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On the effect of CEOs’ personal characteristics in transport firm value? A stochastic frontier model
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Case Studies on Transport Policy journal homepage: www.elsevier.com/locate/cstp
On the effect of CEOs’ personal characteristics in transport firm value? A stochastic frontier model Ezzeddine Ben Mohamed a,*, Sami Jarboui a, Amel Baccar b, Abdelfettah Bouri c a
Faculty of Economics and Management at Sfax Tunisia, FSEG Sfax, Tunisia Faculty of Economics and Management at Mahdia Tunisia, Tunisia c Najran University, Saudi Arabia b
A R T I C L E I N F O
Article history: Available online xxx Keywords: CEOs’ personal characteristics Firm value Stochastic frontier approach Transport firms Public transport
A B S T R A C T
The aim of this paper is to investigate the effect of managerial personal characteristics on his/her firm value. Especially, we seek to determine the impact of CEOs’ age, education nature and tenure on their firm value. In fact, we try to explain why transport firms do not arrive to trade in their optimal value summarized by the Tobin-Q. Using a stochastic analysis frontier model, we generate the optimal firm value that a transport firm can realize if their CEOs act in a full rational way and use their production factors in optimal manner (Q*). We try then to explain the shortfall on firm value that represents the difference between the optimal firm value (Q*) and its observed value (Q). The shortfall (Q* Q) represents the inefficiency term on transport firm value and it is explained by CEOs’ characteristics. Departing from a sample of 53 transport firms from a multinational context publicly traded from 2000 to 2011, our results show that CEOs’ age and technical education can reduce the shortfall and so increase firm’s value while a long tenure of CEOs can reduce the firm value. Transport firms should be aware that CEOs’ personal characteristics can largely affect their value and policy makers are invited to provide the optimal CEOs’ characteristics that can avoid such distortions on firm value. ß 2015 World Conference on Transport Research Society. Published by Elsevier Ltd. All rights reserved.
1. Introduction How a top executive manager in transport firms affects corporate value? The transport literature largely ignored this question while there are some answers from the financial literature since it has been assumed that economic agents and CEOs are fully rational and so manager personal traits cannot affect firm value. The financial literature mainly refers to four theories in order to answer such interrogation: the agency theory (Jensen and Meckling, 1976; Jensen, 1986), the asymmetric information theory (Myers and Majluf, 1984), the corporate governance theory and the behavioral finance which document that some psychological, emotional and cognitive factors can be used to explain distortions on corporate policy and value (Heaton, 2002; Malmendier and Tate, 2005a, 2005b, 2008; Lin et al., 2005; Huang et al., 2011; Campbell et al., 2011). The determinants of firm’s value are dominated by factors that refer to the firm level or the market level but the existing literature
* Corresponding author. Tel.: +216 50 850 064. E-mail address: [email protected] (E. Ben Mohamed).
largely ignore the possible role that individual managers may play (Bertrnard and Schoar, 2003). Recently, a wave of research papers focus on the effect of CEOs’ personality traits on their decision making (Bertrnard and Schoar, 2003; Hackbarth, 2008; Heaton, 2002; Malmendier and Tate, 2005a,b, 2008; Lin et al., 2005; Huang et al., 2011; Campbell et al., 2011; Ben Fatma et al., 2013; Baccar et al., 2013; Ben Mohamed et al., 2012, 2014a). Their results document that managerial trait of personality can largely affect their management style (Bertrnard and Schoar, 2003), corporate investment decision (Heaton, 2002; Malmendier and Tate, 2005a, 2005b; Lin et al., 2005; Huang et al., 2011), firm’s capital structure and financing decision (Hackbarth, 2008) and firm’s acquisition strategy acquisition (Malmendier and Tate, 2008). A key feature in the existing literature is the absence of studies regarding the effect of such personality traits on firm value. Hence, this paper contributes to the transport literature by investigating the effect of some CEOs’ personal characteristics on firm value. In this paper, we explore the effect of CEOs’ personal characteristics, other than behavioral factors, on firm value. The contribution of this paper on the transport literature is that this is the first paper that seeks to explain why the firms, that operate in the transport sectors, do not arrive to trade in their
http://dx.doi.org/10.1016/j.cstp.2015.01.001 2213-624X/ß 2015 World Conference on Transport Research Society. Published by Elsevier Ltd. All rights reserved.
Please cite this article in press as: Ben Mohamed, E., et al., On the effect of CEOs’ personal characteristics in transport firm value? A stochastic frontier model. Case Stud. Transp. Policy (2015), http://dx.doi.org/10.1016/j.cstp.2015.01.001
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optimal value summarized by the Tobin-Q. In fact, we use a stochastic frontier model in order to generate the optimal value that a transport firm can realize if their CEOs act in a rational manner and use the production factors in an optimal way (Q*). We then try to explain the shortfall (Q* Q) that represents the distance between the current transport firm value (Q) and the optimal firm value (Q*). More specifically, we tend to demonstrate the effect of managerial characteristics such as his/her age, education nature and tenure on transport firm value. The remainder of this paper proceeds as follows. In Section 1 we present review around the potential effect of CEOs’ personal characteristics on firm value and we formulate our hypothesis. In Section 2 we introduce our approach, model and variables. In Section 3 we describe our database and it sources. Section 4 presents and discusses our results. Finally, Section 5 concludes.
