Do Better Connected Executives Have Longer Incentive Horizons?

Minjie Huang

Department of Finance

University of Louisville

Louisville, KY 40292

minjie.huang@louisville.edu

June 2016

I am deeply grateful to George Bittlingmayer and Felix Meschke for their guidance and suggestions. I thank

Varouj Aivazian, Anup Agrawal, Christopher W. Anderson, George Cashman, Elif Sisli Ciamarra, David

Cicero, Lauren Cohen, Douglas O. Cook, Robin Cowan, Bob DeYoung, Ahmed Elnahas, Stuart Gillian,

Jennifer Itzkowitz, Hui Liang James, Chao Jiang, Darren Kisgen, Paul Koch, Tunde Kovacs, Tom Kubick,

Lei Li, Yun Liu, William McCumber, Darius Miller, Debarshi K Nandy, M. Fabricio Perez, Lynnette Purda,

Selim Topaloglu, Kevin Tseng, Shane Underwood, Albert Wang, Jide Wintoki, Hong Zhao, and seminar

and conference participants at the University of Kansas, the Midwest Finance Association 2015 Annual

Meeting, the Southwestern Finance Association 2015 Annual Meeting, the Eastern Finance Association

2015 Annual Meeting, the Southern Finance Association 2015 Annual Meeting, and the University of

Louisville for helpful comments. All errors remain my own.

Do Better Connected Executives Have Longer Incentive Horizons?

Abstract

I examine the role of executive connections in mitigating managerial short-termism. I hypothesize that

better connected executives have less short-termism because larger networks give them better access to

information and protect them against adverse job outcomes. Using the duration of managerial compensation

to measure incentive horizon, I find that better connected executives have longer incentive horizons.

Executives with larger networks also engage in less earnings management, and executive connections affect

earnings management through the channel of incentive horizon. These findings suggest that social networks

mitigate managerial short-termism and facilitate corporate long-term behaviors.

Keywords: social networks, managerial short-termism, incentive horizons, earnings management

JEL classification: G30, G34, J33, L14

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1 Introduction

The recent financial crisis has reignited researchers and practitioners’ interests in the issue of

managerial short-termism. Researchers have argued that managers prefer short-term projects due to career

concerns (Narayanan 1985), near-term stock price pressure (Stein 1989), and herding behaviors (Zwiebel

1995). In a survey of financial managers, the majority of executives would pass on a long-term project with

positive net present value (NPV) to avoid missing earnings forecasts of the current quarter (Graham,

Harvey, and Rajgopal 2005). Long-term projects usually have highly uncertain payoffs and substantial

probability of failure (Kothari, Laguerre, and Leone 2002). Top executives are also hesitant to engage in

long-term behaviors due to career concerns. Therefore, risk-averse managers are reluctant to choose long-

term projects and exhibit short-termism. In this paper, I examine the role of executive connections in

mitigating managerial short-termism through access to information and labor market insurance.

Prior literature has established that executives with more connections have better access to

information via their networks (e.g., Fracassi 2014; Engelberg, Gao, and Parsons 2012; Faleye, Kovacs,

and Venkateswaran 2014). Burt (2004) and McDonald, Khanna, and Westphal (2008) also show that

network connections expose the manager to alternative perspectives that improve the quality of strategic

decisions in settings with great uncertainties. Well-connected executives are able to access network

information that helps them identify and implement long-term projects with positive NPV. The access-to-

information effect can also reduce managerial risk aversion to long-term focus by diminishing the

uncertainties of long-term projects.

Executives with more connections also have better labor market insurance against losses of

employment. Better connected managers are more likely to find new and better jobs after forced turnovers,

and have more outside employment options and higher compensation (e.g., Nguyen 2012; Liu 2014;

Engelberg, Gao, and Parsons 2013). A network of acquaintances, former colleagues, and alumni provides

a safety net that protects the manager against adverse job outcomes by increasing the probability of re-

employment after a job turnover. The labor-market-insurance effect reduces the manager’s exposure to the

downside risk of long-term projects without affecting her exposure to the upside gains.

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An emerging literature has provided theoretical frameworks and empirical evidence that

compensation schemes with short incentive horizons reflect managerial short-termism (e.g., Bolton,

Scheinkman, and Xiong, 2006; Manso, 2011; Peng and Roell, 2014; Edmans, Gabaix, Sadzik, and

Sannikov, 2012). Gopalan, Milbourn, Song, and Thakor (2014) use pay duration - the weighted average of

vesting periods of different pay components - to measure incentive horizon, and find that shorter pay

duration is associated with more earnings-increasing accruals, a sign of short-termism. Baranchuk,

Kieschnick, and Moussawi (2014) show that, in a sample of newly public firms, corporations become more

innovative after they award the CEO equities with longer vesting periods. This implies that incentive

horizon not only captures managerial short-termism but also acts as a channel through which long-term

corporate behaviors are induced.

To test whether better connected executives mitigate managerial short-termism through longer

incentive horizons, I construct measures of incentive horizons and executive connections in a sample of

more than 13,000 executives in S&P 1500 firms from 2000 to 2013. Consistent with recent papers (e.g.,

Gopalan, Milbourn, Song, and Thakor 2014; Engelberg, Gao, and Parsons 2013), I estimate the duration of

executive compensation to measure incentive horizons, and I use the number of first-degree links an

executive has to other individuals in elite corporations to measure the executive’s network centrality. I find

that better connected executives have significantly longer incentive horizons in the full sample and

subsamples. I explore variations in employment history and education history to construct instruments for

executive connections. Results from instrumental variable regressions show that network connectedness of

an executive significantly and positively affects her incentive horizon. This positive relationship remains

significant after a battery of robustness tests. Furthermore, the effect of the executive’s network centrality

on her incentive horizon is stronger in firms with more growth options and weaker in firms with greater

risk. Finally, executives with larger networks engage in less earnings management, and executive

connectedness reduces earnings management through the channel of incentive horizons. Therefore, the

empirical evidence strongly supports that better connected executives have longer incentive horizons and

thus exhibit less managerial short-termism.

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These findings contribute to two strands of literatures. First, I contribute to the literature on

managerial short-termism. Prior studies have examined whether and how managerial short-termism can be

alleviated by enhancing monitoring (e.g., Bushee 1998; Farber 2005; Cadman and Sunder 2014). I extend

this literature by showing that managers’ access to network information and career concerns also play

significant roles in affecting managerial short-termism. Second, I contribute to the literature on social

networks in finance. Fracassi (2014) show that external networks help the firm obtain information

advantages to improve its investment decisions, and firms that are more central in the networks exhibit

better performance. Engelberg, Gao, and Parsons (2012) find that when banks and firms have managers

interconnected through professional or education networks interest rates are significantly reduced. My

results suggest that executive connections may have a positive effect by inducing long-term corporate

behaviors. Specifically, executive connections may help the manager choose desirable yet risky long-term

projects by providing valuable network information and labor market insurance. In addition, my results

suggest that the manager’s network centrality may facilitate corporate long-term behaviors via the channel

of her incentive horizon.

2 Background and Hypothesis Development

Prior studies indicate that executive pay with long horizon improves long-term firm performance

and thus alleviates managerial short-termism (e.g., Bebchuk and Fried, 2010; Bhagat and Romano, 2010).

Subsequently, an emerging literature has built theoretical frameworks for the optimal incentive horizon,

and the general view is that equity awards with long vesting periods alleviate managerial short-termism

(e.g., Bolton, Scheinkman, and Xiong, 2006; Manso, 2011; Peng and Roell, 2014; Edmans, Gabaix,

Sadzik, and Sannikov, 2012). Gopalan, Milbourn, Song, and Thakor (2014) use pay duration - the weighted

average of vesting periods of different pay components - to measure incentive horizon, and find that firms

with longer pay duration for their CEOs have lower earnings-increasing accruals. Baranchuk, Kieschnick,

and Moussawi (2014) show that, in a sample of newly public firms, corporations become more innovative

after they award the CEO equities with longer vesting periods. They conclude that this finding is consistent

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with the theoretical prediction that equity awards with longer vesting periods provide executives with better

incentives to pursue long-term objectives. While the growing literature has focused on the economic

outcomes and firm characteristics that are related to managerial incentive horizon, few papers attempt to

investigate the effect of individual attributes on pay duration.1

One crucial individual attribute for top executives is their network connectedness. Top executives

in Corporate America are linked to high-ranking managers or directors at other firms through professional,

educational, and social networks, and these networks have been shown to have significant impacts on

economic outcomes, both at the firm level and the individual level. At both levels, one fundamental

advantage of networks is that information flows across network nodes and creates spillovers (e.g., Glaeser

et al. 1992; Jaffe, Trajtenberg, and Henderson 1993), meaning that knowledge generated in one node of the

network becomes accessible to other nodes.

The executive’s network may accrue value to the firm. For example, Fracassi (2014) show that

external networks help the firm obtain information advantages to improve its investment decisions, and

firms that are more central in the networks exhibit better performance. Engelberg, Gao, and Parsons (2012)

find that when banks and firms have managers interconnected through professional or education networks

interest rates are significantly reduced. They show that firms with connected deals subsequently improve

performance in future credit ratings or stock returns, implying that networks lead to knowledge spillovers.

Faleye, Kovacs, and Venkateswaran (2014) document that firms with more-connected CEOs have higher

R&D investments, and these firms obtain more and better patents. They conclude that external networks

confer the firm better access to information and alleviate career concerns of executives in risk investments.

Cohen, Frazzini, and Malloy (2008) use college connections between mutual fund managers and corporate

directors to infer information flows in stock markets. They find that portfolio managers invest more in

connected firms and their connected holdings perform significantly better than the non-connected holdings.

1 For recent studies that related individual attributes to investment decisions, productivity, and compensation level,

see Bertrand and Schoar (2003); Schoar (2007); Malmendier and Tate (2009); Kaplan, Klebanov, and Sorensen

(2012).

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Overall, these papers suggest that network connections may be an important channel for information

spillovers among organizations and appear to be valuable to the firm.

Networks may also benefit individuals with connections. For instance, Nguyen (2012) find that a

CEO with social connections to directors is less likely to be fired for underperformance and more likely to

find new and better employers after a forced turnover. Liu (2014) studies the network effect on the labor

market conditions for CEOs. She concludes that the CEO’s connectedness expands outside employment

options and reduces job search frictions. Engelberg, Gao, and Parsons (2013) document that CEOs with

larger networks have higher compensation. They show that the wage premium for connections is consistent

with network literature on information spillovers, whereby CEOs are compensated for their valuable and

portable connections that facilitate knowledge flows into the firm. Cohen, Frazzini, and Malloy (2010) find

that stock analysts outperform significantly on stock recommendations for the firm where they have

educational links to the senior executives. They conclude that analysts obtain superior information through

their social networks, and the information advantage benefits them on stock recommendations. Berkman,

Koch, and Westerholm (2014) find that corporate directors gain abnormal returns when they purchase

stocks from firms to which they are connected via interlocking board seats and when they have high network

centrality. To sum up, prior studies support that executives with large networks obtain comparative

advantage that alleviate their career concerns and facilitate better personal performance, and hence

networks appear to be valuable to executives.

