # of firms (log)	0.009***	−0.005**	−0.152**
	(3.05)	(−2.32)	(−2.03)
Past # of sectors (log)	0.001	0.002	0.086
	(0.12)	(0.60)	(0.78)
# of years in sector (log)	0.006***	0.005***	0.141***
	(2.91)	(2.74)	(2.61)
Constant	0.204***	−0.226***	−9.439***
	(3.02)	(−4.61)	(−6.17)
Observations	7063	6555	7016
R²/psuedo R²	0.323	0.095	0.239
FE	Year industry	Year industry	Year industry

Year and industry dummies are included and the regressions are clustered at firm level. t-statistics are in parentheses.

*, **

, and *** indicate statistical significance at the p < 0.1, p < 0.05, and p < 0.01 levels, respectively.

Table 3.

CEO elite school degree and executive commitment to the firm. Pooled OLS regression results of an elite school degree on CEO’s time and financial commitment to the firm.

Variables	Time: CEO outside roles	CEO financial risk in firm	CEO firm-related wealth
Elite school degree	−0.044***	0.182***	0.197***
	(−2.93)	(3.28)	(3.48)
Network (log)	0.002	−0.000	0.005
	(0.24)	(−0.00)	(0.21)
MBA	0.032**	−0.003	−0.018
	(2.44)	(−0.07)	(−0.39)
# director	−0.008	−0.280**	−0.331**
	(−0.23)	(−2.09)	(−2.41)
% of independent director	0.211***	−0.743***	−0.940***
	(3.44)	(−3.06)	(−3.71)
Dual role	0.264***	0.366***	0.385***
	(20.21)	(8.01)	(8.22)
Female	−0.033	−0.404***	−0.399***
	(−1.01)	(−3.09)	(−3.12)
Age	−0.175***	−0.206	−0.154
	(−2.93)	(−0.95)	(−0.68)
Tenure	0.026***	0.449***	0.471***
	(2.99)	(13.32)	(13.65)
Total asset (log) t-1	−0.018***	0.512***	0.468***
	(−3.22)	(24.24)	(21.83)
Leverage t-1	−0.020	−1.045***	−0.974***
	(−0.57)	(−7.48)	(−6.85)
Book to mkt t-1	−0.003	−0.838***	−0.808***
	(−0.24)	(−14.29)	(−13.75)
Rev growth t-1	−0.004	0.506***	0.569***
	(−0.27)	(6.83)	(7.26)
Past # of boards (log)	0.001	0.027	0.041
	(0.08)	(0.79)	(1.16)
Past # of firms (log)	0.005	−0.017	−0.029
	(0.44)	(−0.38)	(−0.63)
Past # of sectors (log)	0.005	−0.035	−0.057
	(0.23)	(−0.56)	(−0.89)
# of years in sector (log)	0.022**	0.036	0.025
	(2.35)	(1.11)	(0.78)
Constant	1.363***	−3.552***	8.123***
	(5.62)	(−3.98)	(8.64)
Observations	7063	7063	7063
R²	0.273	0.547	0.515
FE	Year industry	Year industry	Year industry

Dependent variables are logged CEO outside roles, logged CEO financial risk (delta), and logged firm-related wealth. Year and industry dummies are included and the regressions are clustered at firm level. t-statistics are in parentheses.

*, **

, and *** indicate statistical significance at the p < 0.1, p < 0.05, and p < 0.01 levels, respectively.

Table 4.

CEO elite school degree and stakeholder focus. Probit regression results of an elite school degree on employees and communities.