2. Literature review and hypothesis development The relationship between CEOs’ personal characteristics and transport firm value was largely ignored in the financial and economic literature. The existing literature only gives insights on the effect of managerial trait of personality on his/her corporate decision. The originality of this paper is guaranteed since the existing literature on public transport and on transportation sciences are still discussing the efficiency of transport firms and explaining the sources of inefficiency. The literature on assessing efficiency or, more generally, public transport efficiency is extensive. Analyses have focused both on developing methods for assessing public transport efficiency and on using efficiency findings to make different policy recommendations (Karlaftis and Tsamboulas, 2012). Such efficiency assessment studies have been very popular in public transport literature in a large part because of the interest in reforming public transport operations and assessing the effects of these changes on efficiency. On this subject, Jarboui et al. (2012) offer an interesting overview. Our research aims to investigate if these traits can affect firm’s value. We focus mainly on the CEO’s age, CEO’s financial education, CEO’s technical education, and his/her tenure, traits that are still unexplored in the transport literature. This literature mainly focuses on how technology and financial variables can affect transport firms’ efficiency. 2.1. CEO’s age and transport firm value Taylor (1975) argues that older decision makers tend to take longer time to reach decisions. He finds that older decision makers were able to diagnose the value of information more accurately than younger decision makers. A key feature in their empirical conclusion is that older decision makers were shown to be less affected to their overconfidence bias, a trait that positively influences their decision making quality. Ben Mohamed et al. (2012) show that CEO’s optimism decreases with manager’s age. They argue that older CEOs are supposed to be more rational since they have a long experience. The same idea was adopted by Taylor (1975) who finds that decision makers’ age increases the performance of their decision making. Ben Mohamed et al. (2012) demonstrated that CEO’s age can reduce the probability and according to it the manager will be frapped by optimism bias. Shefrin (2001) affirms that personal risk aversion appears to increase with age and it may decline after the age of 70. Many studies suggest that the age of the CEO can have an important effect on corporate financial policy choices, firm performance, and the existence of agency costs within a firm. Davidson et al. (2006) as well as Wiersema and Bantel (1992) argued that age influences duality. Age can, expectedly, positively relate to duality as human capital accumulates through years of experience.
Holmstrom (1999) and Scharfstein and Stein (1990) developed market learning models, which lead to the prediction that younger CEOs are more risk averse and, therefore, invest less aggressively than older CEOs. In sum, we can predict that older CEOs will act more rationally and so they can contribute to an increase in transport firm value. Hence, we can hypothesis that: H1. Transport firm value increases with CEOs’ age. 2.2. CEO’s background and Transport firm value Academics show some interest on how CEO’s education influences their firms (Gottesman and Morey, 2010). A key feature in this literature is that they are divided into studies which examine the type of education and the firm’s behavior (Tyler and Steensma, 1998; Finkelstein and Hambrick, 1996; and Barker and Mueller, 2002; Graham and Harvey, 2001, 2002; Graham et al., 2005) while the second branch essays to investigate the effect of CEO’s education on firm performance (Deary, 2004; Frey and Detterman, 2004; Ben Fatma et al., 2015; Ben Mohamed, 2014b). The relationship between this variable and firm value is still unexplored. In order to predict the influence of this variable on firm value we can refer to its effects on managerial decisions. Baker and Muller (2002) affirm that CEO’s background is important in her receptivity to innovative ideas and activities. Chen et al. (2011) argue that manager’s education level and his professional background are positively associated also with the firm’s innovation effort. Deary (2004) and Frey and Detterman (2004) report strong evidence on the positive impact of CEO’s education on firm’s performance while Tonello and Torok (2011) finds no strong evidence of a relationship between CEO’s education and firm performance. In his study, he found that only the CEOs having a MBA degree from a Top 20 business school enabled better operating performance and Tobin’s Q. Malmendier and Tate (2005a) argue that CEO’s financial and technical education can affect investment cash flow sensitivity. Ben Mohamed et al. (2012) demonstrated that especially CEO’s technical education can succeed to reduce managerial optimism and its effects on corporate distortions among NYSE manufacturer firms during 1999–2010. More recently, a study by Ben Mohamed et al. (2012) shows that CEOs’ education can positively affect the quality of corporate decisions, and it can reduce the bad effects of managerial optimism. In fact, they demonstrate that CEOs’ education can reduce irrationalities in corporate decision making. Financial education represents a dummy variable that takes one if CEO has undergraduate and graduate degrees in accounting, finance, business and economics. Technical education is a dummy variable and it takes one if CEO has a graduate or undergraduate degree in engineering, physics, operations research, chemistry, mathematics, biology, pharmacy and other applied sciences. We can so hypothesize that: H2a. CEO’s financial education increases transport firm value. H2b. CEO’s technical education increases transport firm value. 2.3. CEO’s tenure and transport firm value Firm value plays a crucial role in CEO turnover research: on one hand, performance is viewed as a proxy for CEO effort and, therefore, the likelihood of CEO turnover is expected to increase following financial distress and performance decline. On the other hand, the relationship between CEO turnover and company performance is viewed to mirror the efficiency of the firm’s governance mechanisms as the turnover performance sensitivity is
Please cite this article in press as: Ben Mohamed, E., et al., On the effect of CEOs’ personal characteristics in transport firm value? A stochastic frontier model. Case Stud. Transp. Policy (2015), http://dx.doi.org/10.1016/j.cstp.2015.01.001