While recent papers on networks study economic outcomes at the individual level and the firm

level, no paper, to the best of my knowledge, has focused on the network effect on the executive’s incentive

horizon. Network connectedness may affect pay horizon from the perspective of both the firm and the

executive. On one hand, the board may prefer awarding long-term contracts to retain the executive for

continuous access to valuable information through her network. In particular, the board may use long

vesting periods to reward managers for their long-term success and at the same time make early voluntary

departures costly for executives (e.g., Gopalan, Milbourn, Song, and Thakor 2014). On the other hand,

networks expand outside options for executives and thus alleviate their dismissal risk. Previous papers

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document that CEOs bear significant costs in forced turnovers. For example, Peters and Wagner (2013)

show that turnover risk is a determinant in managerial compensation. They document a significantly

positive relationship between turnover risk and CEO pay. High turnover risk increases the probability for

the executive to leave the firm early, and thus makes the compensation contract with long vesting periods

less desirable to the executive. Laux (2012) also show that dismissal risk is a major friction that makes long

vesting periods costly to managers.

Therefore, in equilibrium, the executive’s connectedness may be an effective contracting factor that

leads to long incentive horizon. Better connected executives may benefit the firm by providing access to

valuable information, so the board awards compensation with longer vesting periods to retain the executive.

Larger networks reduce the executive’s dismissal risk by providing outside options and hence labor market

insurance, so compensation with long vesting periods becomes less costly to better connected executives.

Prior studies (Bolton, Scheinkman, and Xiong 2006) have suggested that managerial compensation tends

to have longer vesting schedules if doing so is more valuable to the firm and less costly to the manager.

However, compared to the efficient contracting hypothesis that better connected executives have

longer incentive horizon, it is also possible that greater network centrality also coincides with more

powerful executives. Prior studies have shown that the executive’s connections, especially internal

connections within the company, may be detrimental to firm value. For example, Fracassi and Tate (2012)

document that firms with more CEO-director connections exhibit lower value and are involved in in more

value-destroying acquisitions. Coles, Daniel, and Naveen (2014) find that in firms with directors appointed

by the CEO board monitoring decreases. Managers are typically risk-averse and prefer compensation with

short incentive horizon. If the executive’s network has little long-term benefit to the firm, a powerful

executive with high network centrality may be able to have a contract with short incentive horizon.

Therefore, the managerial power hypothesis would predict that better connected executives have shorter

incentive horizon.

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3 Data and Specifications

3.1 Data Construction

To measure executive connections I obtain network connection data from BoardEx. BoardEx is a

business intelligence database that provides detailed profiles of over 400,000 of the world's business leaders

in over 14,800 public and large private companies in North America and Europe. BoardEx uniquely

provides in-depth biographical information about directors and executives, including complete employment

record, education background, professional qualifications, and non-business-related activities. Based on

these detailed biographical information, network connectedness can be measured by constructing networks

among executives and directors via their shared common histories in employment and education. Following

prior studies (e.g., Engelberg, Gao, and Parsons 2013; Faleye, Kovacs, and Venkateswaran 2014), I measure

executive network connectedness by the total number of individuals with whom the executive shares an

overlapping employment history in a public firm or overlapping educational history in a university, which

excludes those individuals who are employed by the executive’s current firm.

A recent paper by Gopalan, Milbourn, Song, and Thakor (2014) proposes to measure incentive

horizon by the weighted average duration of components of compensation (salary, bonus, restricted stock,

and stock option). Following their specification in Equation (1) of Gopalan, Milbourn, Song, and Thakor

(2014), I measure incentive horizon by Incentive duration in the equation below:

Incentive duration = (𝑆𝑎𝑙𝑎𝑟𝑦+𝐵𝑜𝑛𝑢𝑠)×0 + ∑ 𝑅𝑒𝑠𝑡𝑟𝑖𝑐𝑡𝑒𝑑 𝑠𝑡𝑜𝑐𝑘𝑖× 𝑡𝑖+ ∑ 𝑂𝑝𝑡𝑖𝑜𝑛𝑗× 𝑡𝑗

𝑛𝑜𝑗=1

𝑛𝑠𝑖=1

𝑆𝑎𝑙𝑎𝑟𝑦+𝐵𝑜𝑛𝑢𝑠 + ∑ 𝑅𝑒𝑠𝑡𝑟𝑖𝑐𝑡𝑒𝑑 𝑠𝑡𝑜𝑐𝑘𝑖+ ∑ 𝑂𝑝𝑡𝑖𝑜𝑛𝑗𝑛𝑜𝑗=1

𝑛𝑠𝑖=1

, (1)

where Salary and Bonus are the dollar values of yearly salary and bonus, Restricted stocki is the dollar value

of the ith equity compensation of restricted stocks with vesting period ti (in months), and Optionj is the dollar

value of the jth equity compensation of stock options with vesting period tj (in months). The dollar value of

equity awards are estimated at the end of a fiscal year. In the year t, an executive may be awarded multiple

equity grants with different vesting periods, and ns and no are the total number of such grants in stock and

options, respectively. I also construct alternative measures of incentive horizon. Incentive durationGD is

measured in Eq. (1) except that the dollar value of equity awards are estimated at the date when these awards

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are granted. Incentive durationFV is measured in Eq. (1) except that the dollar value of equity awards are the

grant date fair value reported by the firm.

I obtain detailed compensation data to construct Incentive duration from Incentive Lab. Incentive

Lab is a comprehensive compensation database that contains in-depth information from corporate reports

and proxy statements about compensation of executive officers and directors (e.g., Bettis, Bizjak, Coles,

and Kalpathy 2013). In particular, it provides grant-by-grant information of equity awards such as vesting

schedules, vesting periods, and fair values, which is usually not available in standard compensation

databases. The in-depth compensation data from Incentive Lab allow me to construct Incentive duration to

measure incentive horizon.

Finally, I intersect the executive network data from BoardEx with the incentive horizon data from

Incentive Lab and complement firm and executive characteristics using data from Compustat, CRSP, and

Execucomp. The sample period is from 2000 through 2013. The boundary of the sample is set by BoardEx

and Incentive Lab, which mostly covers S&P 1500 firms. The final sample contains executive level data of

network connections and incentive horizon with 51,452 observations, which covers 13,485 executive

officers from 1,242 firms during the time period of 2000 – 2013.

I am facing several data limitations when drawing inferences from the final sample. First, the social

network of an individual is dynamically changing and difficult to fully capture. To the extent that the

network data I use from BoardEx covers mainly S&P 1500 firms in the U.S., the network measures in this

paper may underestimate the actual network connectedness for executives. Be that as it may, it is perhaps

relevant to focus on network connectedness among executives and directors who have worked in an S&P

1500 firm, through which the information access and potential job opportunities may be more valuable to

both firms and executives. Second, in this version of the paper, I focus on the incentive duration of new

equity awards granted to executives every year. While the new equity awards reflect the most current

incentive horizon, grants that were awarded before but are yet to fully vest remain important to the overall

incentive horizon. I plan to address this issue by including in the next version of the paper alternative

measures of incentive horizon that take into account the current and past equity grants.

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3.2 Summary Statistics

The summary statistics, reported in Table 2, show that the average Incentive duration for an

executive is 18.39 months, while the median is 18.80 months. An increase of 7.21 months moves the 25th

percentile of Incentive duration to the median, and another increase of 5.85 months moves the median of

Incentive duration to the 75th percentile. Taken together, the distribution statistics of Incentive duration

seem to suggest that its distribution in the final sample is quite symmetric. Alternative measures of incentive

horizon, Incentive durationGD and Incentive durationFV, also show similar distributions. In untabulated tests,

I compare average incentives horizon for executives across the Fama-French 49 industries. The

considerable amount of variation across industries implies that we should control for industry heterogeneity.

Similar to Gopalan, Milbourn, Song, and Thakor (2014), I also find that executive incentive horizon is

longer in industries that usually have long project horizon such as Aircraft, Medical Equipment,

Shipbuilding, Pharmaceuticals and that have significant intangible assets such as Computers and Computer

Software. I also compare in untabulated tests the average incentive horizon for executives along the time

period of 2000 – 2013. While Incentive duration is generally stable during 2000 – 2005 and 2006 – 2013,

the apparent jump in 2006 warrants further investigation. One potential explanation may be that after the

adoption of FAS 123R in 2005, large public firms in the U.S. are required to expense their stock options in

income statements using fair value on the grant date, which leads to more compensation expenses and thus

results in the decline of option awards to executives.2 Another consequence of the adoption of FAS 123R

may be the increase of vesting periods, which may help reduce the compensation expenses in the income

statement and may also accidentally increase Incentive duration. I further investigate this concern by

splitting the sample by the time period of 2000-2005 and the time period of 2006-2013 and conduct

robustness tests in these subsamples.

Executive network connectedness is mainly measured by Total connections, which is the total

number of individuals with whom the executive shares an overlapping employment history in a public firm

2 For example, see Hayes, Lemmon, and Qiu (2012) for detailed discussion.

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or overlapping educational history in a university, which excludes those individuals who are employed by

the executive’s current firm. On average, the typical executive in the final sample is connected via

employment or educational network to 203 individuals who have worked in an S&P 1500 firm. 126 of the

connections come from overlapping employment history, and the remaining 77 connections come from

alumni network based on overlapping university education. Alternatively, BoardEx also provides the

number of first-degree connections to all other individuals in the BoardEx universe as the variable Network

size (Akbas, Meschke, and Wintoki 2015). Since Network size captures internal and external networks for

an individual through multiple organizations, it is larger than Total connections in both mean and median.

Compared to the mean, the median value of Total connections is 121 and thus indicates that the distribution

is skewed to the right. I therefore use the natural logarithm of measures of network connectedness in

regressions to reduce the influence of outliers. Similarly, the final sample mainly covers S&P 1500 firms

and thus has distributions of assets, sales, and cash compensation to be also skewed to the right, and I use

the natural logarithm of these variables in regressions to reduce the influence of outliers.