Variables	Employee involvement	Employee health and safety concern	Negative community economic impact	Global charitable giving
Elite school degree	0.213**	−0.170***	−0.349**	0.584**
	(1.94)	(−2.75)	(−2.02)	(2.52)
Network (log)	−0.020	−0.033	0.093	0.239
	(−0.44)	(−1.33)	(1.42)	(1.37)
MBA	−0.090	0.100**	0.013	0.138
	(−0.88)	(1.91)	(0.08)	(0.70)
# director	−0.320	0.297**	−0.760**	0.287
	(−1.24)	(1.77)	(−2.01)	(0.41)
% of independent director	0.451	0.423	1.538**	2.403**
	(0.99)	(1.37)	(2.20)	(2.28)
Dual role	−0.202**	0.109**	−0.056	0.127
	(−2.14)	(1.94)	(−0.40)	(0.55)
Female	−0.133	−0.020	−0.220	0.658
	(−0.64)	(−0.14)	(−0.78)	(1.56)
Age	−0.399	0.308	0.474	−0.317
	(−0.93)	(1.14)	(0.80)	(−0.29)
Tenure	0.078	0.089**	0.055	−0.087
	(1.18)	(2.38)	(0.63)	(−0.63)
Total asset (log) t-1	0.264***	0.461***	0.477***	0.513***
	(5.75)	(18.74)	(7.07)	(4.43)
Leverage t-1	−1.135***	0.354**	−0.495	−1.610***
	(–3.39)	(1.98)	(−1.10)	(−2.60)
Book to mkt t-1	−0.548***	−0.187**	0.020	−1.065***
	(−3.24)	(−2.35)	(0.10)	(−2.64)
Rev growth t-1	0.227	−0.413***	−0.180	0.146
	(1.63)	(−3.22)	(−0.92)	(0.28)
Past # of boards (log)	0.004	−0.166***	−0.063	0.214
	(0.06)	(−4.40)	(−0.61)	(1.24)
Past # of firms (log)	0.015	−0.155***	−0.120	0.087
	(0.17)	(−3.31)	(−0.89)	(0.45)
Past # of sectors (log)	0.140	−0.047	0.136	−0.549**
	(1.11)	(−0.60)	(0.69)	(−1.99)
# of years in sector (log)	−0.036	0.053	−0.025	0.229
	(−0.55)	(1.38)	(−0.26)	(1.62)
Constant	−0.502	−13.559***	−5.812**	−8.460**
	(−0.30)	(−11.61)	(−2.37)	(−1.91)
Observations	4692	6205	5483	2706
Psuedo R²	0.1589	0.4108	0.3875	0.4917
FE	Year industry	Year industry	Year industry	Year industry

Year and industry dummies are included and the regressions are clustered at firm level. t-statistics are in parentheses. *, ** and *** indicate statistical significance at the p < 0.1, p < 0.05, and p < 0.01 levels, respectively.

Table 5.

CEO elite school degree and LTO indexes. Multiple regression results of an elite school degree on three sub-indexes for LTO in strategy, personal commitment, and stakeholders (Panel A). Panel A: Elite degree on three sub-indexes.