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hypothesized to be stronger when owners and their representatives on corporate boards assume their monitoring role vigilantly. The effect of efficiency is well documented in the literature and across international contexts (Renneboog, 2000). There are many studies that have been able to corroborate the negative relationship between turnover and firm value (Jarboui et al., 2014). Moreover, not all studies find a significantly negative relationship between turnover and firm value. For example, Dalton and Kesner (1985) failed to document any statistically significant relationship between the two variables. Morck et al. (1988) reported a large and positive relationship between the two variables indicating that CEOs might actually be hindered from leaving the company when business goes downhill. Certain scholars claim that firm value explains only very little of the variation in CEO turnover (Brickley, 2003) and that considerable performance declines are necessary before CEOs get fired (Schwartz and Menon, 1985). It follows from this that factors other than performance such as the structure of corporate ownership, the characteristics of the top executives and possibly also the independence of corporate boards are likely to influence CEO turnover decisions. Existing literature have largely focused on directly observable characteristics such as CEO’s tenure (Kaplan and Mauldin, 2008). This literature mainly studies the effect of CEO’s tenure on firm performance and on the quality of managerial decision making. Hermalin and Weisbach (1998) link between CEO’s tenure and his/ her negotiation power. They conclude that CEO’s negotiating power increases with CEO’s tenure. Firm value may increase with tenure on referring to the CEO power hypothesis. According to this hypothesis, manager will gain power over time. Dikolli et al. (2014) departing from American firms find that the negative relationship between CEO turnover and firm performance monotonically declines in CEO tenure showing the benefits of long CEO tenure, however they also find that tenure contributes to decrease the positive effect of CEO ownership change on Tobin’s Q. Other research papers document an inverse influence of CEO tenure at the firm level. Ben Mohamed et al. (2012) found that CEO tenure can contribute to the emergence of CEO’ s optimism bias which causes some well documented problems such as investment cash flow sensitivity (Heaton, 2002) which causes under or overinvestment problems. This can reduce firm value. H3. Transport firm value decreases with CEO’s tenure. 3. Methodology Referring to Habib and Ljungqvist (2005), a firm’s value is the present value of the cash flows generated by the firm’s assets, which consist of assets in place and growth opportunities. An estimate of the firm’s value is provided by the market capitalization of its debt and equity. Tobin’s Q is the ratio of the market value of debt and equity and the replacement cost of the firm’s assets in place. If a firm operates and invests in assets that are expected to create value, then its Q will be greater than 1. The more value created, the higher is the Q. To estimate the model, we need to take a stand on what we consider to be an X variable that determines the location of the frontier and what we consider to be a Z variable that explains shortfalls from the frontier. In principle, there are two ways to partition the variable set: on the basis of an econometric criterion, such as maximizing the log likelihood, or on the basis of economic theory (Habib and Ljungqvist, 2005). We employ the parametric approach using the stochastic frontier production function for panel data proposed by Battese and Coelli (1995). The starting point of this parametric approach is to estimate a stochastic optimal value frontier. This frontier can be
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written as follows: Y it ¼ exp ðxit b þ V it U it Þ
(1)
where Yit is the output of the i-th transport operator (i = 1, 2, . . ., N) in the t-th period (t = 1, 2,. . ., T); Xit is a (1 * k) vector of input quantities of the i-th transport operator in t-th period; b is a (k * 1) vector of unknown parameters to be estimated; Vit is a random variable which is assumed to be iid N ð0; s 2v Þ and independent of Uit; the Uit is a non-negative random variable, associated with technical inefficiency of production, which is assumed to be independently distributed as truncations at zero of the N (m; s 2U ) distribution; where m ¼ zit d and variance s 2U ; and zit s 2U is a (1 * p) vector of explanatory variables associated with technical inefficiency of public transport production industry over time; where d is a (p * 1) vector of unknown parameters. Eq. (1) specifies the stochastic frontier production function in terms of the original production values. However, the technical inefficiency effects, Uit, are assumed to be a function of a set of explanatory variables, zit, and an unknown vector of coefficients, d. The technical inefficiency effect, Uit, in the stochastic frontier model (1) is specified by Eq. (2), U it ¼ zit d þ W it
(2)
where the random variable Wit follows truncated normal distribution with mean zero and variance s2, such that the point of truncation is zitd that is, Wit > zitd. These assumptions are consistent with Uit being a non-negative truncation of the N (zitd, s2U) distribution (Battese and Coelli, 1995). The mean zitd of the normal distribution, which is truncated at zero to obtain the distribution of Uit, is not required to be positive for each observation (Jarboui et al., 2013a,b). In this paper we adopt a stochastic frontier approach. Similar to Habib and Ljungqvist (2005) and Pawlina and Renneboog (2005) we use a stochastic frontier model in order to generate the optimal firm value summarized by Tobin’s Q*, we assume that firm utilizes the optimal combination of its inputs. We use a package FRONTIER 4.1 as developed by Coelli (1996) to run our empirical model. We aim to compute the difference between optimal firm value Q* and its actual Q. For this reason, we use the next empirical model: MV b It b K t1 2 ¼ b0 þ b1 ln sales þ b2 ðln salest Þ þ 3 þ 4 BV K t1 salest þ b5 O perMarg t þ b6 LEV t V t þ et
(3)
in this formulation, we denote by (MV/BV) which is defined as the market to book ratio and it represents the actual observed Q.1 ln salest represents the natural logarithm of revenues. The variable (It/Kt1) is the investment of capital ratio (Kt1/salest) which represents the capital intensity. The OperMarg and LEV represent respectively the operating margin and the market leverage. K here denotes the value of tangible long-term assets (property, plant, and equipment). We follow Habib and Ljungqvist (2005) to compute the operating margin and leverage. The operating margin is defined as the ratio of operating income before depreciation to sales. Leverage is defined as the book value of long-term debt divided by market value of equity plus book value of long-term debt. vitis a random component distributed according to NðZ t m; s 2v Þ where Zt denotes the vector of variables affecting the inefficiency level of a given firm and m is a vector of unknown parameters. Following Habib and Ljungqvist (2005) and Pawlina and Renneboog (2005), the potential determinants of the inefficiency 1 In this level we use also another measure of firm value as described by Habib and Ljungqvist, 2005. The measurement of firm value as described by these authors generates the same results.