In particular, I use two instruments for network connections in instrumental variable regressions to

address the endogeneity concern that network connectedness may also capture unobservables such as

ability. The motivation and validity of these instruments are discussed in later sections. Number of prior

firms is the number of public firms the executive has previously worked for, and Graduate degree measures

whether the executive has earned a graduate degree. On average, an executive in the sample has worked for

2.59 firms, and 61% of the executives in the final sample have earned a graduate degree. I use the natural

logarithm of Number of prior firms in regressions to reduce the influence of outliers. To measure earnings

management, I use the performance-adjusted cross-sectional variation of the modified Jones model

(Dechow et al. 1995; Kothari et al. 2005). I estimate the residual term in the model as the measure of

discretionary accruals.3 Table 2 shows that the variable Accruals has a mean value of 0.002, which is similar

3 I use the signed value of discretionary accruals to test for earnings management. See Hribar and Nichols (2007) for

discussion of the implications of using signed value and absolute value of discretionary accruals.

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to the mean value of 0.00 reported by Kothari et al. (2005). To reduce the influence of extreme values, I

winsorize distributions of all continuous variables at the 1st and 99th percentiles.

3.3 Econometric Specifications

I use the following empirical model to examine the effects of executive connections on incentive

horizon,

𝑦𝑖𝑗𝑘𝑡 = 𝛼 + 𝛽′𝑛𝑒𝑡𝑤𝑜𝑟𝑘𝑖𝑗𝑘𝑡 + 𝛾′𝑥𝑖𝑗𝑘𝑡 + 𝛿′𝜇𝑘 + 𝜑′𝜈𝑡 + 휀𝑖𝑗𝑘𝑡, (2)

where i indicate individuals, j indicates firms, k indicates industries, and t indicates years. The outcome

variable, 𝑦𝑖𝑗𝑘𝑡, represents the incentive horizon, Incentive duration, for an executive at a firm in a year. The

covariate 𝑛𝑒𝑡𝑤𝑜𝑟𝑘𝑖𝑗𝑘𝑡 is the natural logarithm of network connections, ln(Total connections). The vector

𝑥𝑖𝑗𝑘𝑡 controls for firm and executive characteristics, 𝜇𝑘 represents two-digit SIC industry fixed effects, and

𝜈𝑡 represents year fixed effects. I assume that the executive-firm-year specific error term, 휀𝑖𝑗𝑘𝑡, is

heteroskedastic and correlated within individuals and follow Petersen (2009) in reporting robust standard

errors clustered by executives in all regressions.

Individuals with larger network size often tend to be those with high ability, and the better network

connectedness of these individuals may merely capture their unobserved ability. Furthermore, firms may

also use compensation contracts with longer incentive horizon to retain executives with high aptitude. To

the extent that unobservables such as ability drives both network connections and incentive horizon, any

findings from OLS regressions should be interpreted with caution against causal inferences. Coles,

Lemmon, and Meschke (2012) document that finance researchers face many challenges in addressing

endogeneity, and I do not claim to fully address all conceivable concerns. To mitigate endogeneity issues

as outlined above, I apply two-stage instrumental variable regressions in the following specification,

𝑦𝑖𝑗𝑘𝑡 = 𝛼 + 𝛽𝑛𝑒𝑡𝑤𝑜𝑟𝑘̅̅ ̅̅ ̅̅ ̅̅ ̅̅ ̅̅𝑖𝑗𝑘𝑡 + 𝛾′𝑥𝑖𝑗𝑘𝑡 + 𝛿′𝜇𝑘 + 𝜑′𝜈𝑡 + 휀𝑖𝑗𝑘𝑡, (3)

where 𝑛𝑒𝑡𝑤𝑜𝑟𝑘̅̅ ̅̅ ̅̅ ̅̅ ̅̅ ̅̅𝑖𝑗𝑘𝑡 is estimated from firs-stage instrumental variable regressions using control variables

in Equation (2) and two excluded instruments: the natural logarithm of Number of prior firms and Graduate

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degree. Similar instruments are also used in Faleye, Kovacs, and Venkateswaran (2014). The rationale to

use these instruments is that on the one hand the past working experience and graduate school education

may be mechanically associated with network size, on the other hand it is not clear how the number of prior

firms to work for and the graduate degree could be indicative of an individual’s ability that is relevant in

business world. For example, a high-tech company may be more likely to hire a long-term corporate senior

officer who spends most of her career in a large IT company than some job hoppers who frequently switch

among companies. Moreover, one may also argue that executives who never find the need or time to earn

a graduate degree may be individuals of high aptitude. In later sections, I further discuss the relevancy and

exclusion conditions for these instruments.

4 Results

4.1 Univariate Analysis

Table 3 reports Pearson correlation coefficients between variables of the full sample. Consistent

with the hypothesis, Panel A shows that incentive horizon and executive network connections are positively

correlated, no matter whether the connectedness is measured by total connections, connections via

overlapping employment history, connections via overlapping education history in the same university, or

network size provided by BoardEx. Number of prior firms and Graduate degree are also positively

correlated with measures of incentive horizon. More interestingly, prior work history and graduate

education are indeed positively correlated with network connections with correlation coefficients as high

as 0.56, which seems to support the relevancy condition for instrumental variable regression. Intuitively,

prior work history is more correlated with employment connections than with educational connections,

while graduate education seems opposite. It also suggests that prior work history and graduate education

are much more relevant for network connections than for incentive horizon, which may alleviate the concern

that these excluded instruments directly affect incentive horizon. Panel B provides Pearson correlations for

variables in main regression models. Similar to prior studies (e.g., Gopalan, Milbourn, Song, and Thakor

2014), I also find that incentive horizon is negatively correlated with measures of firm risk (Debt ratio,

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Volatility, and Spread) and corporate governance measures (Entrenchment index and G-index). Firms with

larger size, more long-term assets and R&D investments, more growth options (Market-to-book), better

performance (Excess return), and higher institutional ownership have longer incentive horizon. Executives

with older age, larger cash compensation, and low optimism (Holder30) have shorter incentive horizon.4

Overall, univariate results from correlation matrix are consistent with my hypotheses and prior studies,

which motivate further investigation in multivariate regression frameworks.

4.2 Baseline

Table 4 reports multivariate results for incentive horizon in the full sample. Column (1) include

ln(Total connections), firm characteristics, industry fixed effect, and year fixed effect as independent

variables. Column (2) through (4) further controls for executive characteristics such as position, age and

compensation. Column (5) includes both firm characteristics and executive characteristics and hence

provides the baseline regression. The coefficients on ln(Total connections) are positive for all five

specifications and are statistically significant at the 1% level. Consistent with the hypothesis, better

connected executives have longer incentive horizon. In terms of economic significance, a one-standard-

deviation increase in ln(Total connections) is associated with 6.28% of one standard deviation shock to

Incentive duration.5

Most of the control variables in Table 4 have signs consistent with prior literature. Incentive horizon

for executives is longer in firms that have larger sales, more long-term assets and R&D investments, more

growth opportunities, better stock performance, and higher shares turnover. Firms with higher debt ratio

and more volatile stock performance have shorter incentive horizon for their executives. CEOs have longer

4 For discussion about measures of executives’ overconfidence and low-optimism, see Malmendier and Tate (2005)

and Campbel et al. (2011) 5 1.17 × 0.566 ÷ 10.55 = 0.0628, where 1.17 is the sample standard deviation of ln(Total connections, 0.566 is the

coefficient on ln(Total connections) in Column (5), Table 4, and 10.55 is the sample standard deviation of Incentive

duration.

14

incentive horizon, while executives that are older and have higher cash compensation have shorter incentive

duration.

Consistent with the univariate results, the multivariate results in Table 4 also find that executives

with larger networks have longer incentive horizon. Controlling for common determinants suggested by

prior studies and executive characteristics, I show that network connections are significantly and positively

associated with incentive horizon.

4.3 Subsample Analysis

In Table 5 I examine whether previous results hold in subsample analysis. To account for a potential

exogenous shock to incentive horizon around 2006 due to changes in accounting practices, I divide the full

sample into before-2006 subsample and post-2006 subsample to investigate whether the effect of network

connection on incentive horizon is concentrated in a certain subsample. Columns (1) and (2) of Table 5

show that the coefficients on ln(Total connections) are both positive and significant at the 1% level. It

suggests that despite a potential shock from changes in accounting practices, the positive association

between network connections and incentive horizon is significant before and after such shock. Prior studies

find that firm size is an important determinant of incentive horizon for executives. I then divide the full

sample into small firm subsample and large firm subsample to investigate whether the effect of network

connection on incentive horizon is concentrated in a certain size of firms. The small firm subsample

includes firms with sales smaller than the sample median, while the large firm subsample includes firms

with sales larger than the sample median. Columns (3) and (4) of Table 5 show that the coefficients on

ln(Total connections) are both positive and significant at the 1% level. It seems that the marginal effect of

network connections on incentive horizon is more pronounced in small firm, which may imply that

executives with larger networks are particularly valued by small firms. Lastly, I split the sample into CEO

sample and non-CEO sample. While in both samples the relationship between connections and incentive

duration is positive and significant at the 1% level, the CEO sample exhibits strong economic significance.

In terms of economic significance, a one-standard-deviation increase in ln(Total connections) for a CEO is

15

associated with 7.01% of one standard deviation shock to Incentive duration.6 Overall, results of Table 5

continue to support the hypothesis that executive network connectedness is an effective and important

determinant of incentive horizon.

4.4 Instrumental Variable Regressions

So far I have shown a positive and significant association between an executive’s network

connectedness and her incentive horizon in the full sample and subsamples. Yet it is difficult to estimate

the causal effect of network connectedness on incentive horizon as they may be jointly determined. First,

unobservables such as individual ability are likely to affect both network connections and incentive

horizons. Individuals of high ability may be central in social networks and thus have high connectedness,

while firms may provide long term contracts in order to retain top talents. As a result, such unobservables

are hidden in the error term in Equation (2), and they may cause biases in estimation as they are correlated

with both incentive horizon and network connections. Second, managerial contract is the equilibrium

outcome of a matching mechanism between executives and the board. It is possible that the board has

private information about the growth potential of the firm, and it seeks to hire or retain executives that may

help fulfil the firm’s growth opportunities. For example, when a pharmaceutical firm is planning to start a

new pipeline, the board may hire a well-connected executive officer and offer her long term contracts. The

matching between network connectedness and horizon of executive incentives also challenge the estimation

of causal effect.