	Strategy			Commitment			Stakeholder
	(1)	(2)	(3)	(1)	(2)	(3)	(1)	(2)	(3)
	High/Low	Stdized	[0,1]	High/Low	Stdized	[0,1]	High/Low	Stdized	[0,1]
Elite school degree	0.146***	0.337***	0.084**	0.161***	0.414***	0.077***	0.174**	0.751***	0.153**
	(2.83)	(2.98)	(2.56)	(3.14)	(4.22)	(4.21)	(2.06)	(2.94)	(2.30)
Network (log)	0.034*	0.104**	0.027**	−0.015	−0.002	−0.000	0.000	0.097	0.036
	(1.76)	(2.41)	(2.25)	(−0.75)	(−0.05)	(−0.04)	(0.01)	(1.10)	(1.57)
MBA	−0.102**	−0.332***	−0.090***	−0.030	−0.128	−0.023	0.051	0.005	−0.011
	(−2.28)	(−3.35)	(−3.14)	(−0.66)	(−1.53)	(−1.47)	(0.74)	(0.03)	(−0.19)
# director	−0.433***	−1.032***	−0.426***	−0.074	−0.387*	−0.074*	0.067	0.421	0.030
	(−3.38)	(−3.80)	(−5.14)	(−0.62)	(−1.66)	(−1.70)	(0.43)	(0.99)	(0.25)
% of independent director	0.049	−0.058	−0.039	−0.786***	−1.886***	−0.349***	−0.774***	−0.930	−0.073
	(0.24)	(−0.14)	(−0.31)	(−3.66)	(−4.38)	(−4.35)	(−2.71)	(−1.19)	(−0.34)
Dual role	−0.125***	−0.323***	−0.076***	−0.158***	−0.414***	−0.067***	0.043	0.110	−0.011
	(–2.94)	(–3.50)	(–2.89)	(–3.58)	(–5.15)	(–4.47)	(0.63)	(0.49)	(–0.19)
Female	−0.108	−0.132	0.003	−0.110	−0.432*	−0.084*	0.078	0.603	0.084
	(−1.09)	(−0.64)	(0.05)	(−1.02)	(−1.80)	(−1.87)	(0.41)	(0.84)	(0.58)
Age	−0.984***	−1.720***	−0.538***	0.205	0.368	0.062	0.151	1.582**	0.345
	(−4.94)	(−3.66)	(−4.32)	(1.11)	(0.96)	(0.87)	(0.55)	(1.97)	(1.64)
Tenure	0.010	0.038	0.018	0.223***	0.535***	0.103***	0.064	0.092	0.039
	(0.32)	(0.62)	(0.97)	(7.45)	(9.31)	(9.62)	(1.43)	(0.63)	(1.05)
Total asset (log) t-1	−0.072***	−0.314***	−0.081***	0.325***	0.732***	0.139***	0.058*	0.127	0.021
	(−3.55)	(−5.24)	(−6.16)	(18.31)	(19.92)	(20.27)	(1.91)	(1.41)	(0.80)
Leverage t-1	−0.116	0.126	0.131	−0.470***	−1.306***	−0.250***	−0.258	−1.082**	−0.309**
	(−0.93)	(0.36)	(1.63)	(−4.03)	(−5.52)	(−5.66)	(−1.47)	(−2.12)	(−2.19)
Book to mkt t-1	0.076*	0.165*	0.102***	−0.492***	−1.109***	−0.212***	−0.055	−0.156	−0.034
	(1.73)	(1.68)	(3.48)	(−10.01)	(−11.16)	(−11.41)	(−0.62)	(−0.58)	(−0.48)
Rev growth t-1	0.314***	0.432*	0.164***	0.235***	0.747***	0.142***	0.328***	0.878**	0.283***
	(5.12)	(1.87)	(4.63)	(4.23)	(6.32)	(6.40)	(2.61)	(2.11)	(2.70)
Past # of Boards (log)	0.086***	0.200***	0.032	0.041	0.043	0.008	−0.090	−0.232	−0.045
	(2.69)	(2.79)	(1.61)	(1.32)	(0.73)	(0.75)	(−1.58)	(−1.27)	(−0.99)
Past # of Firms (log)	0.078*	0.267***	0.061**	−0.004	−0.050	−0.009	−0.047	0.053	0.018
	(1.88)	(3.00)	(2.39)	(−0.10)	(−0.64)	(−0.63)	(−0.77)	(0.29)	(0.36)
Past # of Sectors (log)	−0.027	−0.074	−0.032	−0.026	−0.079	−0.015	−0.069	−0.274	−0.048
	(−0.44)	(−0.47)	(−0.80)	(−0.43)	(−0.70)	(−0.71)	(−0.66)	(−0.85)	(−0.56)
# of years in sector (log)	−0.034	−0.041	−0.032*	−0.003	−0.035	−0.006	0.109**	0.290*	0.039
	(−1.13)	(−0.62)	(−1.73)	(−0.10)	(−0.59)	(−0.51)	(2.35)	(1.96)	(1.03)
Constant	6.827***	11.074***	3.355***	−0.945	−4.473***	−0.266	−0.331	−8.696**	−1.598*
	(7.87)	(5.97)	(6.81)	(−1.11)	(−2.81)	(−0.90)	(−0.27)	(−2.35)	(−1.66)
Obs	6552	6552	6552	7063	7063	7063	1364	1364	1364
R²	0.320	0.270	0.294	0.324	0.419	0.427	0.210	0.239	0.218
FE	Year industry	Year industry	Year industry	Year industry	Year industry	Year industry	Year industry	Year industry	Year industry

For each index, column 1 represents index calculated as the sum of high and low dummies. Column 2 represents index calculated by adding all standardized variables (with mean 0 and standard deviation 1). Column 3 represents index calculated by adding variables standardized between [0,1].

Table 6.

An aggregate formative index for LTO (Panel B). Panel B: Elite school degree on composite LTO indexes.