Please cite this article in press as: Ben Mohamed, E., et al., On the effect of CEOs’ personal characteristics in transport firm value? A stochastic frontier model. Case Stud. Transp. Policy (2015), http://dx.doi.org/10.1016/j.cstp.2015.01.001
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Table 2 Summary of descriptive statistics.
term are described by the next model:
vit ¼ d0 þ d1 Age þ d2 Age2it þ d3 FEit þ d4 FE2it þ d5 TEit þ d6 TE2it þ d7 Tenure þ d
2 8 Tenureit
þ wit
(4)
in this formulation, we denote by (Age) the CEO’s age; (FE) is a dummy variable that indicates if a CEO has undergraduate and graduate degrees in accounting, finance, business and economics; (TE) is also a dummy variable that indicates if CEO has a graduate or undergraduate degree in engineering, physics, operations research, chemistry, mathematics, biology, pharmacy and other applied sciences and (Tenure) represents CEO’s tenure. It is measured by the number of years that he/she spent as CEO. Finally, (Wit) is the error term. 4. Data description We use the Thomson financial database in order to extract information concerning the CEO’s characteristics. This database offers a CEO biography that presents a practical tool to construct our variables around CEO characteristics. We use the World scope database for other financial variables. We use all transport firms that are included in this database. Finally, we only retain firms with all information available in our database. Our data set consists of 53 transport firms from 17 countries publically traded between 2000 and 2011. The 17 countries are namely: Brazil, Canada, China, Germany, Hong Kong, Indonesia, Japan, Jordan, Korea (south), Malaysia, Norway, Poland, Saudi Arabia, Singapore, United Kingdom, United States and Vietnam. The number of firms by country is summarized in Tables 1 and 2. We obtain thus a panel of 636 annual observations.
BV/MVt ln salest (ln sales)2 It/Kt1 Kt1/salest opermargt Levt Age CEO Financial education Technical education Tenure
Mean
Standard deviation
Minimum
Maximum
1.931 18.724 354.262 0.263 1.070 0.029 0.249 50.695 0.842 0.512 2.708
19.034 1.916 70.698 0.459 1.204 0.500 0.262 9.349 0.365 0.500 3.200
386.347 13.632 185.837 0.000 0.024 8.703 0.000 26.000 0.000 0.000 0.000
98.179 23.056 531.577 7.095 14.650 0.525 0.848 75.000 1.000 1.000 12.000
firms with larger growth opportunities, suggesting an underinvestment problem. Our hypotheses concerning the CEO’s education are partially validated. We find that only CEO’s technical background can arrive to reduce distortions in firm value (Q* Q) however the financial education seems having no impact on firm value. Our finding can be due to the nature of our firms that are all in the transport domain which needs technical skills rather than a financial one. This result shows that CEO’s education is not significantly related to firm value, that education is a poor proxy for CEO ability. Nevertheless education does play an important role in CEO hiring Table 3 Estimation results. Variables
Parameters
Estimated MLE coefficients Model
Constant
b0
ln salest
b1
40.169 (33.398)*** 5.135 (24.228)*** 0.141 (14.880)*** 0.857 (1.073) 0.254 (1.134) 0.047 (0.101) 3.106 (3.661)*** 0.601 (0.596) 7.075 (16.966)*** 0.057 (9.043)*** 0.071 (0.067) 0.072 (0.067) 1.895 (1.918)** 1.895 (1.918)** 3.038 (2.890)*** 0.242 (2.287)***
5. Results and discussion Table 1 reports results of estimation of our model. Our results are consistent with those of Habib and Ljungqvist (2005) and Pawlina and Renneboog (2005) concerning the effect of variables in the frontier function. CEO’s age has a positive effect on transport firm value. This hypothesis is validated at the one percent level of significance (see Table 3). The estimated coefficient of the CEO’s age is negative, which indicates that CEO’s age affects negatively the inefficiency term. This result shows that CEO’s characteristics have an impact on firm efficiency: CEO’s age has a positive and significant impact on transport firm value, revealed by the fact that older CEOs invest less than younger CEOs, and that this finding is concentrated in
2
(ln sales)
b2
It/Kt1
b3
Kt1/salest
b4
Opermargt
b5
LEVt
b6
Constant
d0 d1
Age CEO (Age CEO)
Table 1 Distribution of public transport firms by countries. Countries
Number of firms
Brazil Canada China Germany Hong Kong Indonesia Japan Jordan Korea (south) Malaysia Norway Poland Saudi Arabia Singapore United kingdom United states Vietnam
1 1 8 4 3 4 8 5 2 2 1 1 2 2 4 1 4
2
d2
Financial education
d3
(Financial education)2
d4
Technical education
d5
(Technical education)2
d6
Tenure
d7
(Tenure)
2
d8
Sigma-squared
s 2 ¼ s 2V þ s 2U
Gamma
g ¼ s2 þUs2
s2
V
Log likelihood function *
U
740.469 (706.004)*** 0.986 (793.692)*** 1534.403
denotes significance at the 10% level. denotes significance at the 5% level. denotes significance at the 1% level.