To address the endogeneity concern that unobservables such as individual aptitude jointly

determine network connections and incentive horizon, I use two-stage instrumental variable regressions in

Table 6. Consistent with prior studies (Faleye, Kovacs, and Venkateswaran 2014), I explore variations in

the prior employment history and education background and use the number of firms an executive has

6 1.07 × 0.751 ÷ 11.47 = 0.0701, where 1.07 is the CEO sample standard deviation of ln(Total connections, 0.751 is

the coefficient on ln(Total connections) in Column (5), Table 5, and 11.47 is the CEO sample standard deviation of

Incentive duration.

16

previously worked for and whether she has graduate degree as excluded instruments. I examine whether

these instruments are correlated with network connections and are uncorrelated with the error term in

Equation (2).

Panel A of Table 6 reports regressions results in the full sample. Columns (1) through (3) show

reduced form results of instrumental variable regressions, where instruments (ln(Number of prior firms)

and Graduate degree) are directly included in regressions of incentive horizon. The positive and significant

coefficients on the instruments with respect to incentive horizon support the relevancy condition. To

examine the exclusion condition, one needs to argue that the instruments (ln(Number of prior firms) and

Graduate degree) affect incentive horizon only through executive connections. Column (4) reports that

after control for ln(Total connections), coefficients of ln(Number of prior firms) and Graduate degree on

incentive horizon are no longer significant, which is consistent with the exclusion condition. Lastly, Column

(5) reports second-stage results of instrumental variable regressions. The coefficient on ln(Total

connections), instrumented by ln(Number of prior firms), Graduate degree, and second-stage control

variables, is positive and significant at the 1% level. The first stage F statistics are significant at the 1%

level. In unreported tests, weak instrument test are significant at the 1% level and well above the critical

values (around 20) of Stock-Yogo test for weak instruments. Consistent with the reduced form results, these

test statistics reject null hypotheses that the instruments are weak. Hence I find strong evidence to support

the relevancy condition for instruments. Moreover, the Hansen J statistics are not significant at all, not

rejecting the null hypothesis of overidentification. That is, the exclusion condition cannot seem to be

rejected. Therefore, the reduced form results and the econometric tests suggested by prior literature all show

that the instruments used in Table 6 seem to satisfy both the relevancy condition and the exclusion

condition, and hence they are valid instruments. Panel B of Table 6 reports regression results in the CEO

sample and find similar and strong results. In particular, instrumental variable regression in the CEO sample

suggests a strong economic effect of connections on incentive horizon. In terms of economic significance,

a one-standard-deviation increase in ln(Total connections) for a CEO is associated with 12.97% of one

17

standard deviation shock to Incentive duration.7 Taken together, the instrumental variable regressions in

Table 6 confirm what I find in previous multivariate analysis: Network connectedness of an executive

positively affects her incentive horizon.

4.5 Robustness Checks

In Table 7, I investigate the effect of connections on incentive horizon using alternative measures

of incentive horizon, alternative measures of network connectedness, and additional control variables. Panel

A reports regression results with alternative measures of incentive horizon. Instead of measuring incentive

horizon with dollar value of equity grants estimated at the end of a fiscal year, I estimate the dollar value

of equity grants at the grant date and use the grant date fair value of equity grants reported by the company,

respectively. Columns (1) through (2) show that the positive effect of executive connections is robust to

alternative measures of incentive horizon.

Panel B of Table 7 reports regression results with alternative measures of executive connections.

Instead of ln(Total connections), I use ln(Employment connections), ln(Educational connections), and

ln(Network size) to measure network connectedness. Columns (1) and (2) of Table 6 show that connections

via employment history and education history are positively associated with the horizon of executive

compensation, respectively. When put together in Column (3), these two subcategories of network

connectedness also jointly determine the horizon of executive incentives. Moreover, Column (3) of Table

6 finds that the coefficient on ln(Employment connections) is significantly larger than that on

ln(Educational connections) at the 1% level. It seems to imply that the marginal effect of employment-

induced connections on incentive horizon is stronger than that of education-induced connections. Columns

(4) and (5) of Table 6 include ln(Network size) in regressions and show that network connectedness

measured by the size of the total network of an executive is also positively and significantly associated with

7 1.01 × 1.466 ÷ 11.42 = 0.1297, where 1.01 is the CEO sample standard deviation of ln(Total connections, 1.466 is

the coefficient on ln(Total connections) in Column (5) of Panel B, Table 6, and 11.42 is the CEO sample standard

deviation of Incentive duration.

18

incentive horizon. In sum, while I find that incentive horizon is more affected by network connections based

on past employment than past education, alternative measures of network connectedness point to the same

finding: better connected executives have longer horizon of incentives.

Panel C of Table 7 reports regression results with additional control variables. While previous

regressions include control variables suggested by prior studies (Gopalan, Milbourn, Song, and Thakor

2014), it is possible that I haven’t exhausted the potential set of determinants of incentive horizon. Columns

(1) through (7) show that firms with better corporate governance (Entrenchment index, G-index, and Board

independence) have longer incentive horizon for their executives and CEOs that are optimistic have longer

incentive horizon (Holder67, Holder30). In untabulated tests, I also find that firms with institutional

ownership higher than its sample median and spread lower than its sample median have longer incentive

horizon. This is consistent with hypotheses that long-term contracts are desirable for firms with more long-

term perspective. Overall, the positive effect of executive connections on incentive horizon is robust to

alternative measures of incentive horizon, alternative measures of network connectedness, and additional

control variables.

4.6 Heterogeneity in the Effect of Executive Connections on Incentive Horizons

Previous results show that executive connections are positively and significantly associated with

incentive horizon, I then examine through what channel executive connections affect incentive horizon in

Table 8 by investigating interaction terms between connections and relevant control variables.

The effect of network connection may be dampened for firms with greater risk. On one hand the

executive may demand short-term contracts due to greater uncertainty of the firm. On the other hand the

board may find it less feasible to implement long-term strategy due to current risk imposed to the firm.

Columns (1) and (2) of Table 8 show results that support this conjecture. The effect of network

connectedness on incentive horizon decreases in debt ratio and stock volatility.

Network connections may be more valuable to firms with more growth options, as the potential

access to information through network may help explore these growth options in the future. Hence firms

19

are more likely to design long-term contracts to match their growth options with the executive’s network

capital. Column (3) of Table 8 reports a positive coefficient on the interaction term of network

connectedness and market-to-book ratio, although not statistically significant. Untabulated tests show that

in the CEO sample the effect of CEO connectedness on incentive horizon significantly increases in market-

to-book ratio at the 5% level.

Column (4) reports that the effect of executive connectedness on incentive horizon significantly

decreases in high institutional ownership at the 1% level. Prior studies find that institutional ownership is

positively related to the quality of corporate governance (Chung and Zhang 2011). Corporate governance

and executive connectedness may be substitutes to affect incentive horizon as the firm may not need long-

term contract to restrict the executive if the quality of governance is good enough to foster long-term bond

between the firm and the executive. In that sense, results in Column (4) support the substitution effect of

network connectedness and corporate governance.

Lastly, Column (5) of Table 8 shows that the effect of executive connectedness on incentive horizon

significantly decreases in firms with high spread at the 1% level. Consistent with Bolton, Scheinkman and

Xiong (2006), it becomes more costly to implement long-term contracts between the firm and the executive

as short-term mispricing increases. Hence short-term mispricing dampens the effect of executive

connectedness on incentive horizon. However, perhaps more evidence is needed to further test the short-

term mispricing channel.

Taken together, I find evidence on the heterogeneity in the effect of executive connections on

incentive horizon by testing interaction terms between connections and relevant control variables. I show

that the effect of executive connections on incentive horizon is stronger in firms with more growth options

and weaker in firms with greater risk and more short-term mispricing. I also find that for firms with good

governance, long incentive horizon is not used to retain executives with large networks. These results seem

consistent with the hypotheses that executive’s network connectedness affects incentive horizon through

the long-term perspective channel.

20

4.7 Additional Endogeneity Concerns

While the effect of executive connections on incentive horizons is strong in instrumental variable

regressions and remains significant after a battery of robustness tests, I may face additional challenges in

addressing endogeneity as follows. First, the exclusion restriction in instrumental variable regressions may

be violated if instruments directly affect outcome variables. For instance, the propensity for a manager to

have a graduate degree may proxy her aptitude or risk aversion and thus directly impact her compensation.

In untabulated tests, I use only one instrument, ln(Number of prior firms), to instrument for executive

connections and find robust results. It suggests that even if the instrument Graduate degree may not satisfy

the exclusion restriction, I still find consistent results using only ln(Number of prior firms) as the instrument.

Second, I measure a manager’s connections by counting the number of her personal associations with top

executives or directors through employment and education networks. Previous studies have shown that it

is not suitable to include manager fixed effects or firm fixed effects because this type of “elite” executive

connections display very little time-series variation over the manager’s tenure (Engelberg, Gao, and Parsons

2013). I could alternatively rely on the cross-sectional variations to perform additional tests. For example,

school fixed effects could be included so that the effect of executive connections on incentive horizons is

estimated within alumni from the same school. I could also examine connections that were built at the early

stage of a manager’s career and hence are less likely to be predetermined by the current career objective of

the manager. Third, it is also possible that executive connections capture managerial power. While

managerial power is unobserved, powerful executives are often accompanied by weak corporate boards. I

control for various measures of corporate governance in Table 7 and show that the positive effect of

executive connections on incentive horizons is robust and thus not the byproduct of managerial power.

Lastly, while I hypothesize that better connected executives have less short-termism because larger

networks give them better access to information and protect them against adverse job outcomes, these

channels haven’t been directly examined in this version of the paper. I plan to test the implications of these

channels in the next version of the paper. For instance, the access-to-information channel would predict

stronger effects in connections that contain more valuable information. The labor-market-insurance channel

21

would predict stronger effects in connections that are more likely to alleviate career concerns. The detailed

biographical information I have on managers would allow me to further explore cross-sectional variations

in executive connections and examine the channels through which social networks affect incentive

horizons.

4.8 The Effect of Executive Connections on Earnings Management

I examine the effect of executive connections on earnings management by analyzing discretionary

accruals. Using the performance-adjusted cross-sectional variation of the modified Jones model (Dechow

et al. 1995; Kothari et al. 2005), I estimate the residual term in the model as the measure of discretionary

accruals and also split discretionary accruals into positive and negative accruals to examine whether

executive connectedness is associated with earnings-enhancing accruals.