Variables	LTO1—high/low	LTO2—standardized	LTO3—[0,1]
Elite degree	0.244**	0.856***	0.103***
	(2.33)	(3.81)	(3.02)
Network (log)	−0.024	0.129	0.020
	(−0.49)	(1.47)	(1.50)
MBA	0.023	−0.156	−0.043
	(0.26)	(−0.84)	(−1.44)
# director	−0.067	−0.651*	−0.193***
	(−0.36)	(−1.76)	(−3.07)
% of independent director	−0.592*	−0.938	−0.111
	(−1.86)	(−1.48)	(−1.09)
Dual role	−0.062	−0.211	−0.034
	(−0.80)	(−1.09)	(−1.27)
Female	0.072	0.138	0.022
	(0.32)	(0.33)	(0.35)
Age	−0.005	0.295	−0.069
	(−0.01)	(0.41)	(−0.66)
Tenure	0.112**	0.223*	0.037**
	(2.00)	(1.83)	(1.99)
Total asset (log) t-1	0.197***	0.374***	0.043***
	(5.17)	(4.40)	(3.35)
Leverage t-1	−0.667***	−1.102**	−0.126*
	(−3.15)	(−2.24)	(−1.75)
Book to mkt t-1	−0.251***	−0.331	−0.026
	(−2.89)	(−1.40)	(−0.82)
Rev growth t-1	0.594***	1.531***	0.233***
	(4.62)	(3.75)	(4.35)
Past # of boards (log)	0.009	−0.082	0.002
	(0.14)	(–0.49)	(0.11)
Past # of firms (log)	0.037	0.242	0.047**
	(0.49)	(1.54)	(2.00)
Past # of sectors (log)	−0.080	−0.444*	−0.037
	(−0.69)	(−1.67)	(−1.01)
# of years in sector (log)	0.054	0.106	0.007
	(1.03)	(0.90)	(0.37)
Constant	−0.315	−3.988	1.398***
	(−0.23)	(−1.23)	(3.09)
Observations	1300	1300	1300
R²	0.262	0.339	0.376
FE	Year industry	Year industry	Year industry
Cronbach’s alpha	0.37	0.46	0.38

Year and industry dummies are included and the regressions are clustered at firm level. t-statistics are in parentheses.

*, **

, and *** indicate statistical significance at the p < 0.1, p < 0.05, and p < 0.01 levels, respectively.

Table 7.

Elite degree and long-term orientation: propensity score matching results. Panel A. PSM model for LTO indexes.

Dep: elite degree	Coeff	t		Coeff	t		Coeff	t
LTO1 t-1	0.070	0.79	LTO2 t-1	0.185	3.43	LTO3 t-1	1.104	3.32
# director	−0.483	−1.04	# director	−0.378	−0.81	# director	−0.361	−0.77
% of ind director	−0.188	−0.28	% of ind director	−0.054	−0.08	% of ind director	−0.225	−0.33
Dual role	0.087	0.57	Dual role	0.118	0.77	Dual role	0.102	0.66
Total asset (log) t-1	0.097	1.18	Total asset (log) t-1	0.016	0.19	Total asset (log) t-1	0.041	0.49
Leverage t-1	−0.446	−1.05	Leverage t-1	−0.108	−0.25	Leverage t-1	−0.290	−0.68
Book to mkt t-1	−0.875	−2.44	Book to mkt t-1	−0.596	−1.63	Book to mkt t-1	−0.766	−2.13
Rev growth t-1	−0.509	−1.41	Rev growth t-1	−0.650	−1.76	Rev growth t-1	−0.666	−1.79
ΔLTO1 (treated)	0.169		ΔLTO2 (treated)	0.105		ΔLTO3 (treated)	−0.038
ΔLTO1 (control)	−0.182		ΔLTO2 (control)	−0.600		ΔLTO3 (control)	−0.117
Average treatment effect (ATT)	0.351***	3.13	ATT	0.705***	3.91	ATT	0.079***	2.95

Propensity score matching based on the probability of a firm having an elite schooled CEO. Panel A reports the match model, which matches the probability of a firm with an elite schooled CEO (the treated group) to a nonelite schooled CEO firm (the control group) and reports the average treatment effect (ATT) on the outcome, at the bottom of the panel. The outcome variable is the difference in LTO from year t+3 to year t-1.

Table 8.

Panel B. PSM model matching quality.