**
***
Please cite this article in press as: Ben Mohamed, E., et al., On the effect of CEOs’ personal characteristics in transport firm value? A stochastic frontier model. Case Stud. Transp. Policy (2015), http://dx.doi.org/10.1016/j.cstp.2015.01.001
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decisions; boards still use educational qualifications as criteria in evaluating potential CEOs. Finally, CEO’s tenure positively affects inefficiency term and so we can say that CEO with long tenure can negatively influence firm value. This hypothesis is validated at the one percent level of significance (see Table 3). The estimated coefficient of the CEO tenure is positive, which indicates that CEO tenure affects positively the inefficiency term. This result shows that CEO tenure has a negative and significant impact on transport firm value, which we clarify by stating that transport firm value is viewed as a proxy for CEO effort and CEO turnover is expected to increase following financial distress and efficiency declines. Also, we can say that the relationship between CEO turnover and company performance is viewed as mirroring the transport firm’s value, since the turnover-performance sensitivity is hypothesized to be stronger when owners and their representatives on corporate boards assume their monitoring role vigilantly. Also, a negative relation between firm value and the probability of forced chief executive officer (CEO) turnover was well documented by Parrino (1997) and Warner et al. (1988). This can be explained by the fact that a large tenure can create agency problems and other entrenchment problems. A CEO with long tenure is entrenched and so they can take sub-optimal decisions that aim to increase firm size but not firm value. They search to maximize firm size in order to escape from shareholder’s control.
6. Conclusion The aim of our study is to determine the incidence of CEOs’ personal characteristics on transport firm value. We propose an estimate of the distance to the optimal frontier of firm value of a sample of 53 firms in public road transport, during the period 2000–2011. This estimation is based on a stochastic frontier model. We also relate these optimal scores of transport firm value and the CEOs’ personal characteristics. This paper is an original essay that investigates the effect of CEOs personal characteristics’ influences on their firms’ value, a subject that is largely ignored by the transport literature. Using an SFA approach and a package Frontier 4.1, we empirically demonstrate that CEOs’ age, technical education and their tenures can largely explain the shortfall on firm value (Q* Q). The CEO’s age and their technical background can succeed to increase firm value while a long tenure increases inefficiency term and reduces firm value. Transport firms should take care about their CEOs characteristics; especially they should avoid a CEO with long tenure and select those with a technical education and relatively older in order to reduce inefficiency and increase their firm value. This study has some limitations that should be noted. First, the sample used is of relatively small size. Second is the number of factors that we use in order to explain the level of transport firm value. Nevertheless, this work can be considered a starting point for further research. More specifically, two main research perspectives can be outlined. The first is to add other variables, such as macroeconomic variables and structural variables in the inefficiency of firm value term, given the importance of these variables in explaining the level of firm value. A comparison could also be conducted with what is practiced in other countries, particularly emerging countries. Regarding the speed development of the behavioral economics and finance literature which demonstrate that managerial psychology is for interest, we can investigate the effect of behavioral factors such as managerial optimism and overconfidence on his/her firm value. In fact, these variables have a great impact on corporate decisions and so they can affect firm value.
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References Baccar, A., Ben Mohamed, E., Bouri, A., 2013. Managerial overconfidence and board characteristics: toward a new role of corporate governance. Aust. Basic Appl. Sci. 7 (7), 287–301. Baker, V.L., Muller, G.C., 2002. CEO characteristics and firm R&D spending. Manage. Sci. 48 (6), 782–801. Barker, V.L., Mueller, G.C., 2002. CEO characteristic and firm R&D spending. Manage. Sci. 48 (1), 782–801. Battese, G.E., Coelli, T., 1995. A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empir. Econ. 20, 325–332. Ben Fatma, E., Ben Mohamed, E., Boudabbous, S., 2015. Does entrepreneur dispositional optimism bias affect small firms’ technical efficiency. Int. J. Enterpren. Small Bus. (in press). Ben Fatma, E., Ben Mohamed, E., Boudabouss, S., 2013. Entrepreneurship theory: from rational to behavioral theory. Int. J. Contemp. Bus. Stud. 4 (5), 72–89. Ben Mohamed, E., Fairchild, R., Bouri, A., 2014a. Investment cash flow sensitivity under managerial optimism: new evidence from NYSE panel data firms. J. Econ. Financ. Adm. Sci. 19 (36), 11–18. Ben Mohamed, E., Mohamed Naceur, R., Baccar, A., Bouri, A., 2014b. CEO’s personal characteristics, ownership and investment cash flow sensitivity: evidence from NYSE panel data firms. J. Econ. Financ. Adm. Sci. 19 (37), 98–103. Ben Mohamed, E., Baccar, A., Fairchild, R., Bouri, A., 2012. Does corporate governance affect managerial optimism: evidence from NYSE panel data firms. Int. J. Euro-Mediterr. Stud. 5 (1), 41–56. Bertrnard, M., Schoar, A., 2003. Managing with style: the effect of managers on firm policies. Q. J. Econ. 118 (4), 1169–1208. Brickley, J.A., 2003. Empirical research on CEO turnover and firm-performance: a discussion. J. Acc. Econ. 36, 227–233. Campbell, C., Jhonson, S., Rutherford, J., Stanley, B., 2011. CEO confidence and forced turnover. J. Financ. Econ. 101 (3), 695–712. Chen, J., Ezzamel, M., Cai, Z., 2011. Managerial power theory, tournament theory, and executive pay in China. J. Corp. Finance 17, 1176–1199. Coelli, T.J., 1996. A Guide to Frontier Version 4. 