The results are reported in Table 9. Panel A shows that for total accruals, discretionary accruals,

and positive accruals, the coefficients on ln(Total connections) are negative and statistically significant at

the 1% level. In contrast, the relation between executive connectedness and negative accruals is only

significant at the 10% level. This indicates that executives with larger networks engage less in earnings-

enhancing accruals, and it is consistent with the hypothesis that network connections reduce managerial

myopia. Panel B tests whether executive connectedness affects earnings management through incentive

horizon. I first regress Incentive duration on ln(Total connections) and control variables for accruals and

then use the predicted Incentive duration in the accruals regression models. If executive connectedness

affects earnings management through incentive horizon, the coefficient of Predicted (Incentive duration)

should be negative, as variations in the fitted value come from ln(Total connections) after the first-stage

control variables are also included in the second-stage regression. Results in Panel B show that the

coefficient of Predicted (Incentive duration) are negative and significant at the 1% level for total accruals,

discretionary accruals, and positive accruals.

To test whether the negative relationship between executive connections and accruals may be more

than correlation, I use instrumental variable regressions, where ln(Total connections) is estimated by

22

ln(Number of prior firms), Graduate degree, and second-stage control variables. Panel C of Table 9 shows

that the instrumented ln(Total connections) has negative and significant coefficients at the 5% level on total

accruals and discretionary accruals, although the effect is only significant at the 10% level on positive

accruals and negative accruals. The first stage F statistics are significant at the 1% level, and the Hansen J

statistics are insignificant. This seems to support the relevancy condition and exclusion condition of

instrumental variable regressions. In sum, I show strong evidence that executive with large networks engage

in less earnings management, especially in earnings-enhancing accruals.

5 Conclusions

The excessive focus of corporate managers on short-term results has attracted heightened attention

from researchers and practitioners since the recent financial crisis. While prior literature examine the role

of firm characteristics such as monitoring in managerial short-termism, few papers have looked into

managerial attributes that may alleviate short-termism. In this paper, I study the role of executive

connections in mitigating managerial short-termism through access to information and labor market

insurance. I hypothesize that better connected executives have less short-termism because larger networks

give them better access to information and provide them with more labor market insurances against career

concerns, which lead to more willingness to choose long-term projects.

I construct measures of incentive horizons and executive connections in a sample of more than

13,000 executives in S&P 1500 firms from 2000 to 2013. I use the duration of managerial compensation to

measure incentive horizons and the total number of executive connections to other individuals in elite

corporations to measure the executive’s network centrality. I find strong evidence that better connected

executives have significantly longer horizons in compensation incentives in the full sample and subsamples.

To mitigate the endogeneity concern due to omitted variables, I explore variations in past employment and

education history to construct instruments for executive connections. In instrumental variable regressions,

I find that network connectedness of an executive significantly and positively affects her incentive horizon.

This positive relationship is robust to alternative measures of incentive horizons, alternative measures of

23

network connections, and additional control variables. Furthermore, I show that the effect of executive

connections on incentive horizons is stronger in firms with more growth options and weaker in firms with

greater risk. Lastly, I show strong evidence that executives with larger networks engage in less earnings

management, and executive connectedness affects earnings management through the channel of incentive

horizons. Taken together, the empirical evidence strongly supports that better connected executives have

longer incentive horizons and thus exhibit less managerial myopia.

I contribute to two strands of literatures. First, I contribute to the literature on managerial short-

termism. Existing studies have investigated whether and how managerial short-termism can be mitigated

by enhancing monitoring (e.g., Bushee 1998; Farber 2005; Cadman and Sunder 2014). I extend this

literature by documenting that managers’ access to network information and career concerns also play

significant roles in affecting managerial short-termism. Second, I contribute to the literature on social

networks in finance. Fracassi (2014) find that external networks of managers help the firm obtain

information advantages to improve its investment decisions and to achieve better performance. Engelberg,

Gao, and Parsons (2012) find that firms with managers connected to bankers have lower interest rates. My

results imply that executive connections may have a positive effect in inducing long-term corporate

behaviors. In particular, executive connections increase the manager’s willingness to choose desirable yet

risky long-term projects by providing valuable network information and a safety net against adverse job

outcomes. In addition, my results suggest that the manager’s network centrality may facilitate corporate

long-term behaviors via the channel of her incentive horizon.

24

Appendix

Table A1 - First-stage of IV Regressions

The table reports first-stage results of instrumental variable regressions that examine the effect of executive

connections on incentive horizons in the full sample and the CEO sample. The sample period is from 2000

through 2013. Dependent variables are ln(Total connections). The excluded instruments are ln(Number of

prior firms) and Graduate degree. See Table 6 for corresponding second-stage results. Variable definitions

are provided in Table 1. 2-digit SIC industry and year fixed effects are included. Robust standard errors are

adjusted for clustering by executive and presented in the parenthesis. ***, **, and * indicate significance

at the 1%, 5%, and 10% levels, respectively.

All executives CEO

(1) (2)

ln(Number of prior firms) 1.211*** 1.032***

(0.0292) (0.0637)

Graduate degree 0.345*** 0.352***

(0.0201) (0.0444)

ln(Sales) 0.253*** 0.289***

(0.00898) (0.0186)

Long-term assets -0.0316 -0.00479

(0.0589) (0.125)

R&D 3.009*** 2.886***

(0.248) (0.544)

Debt ratio 0.0671 0.0711

(0.0541) (0.114)

Market-to-book 0.0243*** 0.0341**

(0.00818) (0.0169)

Excess return -0.785*** -0.864***

(0.149) (0.287)

Volatility 2.958*** 2.170

(0.751) (1.572)

Share turnover 0.000531 -0.00373

(0.00129) (0.00293)

CEO 0.350*** -

(0.0253)

ln(Age) -0.319*** -0.720***

(0.0763) (0.175)

ln(Cash pay) 0.144*** 0.0645**

(0.0166) (0.0328)

Constant 0.518 3.473***

(0.350) (0.800)

First stage F statistic 1070.11*** 175.76***

Industry and Year FE Yes Yes

Observations 44,538 9,040

Adjusted R2 0.356 0.381

25

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Table 1 - Variable Definitions

Variable Definition

Incentive horizon

Incentive duration The weighted average duration of components of compensation (salary,

bonus, restricted stock, and stock options) as in Equation (1) of Gopalan,

Milbourn, Song, and Thakor (2014):

(𝑆𝑎𝑙𝑎𝑟𝑦+𝐵𝑜𝑛𝑢𝑠)×0 + ∑ 𝑅𝑒𝑠𝑡𝑟𝑖𝑐𝑡𝑒𝑑 𝑠𝑡𝑜𝑐𝑘𝑖× 𝑡𝑖+ ∑ 𝑂𝑝𝑡𝑖𝑜𝑛𝑗× 𝑡𝑗

𝑛𝑜𝑗=1

𝑛𝑠𝑖=1

𝑆𝑎𝑙𝑎𝑟𝑦+𝐵𝑜𝑛𝑢𝑠 + ∑ 𝑅𝑒𝑠𝑡𝑟𝑖𝑐𝑡𝑒𝑑 𝑠𝑡𝑜𝑐𝑘𝑖+ ∑ 𝑂𝑝𝑡𝑖𝑜𝑛𝑗𝑛𝑜𝑗=1

𝑛𝑠𝑖=1

,

where Salary and Bonus are the dollar values of yearly salary and bonus,

Restricted stocki is the dollar value of the ith equity of restricted stocks

with vesting period ti (in months), and Optionj is the dollar value of the jth

equity compensation of stock options with vesting period tj (in months).

In the year t, an executive may be awarded multiple equity grants with

various vesting periods, and ns and no are the total number of such grants

in stock and options, respectively. Dollar value of equity awards are

estimated at the end of a fiscal year.

Incentive durationGD The weighted average duration of components of compensation (salary,

bonus, restricted stock, and stock options) as in Equation (1) of Gopalan,

Milbourn, Song, and Thakor (2014). Compared to Incentive duration,

Incentive durationGD estimates dollar value of equity awards at the date

when the awards are granted.

Incentive durationFV The weighted average duration of components of compensation (salary,

bonus, restricted stock, and stock options) as in Equation (1) of Gopalan,

Milbourn, Song, and Thakor (2014). Compared to Incentive duration,

Incentive durationFV uses the grant date fair value reported by the firm to

measure the dollar value of equity awards.

Executive network

Total connections The number of individuals with whom the executive shares an overlapping

employment history in a public firm or an overlapping educational history

in a university, which excludes those individuals who are employed by the

executive’s current firm.

Employment connections The number of individuals with whom the executive shares an overlapping

employment history in a public firm, which excludes those individuals

who are employed by the executive’s current firm.

Educational connections The number of individuals with whom the executive shares an overlapping

educational history in a university, which excludes those individuals who

are employed by the executive’s current firm.

Network size The size of the executive’s network, provided by BoardEx.

Number of prior firms The number of public firms the executive has previously worked for.

30

Table 1 (Continued)

Graduate degree Equal to one if the executive has earned a graduate degree and zero

otherwise.

Firm and executive characteristics

Accruals Signed discretionary accruals, the residual term estimated from the

performance-adjusted cross-sectional variation of the modified model

(Dechow et al. 1995; Kothari et al. 2005).

Age The executive’s age in the data year.

Assets Firm’s total assets.

Board independence The number of independent directors/board size.

Cash pay The sum of the executive’s annual salary and bonus

Cashflows Cash flows from operating activities/lagged total assets.

CEO Equal to one if the executive is a CEO and zero otherwise.

Debt ratio (Long-term debt + short-term debt)/total assets.

Entrenchment index The Bebchuk, Cohen, and Ferrell (2009) entrenchment index.

Excess return The average monthly stock returns in excess of market returns.

G-index The Gompers, Ishii, and Metrick (2001) governance index.

High inst. ownership Equal to one if the stock ownership by institutional investors is greater

than the sample median and zero otherwise.

High spread Equal to one if the average daily stock bid-ask spread in a year is greater

than the sample median and zero otherwise.

Holder30 Equal to one if starting from the first year when the person has a pattern

that average per-exercised-option value < 30% of average strike price

for at least two years during the career in a firm and zero otherwise. See

Malmendier and Tate (2005) and Campbel et al. (2011).

Holder67 Equal to one if starting from the first year when the person has a pattern

that average per-unexercised-option value > 67% of average strike price

for at least two years during the career in a firm and zero otherwise. See

Malmendier and Tate (2005) and Campbel et al. (2011).

Institutional ownership The stock ownership by institutional investors.

Long-term assets (PP&E + goodwill) / non-cash assets.

Market-to-book Market value of assets / book value of assets.

Negative accruals Negative discretionary accruals, which is the negative value of the the

residual term estimated from the performance-adjusted cross-sectional

variation of the modified Jones model (Dechow et al. 1995; Kothari et al.

2005).