	Treated	Control	t	P		Treated	Control	t	p	Variable	Treated	Control	t	p
LTO1 t-1	1.24	1.26	−0.14	0.89	LTO2 t-1	1.07	1.06	0.04	0.96	LTO3 t-1	1.48	1.48	0.15	0.88
# director	2.22	2.22	−0.11	0.91	# director	2.22	2.21	0.34	0.74	# director	2.22	2.21	0.3	0.77
% of ind director	0.76	0.76	−0.08	0.93	% of ind director	0.76	0.76	−0.04	0.97	% of ind director	0.76	0.76	0.28	0.78
Dual role	0.52	0.53	−0.18	0.86	Dual role	0.53	0.55	−0.37	0.71	Dual role	0.53	0.52	0.03	0.98
Total asset (log) t-1	6.83	6.85	−0.11	0.91	Total asset (log) t-1	6.84	6.88	−0.24	0.81	Total asset (log) t-1	6.84	6.85	−0.07	0.95
Leverage t-1	0.18	0.18	−0.02	0.98	Leverage t-1	0.18	0.17	0.29	0.77	Leverage t-1	0.18	0.17	0.28	0.78
Book to Mkt t-1	0.32	0.32	−0.2	0.85	Book to Mkt t-1	0.32	0.32	−0.15	0.88	Book to Mkt t-1	0.32	0.32	−0.21	0.84
Rev growth t-1	0.12	0.12	0.11	0.91	Rev growth t-1	0.12	0.12	−0.05	0.96	Rev growth t-1	0.12	0.13	−0.25	0.80

Panel B reports the matching quality that compares the treatment firm with the control firm among all the covariates.

*, **

, and *** indicate statistical significance at the p < 0.1, p < 0.05, and p < 0.01 levels, respectively.

Robustness

We conducted multiple robustness tests. For simplicity, we report only the regression results for the composite LTO index. We first show that our results hold for CEOs with undergraduate elite university degrees (Appendix I). We also varied the roster of top schools to include only the eight Ivy League Universities. In untabulated results, our significant findings were replicated. In addition, we reran our regressions for a much more extensive list of elite schools by including the top 25 schools⁹ ranked by the 2009 US News (Appendix II). Again, our results remain robust. Moreover, in order to assess whether the nature of a program influenced our results, we examined whether there were significant differences between arts, science, business, and engineering degrees. We found no significant differences. We also bifurcated the period of analysis according to the year of graduation. Again, we found no significant differences in our findings. Finally, we report firm fixed effect analyses (Appendix III) and transition analysis to and away from elite education CEOs (Appendix IV) to further address endogeneity concerns.

Findings

Tables 2 to 6 present our results on the impact of an elite university education on LTO. The findings of Table 2 support Hypothesis 1, indicating that CEOs with an elite degree pursue more long-run strategies. They demonstrate higher levels of R&D expenditure (β = 0.011, t = 3.12), engage less in earnings management (β = −0.004, t = −1.67), and have a greater tendency to reinvest all of their income (β = −0.185, t = −1.99). Given that the mean value for R&D expenditure is around 0.04 in our sample, the increase in R&D of 0.011 with an elite degree is economically meaningful. Similarly, CEOs with elite educations reduce earnings management by a magnitude of 0.006 from its mean of 0 and they also decrease the probability of paying a dividend by around 18.5%.

Table 3 shows the relationship between a CEO’s elite degree and his or her personal time and financial commitments to their company, as predicted in Hypothesis 2. We find that CEOs with elite degrees on average hold 4.5% (e^−0.044) fewer outside roles compared with their counterparts; that is, they devote more of their time exclusively to their firms. Also, they have more of a financial stake in their companies, hold more wealth in their firms, and have personal wealth that is more sensitive to their firm’s performance. Specifically, compared with CEOs without elite degrees, the wealth of those with such degrees is 20% (e^0.182) more sensitive to firm performance; moreover, CEOs with elite educations invest around 22% (e^0.197) more wealth in their firms. All these indicators suggest a high level of personal commitment of elite education CEOs to the companies they run.

Table 4 relates to Hypothesis 3 concerning how CEOs with elite educations treat their stakeholders. We find that they do cater more to their employees and communities vis-a-vis their counterparts without such educations. Firms with CEOs with elite degrees have a 35% lower probability of exerting a negative economic impact on their environment, and a 58% higher probability of superior global charitable giving. These outcomes suggest that these CEOs care more about both the local and the global community. They also benefit their employees: average employee involvement for their firms is 21% higher than elsewhere; moreover, their employees have 17% fewer health and safety concerns.