1: A Computer Program. University of New England, Armidale, Australia. Dalton, D.R., Kesner, I.F., 1985. Organizational performance as an antecedent of inside/outside chief executive succession: an empirical assessment. Acad. Manage. J. 28 (4), 749–762. Davidson, W.N., Nemec, C., Worrell, D.L., 2006. Determinants of CEO age at succession. J. Manage. Gov. 10, 35–57. Deary, I.J., 2004. Intelligence: A Very Short Introduction. Oxford University Press. http://ukcatalogue.oup.com/product/9780192893215.do. Dikolli, S.S., Maye, W.J., Nanda, D., 2014. CEO tenure and the performanceturnover relation. Rev. Account. Stud. 19 (1), 281–327, http://dx.doi.org/ 10.1007/s11142-013-9247-6. http://link.springer.com/journal/11142/19/1/ page/1. Finkelstein, S., Hambrick, D.C., 1996. Strategic Leadership: Top Executives and their Effects on Organizations. West Publishing Company, St. Paul, MN. Frey, M.C., Detterman, D.K., 2004. Scholastic assessment or g? The relationship between the scholastic assessment test and general cognitive ability. Psychol. Sci. 15 (6), 373–378. Gottesman, A.A., Morey, M.R., 2010. CEO educational background and firm financial performance. J. Appl. Finance 20 (2), 70–82. Graham, J., Harvey, C., Rajgopal, S., 2005. The Economic Implications of Corporate Financial Reporting, Working Paper. Duke University. Graham, J., Harvey, C., 2001. The theory and practice of corporate finance: evidence from the field. J. Financ. Econ. LX, 187–243. Graham, J., Harvey, C., 2002. How do CFOs make capital budgeting and capital structure decisions? J. Appl. Corp. Finance 15, 8–23, http://dx.doi.org/10.1111/ j.1745-6622.2002.tb00337.x. Habib, M.A., Ljungqvist, A.P., 2005. Firm value and managerial incentives. J. Bus. 78 (6), 2053–2094. Hackbarth, D., 2008. Managerial traits and capital structure decisions. J. Financ. Quant. Anal. 43 (4), 843–882. Heaton, J.B., 2002. Managerial optimism and corporate finance. Financ. Manage. 31 (Summer (2)), 33–45. Hermalin, B.E., Weisbach, S.M., 1998. Endogenously chosen boards of directors and their monitoring of the CEO. Am. Econ. Rev. LXXXVIII, 96–118. Holmstrom, B., 1999. Managerial Incentive Problems: a Dynamic Perspective. Rev. Econ. Stud. 66, 169–182. Huang, W., et al., 2011. Agency cost, top executives’ overconfidence, and investment-cash flow sensitivity: evidence from listed companies in China. PacificBasin Financ. J. 19, 261–277. Jarboui, S., Forget, P., Boujelbene, Y., 2013b. Public road transport efficiency: a stochastic frontier analysis. J. Transp. Syst. Eng. Inf. Technol. 13 (5), 64–71. Jarboui, S., Forget, P., Boujelbene, Y., 2012. Public road transport efficiency: a literature review via the classification scheme. Public Transport 4 (2), 101–128. Jarboui, S., Forget, P., Boujelbene, Y., 2013a. Efficiency evaluation in public road transport: a stochastic frontier analysis. Transport, http://dx.doi.org/10.3846/ 16484142.2013.785019 (in press). Jarboui, S., Forget, P., Boujelbene, Y., 2014. Inefficiency of public road transport and internal corporate governance mechanisms. Case Stud. Transp. Policy, http://dx.doi.org/10.1016/j.cstp.2014.05.004. Jensen, M., 1986. Agency costs of free cash flow, corporate finance and takeovers. Am. Econ. Rev. 76, 323–329.
Please cite this article in press as: Ben Mohamed, E., et al., On the effect of CEOs’ personal characteristics in transport firm value? A stochastic frontier model. Case Stud. Transp. Policy (2015), http://dx.doi.org/10.1016/j.cstp.2015.01.001
G Model
CSTP-53; No. of Pages 6 6
E. Ben Mohamed et al. / Case Studies on Transport Policy xxx (2015) xxx–xxx
Jensen, M., Meckling, W., 1976. Theory of the firm: managerial behavior, agency costs, and ownership structure. J. Financ. Econom. 3, 305–360. Kaplan, S.E., Mauldin, E.G., 2008. Auditor rotation and the appearance of independence: evidence from non-professional investors. J. Account. Public Policy 27, 177–192. Karlaftis, M.G., Tsamboulas, D., 2012. Efficiency measurement in public transport: are findings specification sensitive? Transp. Res. A 46, 392–402. Lin, Y., Hu, S., Chen, M., 2005. Managerial optimism and corporate investment: some empirical evidence from Taiwan. Pacific-Basin Financ. J. 13, 523–546. Malmendier, U., Tate, G., 2005a. CEO overconfidence and corporate investment. J. Financ. 60 (6), 2661–2700. Malmendier, U., Tate, G., 2005b. Does overconfidence affect corporate investment? CEO overconfidence measures revisited. Eur. Financ. Manage. 11 (5), 649–659. Malmendier, U., Tate, G., 2008. Who makes acquisitions? CEO overconfidence and the market’s reaction. J. Financ. Econ. 89, 20–43. Morck, R., Shleifer, A., Vishny, R., 1988. Management ownership and market valuation: an empirical analysis. J. Financ. Econ. 20, 293–315. Myers, S.C., Majluf, N.S., 1984. Corporate financing and investment decisions when firms have information those investors do not have. J. Financ. Econ. 3, 2. Parrino, R., 1997. CEO turnover and outside succession: a cross-sectional analysis. J. Financ. Econ. 46, 165–197.