Positive accruals Positive discretionary accruals, which is the positive value of the the

residual term estimated from the performance-adjusted cross-sectional

31

Table 1 (Continued)

variation of the modified Jones model (Dechow et al. 1995; Kothari et al.

2005).

R&D R&D expenses/total assets, where missing values in R&D are coded as

zero.

Sales Firm’s sales.

Sales growth The firm’s annual growth rate of sales.

S.D. (Cashflows) The standard deviation of the ratio of cash flows to lagged total assets over

previous five years.

S.D. (Sales growth) The standard deviation of yearly sales growth rate of the firms during the

previous five years.

Share turnover The average of (daily share trading volume/shares outstanding) during the

data year.

Spread The average daily stock bid-ask spread in a year.

Total accruals (Income before extraordinary items – operating cash flows)/lagged total

assets.

Volatility Standard deviation of daily stock returns in a year.

32

Table 2 - Summary Statistics

The table provides the summary statistics of the full sample. The sample period is from 2000 to 2013.

Variable definitions are provided in Table 1.

Variable N Mean S.D. 25th % Median 75th %

Incentive horizon

Incentive duration (months) 51,452 18.39 10.55 11.59 18.80 24.65

Incentive durationGD (months) 51,089 18.28 10.60 11.43 18.76 24.59

Incentive durationFV (months) 37,959 19.29 9.40 12.79 19.02 24.67

Executive network

Total connections 51,452 203.03 233.36 46 121 267

Employment connections 51,452 125.75 184.49 26 55 134

Educational connections 51,452 76.57 107.16 1 27 114

Network size 51,452 539.28 538.88 142 357 751

Number of prior firms 51,438 2.59 0.89 2 2 3

Graduate degree 44,548 0.61 0.48 0 1 1

Firm & executive characteristics

Assets ($ millions) 51,452 18,118 46,041 1,754 4,278 13,659

Sales ($ millions) 51,452 8,537 15,866 1,211 2,990 8,227

Long-term assets 51,452 0.42 0.24 0.24 0.43 0.60

R&D 51,452 0.026 0.050 0 0 0.030

Debt ratio 51,452 0.24 0.19 0.098 0.22 0.35

Market-to-book 51,452 1.96 1.23 1.16 1.53 2.25

Excess return 51,452 0.0087 0.031 -0.0084 0.0059 0.023

Volatility 51,452 0.026 0.014 0.016 0.022 0.031

Share turnover 51,452 10.83 8.26 5.27 8.45 13.68

CEO 51,452 0.18 0.39 0 0 0

Age 51,452 51.87 6.74 47 52 57

Cash pay 51,452 849,698 830,004 405,381 587,090 955,001

Entrenchment index 19,784 2.56 1.28 2 3 4

G-index 28,403 9.46 2.57 8 9 11

Board independence 51,419 0.73 0.16 0.67 0.77 0.86

High Inst. ownership 50,780 0.50 0.50 0 0 1

Institutional ownership 50,780 0.78 0.40 0.65 0.79 0.91

Holder67 43,883 0.43 0.49 0 0 1

Holder30 37,070 0.072 0.26 0 0 0

High spread 51,452 0.50 0.50 0 1 1

Spread 51,452 0.033 0.017 0.022 0.029 0.040

Sales growth 51,419 0.10 0.24 -0.010 0.074 0.17

Cashflows 50,431 0.11 0.095 0.055 0.10 0.16

S.D. (Sales growth) 50,127 0.24 0.41 0.071 0.13 0.24

S.D. (Cashflows) 51,354 0.056 0.065 0.020 0.037 0.067

Earnings management

Total accruals 50,367 -0.060 0.071 -0.088 -0.051 -0.022

Accruals 50,367 0.0024 0.062 -0.026 0.0035 0.034

Positive accruals 27,072 0.043 0.040 0.014 0.032 0.059

Negative accruals 23,295 -0.044 0.048 -0.058 -0.029 -0.012

33

Table 3 - Correlation Matrix

The table reports Pearson correlation coefficients for variables in the full sample. The sample period is from

2000 to 2013. Variable definitions are provided in Table 1. Bolded coefficients are significant at the 5%

level.

Panel A: Pearson correlations for incentive horizon and executive network measures

Variable 1 2 3 4 5 6 7 8

1 Incentive duration

2 Incentive duration GD 0.95

3 Incentive duration FV 0.88 0.89

4 ln(Total connections) 0.17 0.17 0.15

5 ln(Employment connections) 0.18 0.19 0.17 0.78

6 ln(Educational connections) 0.08 0.08 0.07 0.71 0.24

7 ln(Network size) 0.15 0.15 0.14 0.77 0.59 0.59

8 ln(Number of prior firms) 0.07 0.07 0.05 0.42 0.56 0.12 0.30

9 Graduate degree 0.04 0.03 0.02 0.21 0.10 0.20 0.17 0.07

34

Table 3 (Continued)

Panel B: Pearson correlations for variables in main regression models

V

aria

ble

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2

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Ince

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tion

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ln(T

ota

l co

nnec

tions)

0.1

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ln(S

ales

) 0.1

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Long

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R&

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Deb

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ket

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Exce

ss r

eturn

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Vola

tili

ty

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Shar

e tu

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0.1

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O

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8

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ln(A

ge)

-0

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ln(C

ash p

ay)

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Entr

ench

men

t in

dex

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4

-0.0

4

15

G-i

ndex

-0

.06

0.0

1

0.1

1

0.0

4

-0.1

1

0.0

5

-0.1

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0.0

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-0.1

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1

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Boar

d i

ndep

enden

ce

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ow

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ship

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er67

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er30

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ad

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35

Table 4 - The Effect of Executive Connections on Incentive Horizons

The table examines the effect of executive connections on incentive horizons in the full sample. The sample

period is from 2000 to 2013. Dependent variables are Incentive duration. Variable definitions are provided

in Table 1. 2-digit SIC industry and year fixed effects are included. Robust standard errors are adjusted for

clustering by executive and presented in the parenthesis. ***, **, and * indicate significance at the 1%, 5%,

and 10% levels, respectively.

(1) (2) (3) (4) (5)

ln(Total connections) 0.681*** 0.509*** 0.676*** 0.786*** 0.566***

(0.0667) (0.0670) (0.0666) (0.0677) (0.0669)

ln(Sales) 1.377*** 1.419*** 1.432*** 1.608*** 2.002***

(0.0661) (0.0658) (0.0662) (0.0712) (0.0712)

Long-term assets 2.552*** 2.509*** 2.525*** 2.512*** 2.354***

(0.456) (0.454) (0.454) (0.457) (0.453)

R&D 18.48*** 19.33*** 18.20*** 18.58*** 19.94***

(1.845) (1.834) (1.836) (1.851) (1.825)

Debt ratio -1.943*** -1.927*** -1.938*** -1.889*** -1.794***

(0.438) (0.434) (0.437) (0.438) (0.431)

Market-to-book 1.304*** 1.302*** 1.295*** 1.307*** 1.295***

(0.0717) (0.0720) (0.0711) (0.0716) (0.0711)

Excess return 17.63*** 17.63*** 17.75*** 18.91*** 20.44***

(1.627) (1.625) (1.622) (1.621) (1.605)

Volatility -26.87*** -25.41*** -29.83*** -30.84*** -35.91***

(6.329) (6.304) (6.302) (6.328) (6.259)

Share turnover 0.0962*** 0.0967*** 0.0928*** 0.0975*** 0.0958***

(0.0106) (0.0105) (0.0106) (0.0107) (0.0105)

CEO 2.115*** 4.017***

(0.196) (0.222)

ln(Age) -4.302*** -5.143***

(0.546) (0.552)

ln(Cash pay) -1.068*** -2.221***

(0.134) (0.147)

Constant 1.592* 1.702* 18.36*** 13.45*** 46.52***

(0.894) (0.887) (2.311) (1.728) (2.750)

Industry fixed effect Yes Yes Yes Yes Yes

Year fixed effect Yes Yes Yes Yes Yes

Observations 51,452 51,452 51,452 51,452 51,452

Adjusted R2 0.147 0.153 0.150 0.150 0.167

36

Table 5 - Subsample Analysis

The table examines the effect of executive connections on incentive horizons in subsamples. The sample

period is from 2000 to 2013. Dependent variables are Incentive duration. Columns 1 and 2 report regression

results in subsamples with time period from 2000 to 2005 and from 2006 to 2012, respectively. Columns 3

and 4 report regression results in subsamples with sales size below the sample median and above the sample

median, respectively. Columns 5 and 6 report regression results in CEO sample and non-CEO sample,

respectively. Variable definitions are provided in Table 1. 2-digit SIC industry and year fixed effects are

included. Robust standard errors are adjusted for clustering by executive and presented in the parenthesis.

***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

2000-2005 2006-2013 Small Firm Large Firm CEO Non-CEO

(1) (2) (3) (4) (5) (6)

ln(Total connections) 0.586*** 0.519*** 0.612*** 0.374*** 0.751*** 0.528***

(0.0967) (0.0780) (0.0883) (0.0937) (0.188) (0.0700)

ln(Sales) 1.556*** 2.238*** 1.638*** 2.200*** 1.955*** 1.999***

(0.107) (0.0808) (0.138) (0.120) (0.188) (0.0756)

Long-term assets 2.605*** 2.498*** 2.001*** 3.031*** 1.521 2.586***

(0.659) (0.556) (0.600) (0.660) (1.205) (0.475)

R&D 17.86*** 18.79*** 13.15*** 22.80*** 19.23*** 19.97***

(2.745) (2.171) (2.159) (3.680) (4.641) (1.947)

Debt ratio -1.412** -1.015** -0.980** -3.512*** -1.139 -1.957***

(0.665) (0.463) (0.497) (0.775) (1.172) (0.451)

Market-to-book 1.110*** 1.280*** 1.111*** 1.589*** 0.898*** 1.388***

(0.0961) (0.0940) (0.0840) (0.120) (0.175) (0.0759)

Excess return 9.165*** 34.56*** 18.39*** 24.47*** 20.78*** 20.40***

(2.439) (1.975) (2.114) (2.306) (3.922) (1.749)

Volatility 15.47 -116.9*** -36.34*** -48.62*** -33.69** -35.84***

(9.768) (7.768) (8.098) (9.858) (16.37) (6.692)

Share turnover 0.122*** 0.0897*** 0.139*** 0.0117 0.0876*** 0.0966***

(0.0171) (0.0116) (0.0129) (0.0173) (0.0300) (0.0110)

CEO 3.096*** 4.521*** 3.724*** 4.265*** - -

(0.308) (0.269) (0.317) (0.291)

ln(Age) -5.877*** -3.996*** -5.397*** -4.505*** -9.533*** -4.214***

(0.776) (0.658) (0.708) (0.791) (1.579) (0.569)

ln(Cash pay) -1.334*** -2.788*** -2.141*** -2.289*** -2.295*** -2.193***

(0.196) (0.198) (0.213) (0.193) (0.346) (0.159)

Constant 35.84*** 49.15*** 50.23*** 44.13*** 70.08*** 42.34***

(3.764) (3.367) (3.734) (3.845) (7.502) (2.885)

Industry fixed effect Yes Yes Yes Yes Yes Yes

Year fixed effect Yes Yes Yes Yes Yes Yes

Observations 23,249 28,203 25,724 25,728 9,491 41,961

Adjusted R2 0.128 0.187 0.160 0.184 0.153 0.166

37

Table 6 - Instrumental Variable Regressions

The table examines the effect of executive connections on incentive horizons using instrumental variable

regressions in the full sample and the CEO sample, respectively. The sample period is from 2000 to 2013.