Tables 5 and 6 present the results for our indexes of LTO in strategy, personal commitment, and stakeholder treatment (in Panel A), as well as the aggregate for LTO (in Panel B) using the three methods of aggregation described above. As can be seen, in virtually all instances, the findings for the indexes confirm those for the individual variables, only more strongly. The composite LTO indexes display Cronbach’s alphas between 0.37 and 0.46, which is acceptable for broad constructs like ours in which non-redundant components are intended to contribute via summation to our overall formative index of LTO (Bollen and Lennox, 1991; Diamantopoulos and Winklhofer, 2001; Van de Ven and Ferry, 1979: 78–80). The items of the index are correlated but sufficiently independent to make distinct, cumulative contributions to the LTO construct.

Finally, in Tables 7 and 8, we begin to evaluate endogeneity concerns that LTO firms favor elite school graduates as CEOs; that is, could our results be driven by firm selection preferences rather than the behavior of elite schooled CEOs? To address this issue, we first use a PSM technique to match each elite-schooled CEO firm (the treatment sample) to a nonelite school firm (the control sample) that should have a similar probability of hiring an elite-schooled CEO. The matching model is performed using a probit regression (Table 7). The regression assesses the probability of an elite-schooled CEO being linked to particular firm scale, performance, and governance characteristics—specifically, lagged total assets, book-to-market ratio, revenue growth, debt leverage, number of directors, percentage of independent directors, and CEO duality. The analysis also controls for lagged LTO to further ensure similarity between treatment and control groups. If results were driven by LTO firms favoring elite-schooled CEOs, we should observe no difference in the change in LTO between the treatment and control groups. Our results reject that possibility. Panel A reports the average treatment effect (ATT) comparing the changes in LTO¹⁰ between the elite schooled and nonelite matched firms. We find significantly higher increases in LTOs for the firms with elite-schooled CEOs. In Panel B, we report the quality of the match between treatment and control groups, finding that after matching, all covariates of the probit model are similar.

Appendixes III and IV report the results from two additional means of addressing endogeneity. First, is a firm fixed effect analysis (Appendix III). If findings were driven by preferences of LTO firms for elite-schooled CEOs, then these results should vanish when controlling for firm fixed effects. We show that our results still hold for LTO2 and LTO3 when including firm fixed effects. For LTO1, the coefficients are of correct sign, with t = 1.44.

We also examine the change in LTO when a firm changes its status from an elite schooled to a nonelite schooled CEO or vice versa. Appendix IV shows that when a firm changes from the former to the latter, all LTO indexes decrease significantly compared with firms that change CEOs but do not change CEO university status. Conversely, in moving from nonelite to elite-schooled CEOs, the LTO indexes all increase, albeit not significantly, likely because it takes more time to implement some aspects of an LTO. Hence, it is unlikely that our results are driven by LTO firm preferences to hire elite-schooled CEOs.

Miller et al. (2015) found that Ivy League educations of CEOs were associated with their firms’ sustainable superior market valuations. We therefore checked to see whether this was also true in our larger sample. We were able to confirm that the firms of CEOs with elite university educations sustained superior market valuations and experienced a lower probability of delisting than their counterparts (these analyses are available from the authors upon request).

Discussion

We have attempted to add to the growing body of literature on upper echelons theory (Bromiley and Rau, 2016; Finkelstein et al., 2009; Hambrick, 2007; Yamak et al., 2014), arguing that a thus far neglected background characteristic of a CEO can profoundly influence his or her orientation toward and success at pursuing a far-sighted management approach, with implications for strategy, personal resource commitments to the firm, and benevolence toward stakeholders inside and outside the organization. Our results very broadly support the notion that a CEO’s selection by and education at an excellent university is most consistent with an organization’s demonstrating an LTO in its strategies, in the executive’s time and financial commitments, and in devotion to employees and the community. These findings are important as there has been a great deal of criticism of short-term opportunism by top executives, which has over the long run hurt both investors and other stakeholders (Fama and Jensen, 1983; Jacobs, 1991; James, 1999; Miller and Xu, 2019). Indeed, the entire agency literature addresses the threats of opportunistic behavior by executives (Fama and Jensen, 1983), while the stewardship literature celebrates a greater LTO (Lumpkin and Brigham, 2011; Miller and Le Breton-Miller, 2005)). Unfortunately, these literatures do not go far enough in identifying empirically the drivers of these selfish and salutary behaviors. In embracing an upper echelons perspective, we have identified one such driver linked to education at a top-ranked university.