Pawlina, G., Renneboog Luc, D.R., 2005. Is investment-cash flow sensitivity caused by agency costs or asymmetric information? Evidence from the UK. Eur. Financ. Manage. 11 (4), 483–513. Renneboog, L., 2000. Ownership, managerial control and the governance listed on the Brussels stock exchange. J. Bank. Financ. 24 (12), 1959–1995. Scharfstein, D.S., Stein, J.C., 1990. Herd behavior and investment. Am. Econ. Rev. 80 (3), 465–479. Schwartz, K., Menon, K., 1985. Auditor switches by failing firms. Account. Rev. 14 (2), 248–260. Shefrin, H., 2001. Behavioral corporate finance. J. Appl. Corp. Financ. 14, 3. Taylor, R., 1975. Age and experience as determinants of managerial information processing and decision making performance. Acad. Manage. J. 18 (1), 74–81. Tonello, M., Torok, J., 2011. The 2011 U.S. Director Compensation and Board Practices Report (The Conference Board, New York). Tyler, B.B., Steensma, H.K., 1998. The effects of executives’ experiences and perceptions on their assessment of potential technical alliances. Strateg. Manage. J. 19 (10), 939–965. Warner, J.B., Watts, R.L., Wruck, K.H., 1988. Stock Prices and Top Management Changes. J. Financ. Econ. 20 (1), 461–692. Wiersema, M.F., Bantel, K.A., 1992. Top Management Team Demography and Corporate Strategic Change. Acad. Manage. J. 35, 91–121.
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Contents lists available at ScienceDirect
Case Studies on Transport Policy journal homepage: www.elsevier.com/locate/cstp
On the effect of CEOs’ personal characteristics in transport firm value? A stochastic frontier model Ezzeddine Ben Mohamed a,*, Sami Jarboui a, Amel Baccar b, Abdelfettah Bouri c a
Faculty of Economics and Management at Sfax Tunisia, FSEG Sfax, Tunisia Faculty of Economics and Management at Mahdia Tunisia, Tunisia c Najran University, Saudi Arabia b
A R T I C L E I N F O
Article history: Available online xxx Keywords: CEOs’ personal characteristics Firm value Stochastic frontier approach Transport firms Public transport
A B S T R A C T
The aim of this paper is to investigate the effect of managerial personal characteristics on his/her firm value. Especially, we seek to determine the impact of CEOs’ age, education nature and tenure on their firm value. In fact, we try to explain why transport firms do not arrive to trade in their optimal value summarized by the Tobin-Q. Using a stochastic analysis frontier model, we generate the optimal firm value that a transport firm can realize if their CEOs act in a full rational way and use their production factors in optimal manner (Q*). We try then to explain the shortfall on firm value that represents the difference between the optimal firm value (Q*) and its observed value (Q). The shortfall (Q* Q) represents the inefficiency term on transport firm value and it is explained by CEOs’ characteristics. Departing from a sample of 53 transport firms from a multinational context publicly traded from 2000 to 2011, our results show that CEOs’ age and technical education can reduce the shortfall and so increase firm’s value while a long tenure of CEOs can reduce the firm value. Transport firms should be aware that CEOs’ personal characteristics can largely affect their value and policy makers are invited to provide the optimal CEOs’ characteristics that can avoid such distortions on firm value. ß 2015 World Conference on Transport Research Society. Published by Elsevier Ltd. All rights reserved.
1. Introduction How a top executive manager in transport firms affects corporate value? The transport literature largely ignored this question while there are some answers from the financial literature since it has been assumed that economic agents and CEOs are fully rational and so manager personal traits cannot affect firm value. The financial literature mainly refers to four theories in order to answer such interrogation: the agency theory (Jensen and Meckling, 1976; Jensen, 1986), the asymmetric information theory (Myers and Majluf, 1984), the corporate governance theory and the behavioral finance which document that some psychological, emotional and cognitive factors can be used to explain distortions on corporate policy and value (Heaton, 2002; Malmendier and Tate, 2005a, 2005b, 2008; Lin et al., 2005; Huang et al., 2011; Campbell et al., 2011). The determinants of firm’s value are dominated by factors that refer to the firm level or the market level but the existing literature
* Corresponding author. Tel.: +216 50 850 064. E-mail address: [email protected] (E. Ben Mohamed).
largely ignore the possible role that individual managers may play (Bertrnard and Schoar, 2003). Recently, a wave of research papers focus on the effect of CEOs’ personality traits on their decision making (Bertrnard and Schoar, 2003; Hackbarth, 2008; Heaton, 2002; Malmendier and Tate, 2005a,b, 2008; Lin et al., 2005; Huang et al., 2011; Campbell et al., 2011; Ben Fatma et al., 2013; Baccar et al., 2013; Ben Mohamed et al., 2012, 2014a). Their results document that managerial trait of personality can largely affect their management style (Bertrnard and Schoar, 2003), corporate investment decision (Heaton, 2002; Malmendier and Tate, 2005a, 2005b; Lin et al., 2005; Huang et al., 2011), firm’s capital structure and financing decision (Hackbarth, 2008) and firm’s acquisition strategy acquisition (Malmendier and Tate, 2008). A key feature in the existing literature is the absence of studies regarding the effect of such personality traits on firm value. Hence, this paper contributes to the transport literature by investigating the effect of some CEOs’ personal characteristics on firm value. In this paper, we explore the effect of CEOs’ personal characteristics, other than behavioral factors, on firm value. The contribution of this paper on the transport literature is that this is the first paper that seeks to explain why the firms, that operate in the transport sectors, do not arrive to trade in their
http://dx.doi.org/10.1016/j.cstp.2015.01.001 2213-624X/ß 2015 World Conference on Transport Research Society. Published by Elsevier Ltd. All rights reserved.
Please cite this article in press as: Ben Mohamed, E., et al., On the effect of CEOs’ personal characteristics in transport firm value? A stochastic frontier model. Case Stud. Transp. Policy (2015), http://dx.doi.org/10.1016/j.cstp.2015.01.001
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optimal value summarized by the Tobin-Q. In fact, we use a stochastic frontier model in order to generate the optimal value that a transport firm can realize if their CEOs act in a rational manner and use the production factors in an optimal way (Q*). We then try to explain the shortfall (Q* Q) that represents the distance between the current transport firm value (Q) and the optimal firm value (Q*). More specifically, we tend to demonstrate the effect of managerial characteristics such as his/her age, education nature and tenure on transport firm value. The remainder of this paper proceeds as follows. In Section 1 we present review around the potential effect of CEOs’ personal characteristics on firm value and we formulate our hypothesis. In Section 2 we introduce our approach, model and variables. In Section 3 we describe our database and it sources. Section 4 presents and discusses our results. Finally, Section 5 concludes.