Dependent variables are Incentive duration. Columns (1) through (4) report reduced form results. Column

(5) reports second-stage results of instrumental variable regressions, where ln(Total connections) is

estimated by ln(Number of prior firms), Graduate degree, and second-stage control variables. See

Appendix Table A1 for corresponding first-stage results. Variable definitions are provided in Table 1. 2-

digit SIC industry and year fixed effects are included. Robust standard errors are adjusted for clustering by

executive and presented in the parenthesis. ***, **, and * indicate significance at the 1%, 5%, and 10%

levels, respectively.

Panel A: All executives Reduced form of IV IV

(1) (2) (3) (4) (5)

ln(Total connections) 0.474*** 0.617***

(0.0823) (0.164)

ln(Number of prior firms) 0.696*** 0.665*** 0.0910

(0.216) (0.216) (0.235)

Graduate degree 0.341** 0.316** 0.152

(0.154) (0.154) (0.156)

ln(Sales) 2.156*** 2.151*** 2.149*** 2.029*** 1.995***

(0.0739) (0.0739) (0.0738) (0.0755) (0.0846)

Long-term assets 2.280*** 2.300*** 2.273*** 2.288*** 2.292***

(0.485) (0.485) (0.485) (0.486) (0.486)

R&D 21.52*** 21.75*** 21.34*** 19.91*** 19.50***

(1.878) (1.872) (1.878) (1.882) (1.962)

Debt ratio -2.068*** -2.052*** -2.083*** -2.115*** -2.123***

(0.463) (0.463) (0.464) (0.463) (0.463)

Market-to-book 1.294*** 1.293*** 1.296*** 1.284*** 1.280***

(0.0747) (0.0748) (0.0747) (0.0747) (0.0747)

Excess return 19.01*** 19.00*** 18.99*** 19.36*** 19.48***

(1.714) (1.713) (1.714) (1.713) (1.721)

Volatility -30.32*** -29.77*** -30.42*** -31.83*** -32.29***

(6.644) (6.638) (6.643) (6.628) (6.643)

Share turnover 0.0869*** 0.0882*** 0.0869*** 0.0866*** 0.0864***

(0.0113) (0.0112) (0.0112) (0.0113) (0.0113)

CEO 4.336*** 4.347*** 4.334*** 4.168*** 4.117***

(0.229) (0.229) (0.229) (0.228) (0.239)

ln(Age) -4.907*** -4.897*** -4.922*** -4.771*** -4.723***

(0.593) (0.594) (0.593) (0.590) (0.595)

ln(Cash pay) -2.302*** -2.280*** -2.297*** -2.365*** -2.389***

(0.157) (0.157) (0.157) (0.157) (0.157)

Constant 47.50*** 47.66*** 47.40*** 47.15*** 47.62***

(2.937) (2.933) (2.936) (2.928) (3.280)

First stage F statistic 1070.11***

Hansen J statistic 0.57

Industry and Year FE Yes Yes Yes Yes Yes

Observations 44,538 44,538 44,538 44,538 44,538

Adjusted R2 0.162 0.162 0.162 0.164 0.163

38

Table 6 (Continued)

Panel B: CEO only Reduced form of IV IV

(1) (2) (3) (4) (5)

ln(Total connections) 0.526** 1.466***

(0.219) (0.443)

ln(Number of prior firms) 1.416*** 1.357*** 0.814

(0.518) (0.520) (0.566)

Graduate degree 0.733* 0.686* 0.501

(0.392) (0.392) (0.399)

ln(Sales) 2.162*** 2.161*** 2.149*** 1.997*** 1.727***

(0.185) (0.185) (0.184) (0.193) (0.222)

Long-term assets 1.897 2.003 1.911 1.914 1.905

(1.219) (1.226) (1.222) (1.221) (1.218)

R&D 20.22*** 20.85*** 19.78*** 18.26*** 15.54***

(4.664) (4.646) (4.635) (4.658) (4.758)

Debt ratio -0.778 -0.690 -0.811 -0.848 -0.920

(1.210) (1.205) (1.210) (1.208) (1.206)

Market-to-book 0.950*** 0.959*** 0.956*** 0.938*** 0.904***

(0.178) (0.179) (0.178) (0.177) (0.176)

Excess return 19.53*** 19.40*** 19.39*** 19.84*** 20.69***

(4.011) (4.015) (4.011) (4.014) (4.027)

Volatility -26.16 -23.48 -25.65 -26.79 -29.18*

(16.42) (16.38) (16.38) (16.44) (16.50)

Share turnover 0.0710** 0.0739** 0.0705** 0.0725** 0.0758**

(0.0307) (0.0305) (0.0306) (0.0307) (0.0310)

ln(Age) -8.991*** -9.106*** -9.063*** -8.685*** -7.986***

(1.601) (1.606) (1.595) (1.588) (1.662)

ln(Cash pay) -2.356*** -2.297*** -2.343*** -2.377*** -2.446***

(0.353) (0.353) (0.351) (0.353) (0.357)

Constant 69.33*** 69.82*** 69.20*** 67.37*** 63.88***

(7.670) (7.683) (7.655) (7.600) (8.341)

First stage F statistic 175.76***

Hansen J statistic 0.25

Industry and Year FE Yes Yes Yes Yes Yes

Observations 9,040 9,040 9,040 9,040 9,040

Adjusted R2 0.147 0.147 0.148 0.149 0.145

39

Table 7 - Robustness Checks

The table examines the robustness of the effect of executive connections on incentive horizons using

alternative measures of incentive horizon, alternative measures of connections, and additional control

variables. The sample period is from 2000 to 2013. Dependent variables are Incentive duration. Variable

definitions are provided in Table 1. 2-digit SIC industry and year fixed effects are included. Robust standard

errors are adjusted for clustering by executive and presented in the parenthesis. ***, **, and * indicate

significance at the 1%, 5%, and 10% levels, respectively.

Panel A: Alternative measures of incentive horizons

Incentive durationGD Incentive durationFV

(1) (2)

ln(Total connections) 0.531*** 0.434***

(0.0670) (0.0688)

ln(Sales) 2.032*** 2.005***

(0.0715) (0.0709)

Long-term assets 2.182*** 2.145***

(0.460) (0.461)

R&D 18.77*** 14.15***

(1.868) (1.818)

Debt ratio -1.889*** -2.024***

(0.440) (0.459)

Market-to-book 1.355*** 1.367***

(0.0709) (0.0763)

Excess return -17.50*** -11.14***

(1.627) (1.702)

Volatility -21.86*** -44.19***

(6.305) (6.661)

Share turnover 0.116*** 0.0704***

(0.0104) (0.0108)

CEO 3.888*** 4.962***

(0.223) (0.226)

ln(Age) -4.886*** -3.055***

(0.551) (0.573)

ln(Cash pay) -2.094*** -2.414***

(0.146) (0.169)

Constant 43.93*** 43.40***

(2.748) (2.922)

Industry fixed effect Yes Yes

Year fixed effect Yes Yes

Observations 51,089 37,959

Adjusted R2 0.163 0.197

40

Table 7 (Continued)

Panel B: Alternative measures of executive connections

(1) (2) (3) (4) (5)

ln(Employment connections) 0.555*** 0.502*** 0.392***

(0.0650) (0.0656) (0.0741)

ln(Educational connections) 0.193*** 0.154*** 0.0920**

(0.0319) (0.0321) (0.0373)

ln(Network size) 0.595*** 0.286***

(0.0725) (0.0949)

ln(Sales) 1.969*** 2.130*** 1.954*** 2.005*** 1.928***

(0.0717) (0.0702) (0.0718) (0.0711) (0.0720)

Long-term assets 2.352*** 2.369*** 2.355*** 2.427*** 2.386***

(0.451) (0.453) (0.452) (0.454) (0.452)

R&D 19.82*** 22.00*** 19.37*** 20.33*** 19.03***

(1.836) (1.816) (1.830) (1.822) (1.829)

Debt ratio -1.835*** -1.702*** -1.819*** -1.801*** -1.841***

(0.430) (0.431) (0.430) (0.432) (0.431)

Market-to-book 1.297*** 1.295*** 1.293*** 1.299*** 1.294***

(0.0709) (0.0713) (0.0709) (0.0711) (0.0709)

Excess return 20.44*** 20.13*** 20.49*** 20.38*** 20.55***

(1.605) (1.607) (1.605) (1.603) (1.603)

Volatility -36.23*** -33.59*** -36.16*** -35.26*** -36.43***

(6.268) (6.255) (6.249) (6.255) (6.246)

Share turnover 0.0975*** 0.0960*** 0.0957*** 0.0966*** 0.0958***

(0.0105) (0.0105) (0.0105) (0.0105) (0.0105)

CEO 4.071*** 4.118*** 3.958*** 3.974*** 3.905***

(0.222) (0.222) (0.222) (0.225) (0.224)

ln(Age) -5.498*** -5.254*** -5.327*** -4.689*** -5.015***

(0.552) (0.553) (0.551) (0.561) (0.567)

ln(Cash pay) -2.199*** -2.169*** -2.245*** -2.257*** -2.279***

(0.147) (0.147) (0.147) (0.147) (0.147)

Constant 48.18*** 47.31*** 48.16*** 44.41*** 46.60***

(2.745) (2.757) (2.744) (2.790) (2.835)

Industry fixed effect Yes Yes Yes Yes Yes

Year fixed effect Yes Yes Yes Yes Yes

Observations 51,452 51,452 51,452 51,452 51,452

Adjusted R2 0.167 0.166 0.168 0.167 0.168

41

Table 7 (Continued)