Another contribution of our research is that we have developed and begun to operationalize the construct of an LTO at the strategy, personal, and stakeholder levels. Along with James (1999), Lumpkin et al. (2010), Miller and Le Breton- Miller (2005), and others, we believe this is an important managerial construct, particularly as a potential antidote to agency problems and an indicator of administrative stewardship (Le Breton-Miller and Miller, 2006).

An LTO may have important ethical implications; indeed Bearden et al. (2006); Nevins et al. (2007) and Wang et al. (2008) contend that ethical behavior toward stakeholders is central to LTO. When an executive stays with the company to implement far-sighted initiatives, invests personally in the stewardship of the company, and works to develop relationships with stakeholders, he is more likely to display responsible behavior. Ethically divergent behavior could compromise all such efforts by alienating key stakeholders, certainly over the long run (Freeman, 2010; Laverty, 1996; Nevins et al., 2007; Wang et al., 2008). By contrast, short-termism among top executives has been associated with behavior that benefits CEOs at the expense of the longer term performance of the firm and the well-being of its stakeholders (Jacobs, 1991; James, 1999). This behavior may involve cuts to long-term investments in R&D and other initiatives, manipulation of earnings, excessive CEO compensation not linked to performance (Miller and Xu, 2019), distraction by service to too many outside boards (Malmendier and Tate, 2009), and less generous treatment of stakeholders such as employees and the community (Freeman, 2010). Such CEO opportunism can erode firm resources that disadvantage the long-term prospects of an organization, its shareholders, and its employees.

Our research is not without limitations. We cannot claim that an excellent education is responsible for a CEO’s LTO. It is just as likely that elite schools select for the talent, confidence, and social consciousness that drives this behavior—characteristics that may manifest in one’s teens or even earlier. It may also be that firms whose board of directors prize an LTO select elite-schooled CEOs, although our numerous endogeneity checks suggest that this is very unlikely. In either event, the association between a particular educational background and a socially and commercially significant type of conduct is important, both for society and investors.

Our research invites further study of the relative importance of personal factors that predispose candidates to apply to elite schools, these candidates’ intellectual, ethical, and social qualities before entering such programs, and how such qualities are altered or developed in the course of their educational experiences. Toward that end, it may be useful for subsequent researchers to conduct qualitative research, perhaps via interviews with CEOs and the stakeholders of their firms, to enquire into their backgrounds, experiences, and motivations behind their LTOs.

Moreover, just as the human capital associated with elite university selection and education relates to LTO, so may the cognitive and social skills we have discussed enhance the managerial capabilities required by the “sensing, seizing and reconfiguring” challenges of dynamic capability creation and business renewal (Helfat and Peteraf, 2015). This too is a question worth further exploration.

Practical implications

Given the potential benefits of an LTO to firm strategic conduct, performance, and stakeholders, and its relationship to selection and education by an excellent university, it will be useful for those charged with recruiting and promoting executives to pay attention to the quality of their educations and educational institutions. Selection by an outstanding university can serve as one among multiple signals of executive attitude and capability. Of course our findings suggest empirical association, not determinism, as no doubt there are many excellent executives with less exalted educations, and certainly less talented and less successful executives with degrees from fine institutions.

Supplemental Material

Appendix_I – Supplemental material for CEO long-term orientation and elite university education

Supplemental material, Appendix_I for CEO long-term orientation and elite university education by Danny Miller and Xiaowei Xu in Strategic Organization

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Supplemental material

Supplemental material for this article is available online.

Notes

ORCID iD

Xiaowei Xu

Author biographies

Danny Miller is a research professor at HEC Montreal, and Director of HEC’s Center for Research on Organization, Strategy and Governance. He received his PhD from McGill University and has held visiting professorships at McGill University, Columbia University, and the University of Alberta. His areas of research interest are strategy, organization design, and family business. He has published nearly 200 articles and six books and is a Fellow of the Academy of Management, the Family Firm Institute and the Strategic Management Society. He also consults with international corporations and family businesses in the areas of strategy and organizational design.

Xiaowei Xu is an assistant professor of finance at University of Rhode Island. She received her PhD from the University of Alberta. Her research interest covers a variety of topics in corporate finance and corporate governance. Her recent publications have been in various journals such as Journal of Portfolio Management, International Review of Finance, Journal of Business Ethics and Strategic Management Journal.

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