Panel C: Additional control variables

(1) (2) (3) (4) (5) (6) (7)

ln(Total connections) 0.577*** 0.594*** 0.556*** 0.569*** 0.564*** 0.561*** 0.567***

(0.101) (0.0879) (0.0671) (0.0669) (0.0730) (0.0842) (0.0668)

Entrenchment index -0.330***

(0.0821)

G-index -0.106***

(0.0369)

Board independence 1.083**

(0.485)

Institutional ownership 0.211

(0.228)

Holder67 1.419***

(0.160)

Holder30 -0.900***

(0.310)

Spread -6.973

(13.54)

ln(Sales) 1.602*** 1.874*** 1.983*** 2.003*** 1.984*** 1.943*** 1.999***

(0.115) (0.0948) (0.0712) (0.0713) (0.0766) (0.0872) (0.0718)

Long-term assets 2.970*** 2.144*** 2.362*** 2.450*** 2.173*** 2.153*** 2.353***

(0.682) (0.601) (0.453) (0.455) (0.499) (0.569) (0.453)

R&D 15.55*** 20.09*** 20.06*** 19.99*** 19.33*** 18.16*** 20.01***

(2.897) (2.638) (1.831) (1.832) (2.073) (2.522) (1.838)

Debt ratio -0.599 -1.317** -1.820*** -1.893*** -1.837*** -1.894*** -1.792***

(0.802) (0.625) (0.429) (0.432) (0.505) (0.581) (0.431)

Market-to-book 1.366*** 1.191*** 1.296*** 1.283*** 1.228*** 1.279*** 1.295***

(0.108) (0.0932) (0.0711) (0.0715) (0.0788) (0.0845) (0.0713)

Excess return 10.81*** 22.03*** 20.43*** 20.39*** 20.90*** 18.48*** 20.46***

(2.947) (2.484) (1.604) (1.609) (1.772) (2.017) (1.611)

Volatility 25.61** 11.54 -34.68*** -34.14*** -39.99*** -25.47*** -27.89*

(11.70) (10.08) (6.287) (6.328) (6.864) (8.411) (16.02)

Share turnover 0.170*** 0.121*** 0.0945*** 0.0955*** 0.0836*** 0.0854*** 0.0959***

(0.0194) (0.0160) (0.0105) (0.0107) (0.0117) (0.0139) (0.0105)

CEO 3.384*** 3.662*** 4.011*** 4.030*** 3.826*** 3.921*** 4.017***

(0.327) (0.279) (0.222) (0.223) (0.235) (0.257) (0.222)

ln(Age) -4.853*** -5.548*** -5.153*** -5.137*** -5.124*** -4.359*** -5.147***

(0.826) (0.703) (0.552) (0.553) (0.604) (0.677) (0.552)

ln(Cash pay) -1.579*** -1.872*** -2.212*** -2.229*** -2.186*** -2.060*** -2.222***

(0.215) (0.177) (0.147) (0.147) (0.156) (0.178) (0.147)

Constant 38.30*** 43.72*** 45.79*** 47.34*** 46.20*** 42.32*** 46.58***

(3.881) (3.332) (2.767) (2.691) (3.001) (3.372) (2.751)

Industry fixed effect Yes Yes Yes Yes Yes Yes Yes

Year fixed effect Yes Yes Yes Yes Yes Yes Yes

Observations 19,784 28,403 51,419 50,780 43,883 37,070 51,452

Adjusted R2 0.154 0.149 0.167 0.167 0.170 0.168 0.167

42

Table 8 – Heterogeneity in the Effect of Executive Connections on Incentive Horizons

The table reports the heterogeneity in the effect of executive connections on incentive horizons. The sample

period is from 2000 to 2013. Dependent variables are Incentive duration. Variable definitions are provided

in Table 1. For conciseness, estimates of control variables are suppressed. 2-digit SIC industry and year

fixed effects are included. Robust standard errors are adjusted for clustering by executive and presented in

the parenthesis. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

(1) (2) (3) (4) (5)

ln(Total connections) 0.714*** 0.741*** 0.520*** 0.858*** 0.697***

(0.102) (0.117) (0.117) (0.0882) (0.0857)

ln(Total connections) × Debt ratio -0.625**

(0.305)

ln(Total connections) × Volatility -6.596*

(3.410)

ln(Total connections) × Market-to-book 0.0229

(0.0537)

ln(Total connections) × High inst. own. -0.574***

(0.111)

High inst. own. 3.041***

(0.556)

ln(Total connections) × High spread -0.247***

(0.0909)

High spread 0.726

(0.448)

Controls Yes Yes Yes Yes Yes

Industry fixed effect Yes Yes Yes Yes Yes

Year fixed effect Yes Yes Yes Yes Yes

Observations 51,452 51,452 51,452 50,780 51,452

Adjusted R2 0.167 0.167 0.167 0.168 0.168

43

Table 9 - The Effect of Executive Connections on Earnings Management

The table examines the effect of executive connections on earnings management in the CEO sample. The

sample period is from 2000 to 2013. Dependent variables are Total accruals, Accruals, Positive accruals,

and Negative accruals. Panel A reports the baseline regression results. Panel B uses fitted values from

regressing Incentive duration on ln(Total connections) and control variables from Panel A in fixed effects

OLS regressions to estimate the effect of executive connections on earnings management through incentive

horizons. Panel C reports second-stage results of instrumental variable regressions, where ln(Total

connections) is estimated by ln(Number of prior firms), Graduate degree, and second-stage control

variables. Variable definitions are provided in Table 1. 2-digit SIC industry and year fixed effects are

included. Robust standard errors are adjusted for clustering by executive and presented in the parenthesis.

***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

Panel A: Baseline

Total accruals Accruals Positive accruals Negative accruals

(1) (2) (3) (4)

ln(Total connections) -0.00443*** -0.00383*** -0.00279*** -0.00165*

(0.00102) (0.000921) (0.000689) (0.000934)

ln(Sales) 0.00732*** 0.00658*** 0.00282*** 0.00421***

(0.000951) (0.000837) (0.000512) (0.000955)

Debt ratio -0.0296*** -0.00802 0.000576 -0.00462

(0.00737) (0.00642) (0.00472) (0.00651)

Market-to-book 0.00983*** 0.00682*** 0.00727*** 0.000929

(0.00157) (0.00134) (0.000963) (0.00125)

Sales growth 0.0187*** 0.00970** 0.0176*** -0.0133***

(0.00576) (0.00478) (0.00358) (0.00506)

Cashflows -0.338*** -0.339*** -0.207*** -0.107***

(0.0229) (0.0191) (0.0166) (0.0190)

S.D. (Sales growth) -0.0121** -0.0111*** -0.00583** -0.00333

(0.00513) (0.00429) (0.00250) (0.00420)

S.D. (Cashflows) -0.123*** -0.103*** 0.0544*** -0.152***

(0.0261) (0.0222) (0.0182) (0.0229)

Constant -0.0559*** -0.00482 0.0191*** -0.0348***

(0.0123) (0.0106) (0.00705) (0.0118)

Industry fixed effect Yes Yes Yes Yes

Year fixed effect Yes Yes Yes Yes

Observations 8,688 8,688 4,674 4,014

Adjusted R2 0.288 0.247 0.290 0.207

44

Table 9 (Continued)

Panel B: Executive connections on earnings management through incentive horizons

Total accruals Accruals Positive accruals Negative accruals

(1) (2) (3) (4)

Predicted (Incentive duration) -0.00520*** -0.00450*** -0.00328*** -0.00194*

(0.00120) (0.00108) (0.000809) (0.00110)

ln(Sales) 0.0128*** 0.0113*** 0.00627*** 0.00625***

(0.00187) (0.00168) (0.00115) (0.00176)

Debt ratio -0.0348*** -0.0125* -0.00270 -0.00655

(0.00743) (0.00648) (0.00480) (0.00659)

Market-to-book 0.0164*** 0.0125*** 0.0114*** 0.00338*

(0.00222) (0.00195) (0.00148) (0.00191)

Sales growth 0.0246*** 0.0148*** 0.0213*** -0.0111**

(0.00598) (0.00500) (0.00365) (0.00528)

Cashflows -0.353*** -0.351*** -0.216*** -0.113***

(0.0232) (0.0193) (0.0170) (0.0194)

S.D. (Sales growth) -0.0134*** -0.0122*** -0.00661*** -0.00379

(0.00511) (0.00428) (0.00252) (0.00420)

S.D. (Cashflows) -0.103*** -0.0853*** 0.0674*** -0.144***

(0.0263) (0.0224) (0.0188) (0.0232)

Constant -0.00973 0.0351** 0.0483*** -0.0176

(0.0176) (0.0154) (0.0115) (0.0164)

Industry fixed effect Yes Yes Yes Yes

Year fixed effect Yes Yes Yes Yes

Observations 8,688 8,688 4,674 4,014

Adjusted R2 0.288 0.247 0.290 0.207

45

Table 9 (Continued)

Panel C: Instrumental variable regressions

Total accruals Accruals Positive accruals Negative accruals

(1) (2) (3) (4)

ln(Total connections) -0.00527** -0.00407** -0.00263* -0.00380*

(0.00227) (0.00201) (0.00155) (0.00206)

ln(Sales) 0.00757*** 0.00665*** 0.00277*** 0.00489***

(0.00111) (0.000985) (0.000634) (0.00115)

Debt ratio -0.0296*** -0.00801 0.000539 -0.00480

(0.00735) (0.00640) (0.00468) (0.00645)

Market-to-book 0.00992*** 0.00684*** 0.00725*** 0.00114

(0.00157) (0.00134) (0.000970) (0.00124)

Sales growth 0.0184*** 0.00963** 0.0176*** -0.0137***

(0.00581) (0.00482) (0.00357) (0.00503)

Cashflows -0.339*** -0.339*** -0.207*** -0.110***

(0.0228) (0.0190) (0.0165) (0.0191)

S.D. (Sales growth) -0.0120** -0.0111*** -0.00586** -0.00313

(0.00509) (0.00426) (0.00248) (0.00414)

S.D. (Cashflows) -0.123*** -0.103*** 0.0543*** -0.152***

(0.0261) (0.0221) (0.0181) (0.0226)

Constant -0.0475*** 0.0162 0.0476*** -0.0272*

(0.0163) (0.0140) (0.0100) (0.0154)

First stage F statistic 160.43*** 160.43*** 118.77*** 127.62***

Hansen J statistic 1.93 0.71 0.00 0.03

Industry fixed effect Yes Yes Yes Yes

Year fixed effect Yes Yes Yes Yes

Observations 8,688 8,688 4,674 4,014

Adjusted R2 0.288 0.247 0.290 0.206