2. Literature review and hypothesis development The relationship between CEOs’ personal characteristics and transport firm value was largely ignored in the financial and economic literature. The existing literature only gives insights on the effect of managerial trait of personality on his/her corporate decision. The originality of this paper is guaranteed since the existing literature on public transport and on transportation sciences are still discussing the efficiency of transport firms and explaining the sources of inefficiency. The literature on assessing efficiency or, more generally, public transport efficiency is extensive. Analyses have focused both on developing methods for assessing public transport efficiency and on using efficiency findings to make different policy recommendations (Karlaftis and Tsamboulas, 2012). Such efficiency assessment studies have been very popular in public transport literature in a large part because of the interest in reforming public transport operations and assessing the effects of these changes on efficiency. On this subject, Jarboui et al. (2012) offer an interesting overview. Our research aims to investigate if these traits can affect firm’s value. We focus mainly on the CEO’s age, CEO’s financial education, CEO’s technical education, and his/her tenure, traits that are still unexplored in the transport literature. This literature mainly focuses on how technology and financial variables can affect transport firms’ efficiency. 2.1. CEO’s age and transport firm value Taylor (1975) argues that older decision makers tend to take longer time to reach decisions. He finds that older decision makers were able to diagnose the value of information more accurately than younger decision makers. A key feature in their empirical conclusion is that older decision makers were shown to be less affected to their overconfidence bias, a trait that positively influences their decision making quality. Ben Mohamed et al. (2012) show that CEO’s optimism decreases with manager’s age. They argue that older CEOs are supposed to be more rational since they have a long experience. The same idea was adopted by Taylor (1975) who finds that decision makers’ age increases the performance of their decision making. Ben Mohamed et al. (2012) demonstrated that CEO’s age can reduce the probability and according to it the manager will be frapped by optimism bias. Shefrin (2001) affirms that personal risk aversion appears to increase with age and it may decline after the age of 70. Many studies suggest that the age of the CEO can have an important effect on corporate financial policy choices, firm performance, and the existence of agency costs within a firm. Davidson et al. (2006) as well as Wiersema and Bantel (1992) argued that age influences duality. Age can, expectedly, positively relate to duality as human capital accumulates through years of experience.
Holmstrom (1999) and Scharfstein and Stein (1990) developed market learning models, which lead to the prediction that younger CEOs are more risk averse and, therefore, invest less aggressively than older CEOs. In sum, we can predict that older CEOs will act more rationally and so they can contribute to an increase in transport firm value. Hence, we can hypothesis that: H1. Transport firm value increases with CEOs’ age. 2.2. CEO’s background and Transport firm value Academics show some interest on how CEO’s education influences their firms (Gottesman and Morey, 2010). A key feature in this literature is that they are divided into studies which examine the type of education and the firm’s behavior (Tyler and Steensma, 1998; Finkelstein and Hambrick, 1996; and Barker and Mueller, 2002; Graham and Harvey, 2001, 2002; Graham et al., 2005) while the second branch essays to investigate the effect of CEO’s education on firm performance (Deary, 2004; Frey and Detterman, 2004; Ben Fatma et al., 2015; Ben Mohamed, 2014b). The relationship between this variable and firm value is still unexplored. In order to predict the influence of this variable on firm value we can refer to its effects on managerial decisions. Baker and Muller (2002) affirm that CEO’s background is important in her receptivity to innovative ideas and activities. Chen et al. (2011) argue that manager’s education level and his professional background are positively associated also with the firm’s innovation effort. Deary (2004) and Frey and Detterman (2004) report strong evidence on the positive impact of CEO’s education on firm’s performance while Tonello and Torok (2011) finds no strong evidence of a relationship between CEO’s education and firm performance. In his study, he found that only the CEOs having a MBA degree from a Top 20 business school enabled better operating performance and Tobin’s Q. Malmendier and Tate (2005a) argue that CEO’s financial and technical education can affect investment cash flow sensitivity. Ben Mohamed et al. (2012) demonstrated that especially CEO’s technical education can succeed to reduce managerial optimism and its effects on corporate distortions among NYSE manufacturer firms during 1999–2010. More recently, a study by Ben Mohamed et al. (2012) shows that CEOs’ education can positively affect the quality of corporate decisions, and it can reduce the bad effects of managerial optimism. In fact, they demonstrate that CEOs’ education can reduce irrationalities in corporate decision making. Financial education represents a dummy variable that takes one if CEO has undergraduate and graduate degrees in accounting, finance, business and economics. Technical education is a dummy variable and it takes one if CEO has a graduate or undergraduate degree in engineering, physics, operations research, chemistry, mathematics, biology, pharmacy and other applied sciences. We can so hypothesize that: H2a. CEO’s financial education increases transport firm value. H2b. CEO’s technical education increases transport firm value. 2.3. CEO’s tenure and transport firm value Firm value plays a crucial role in CEO turnover research: on one hand, performance is viewed as a proxy for CEO effort and, therefore, the likelihood of CEO turnover is expected to increase following financial distress and performance decline. On the other hand, the relationship between CEO turnover and company performance is viewed to mirror the efficiency of the firm’s governance mechanisms as the turnover performance sensitivity is
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hypothesized to be stronger when owners and their representatives on corporate boards assume their monitoring role vigilantly. The effect of efficiency is well documented in the literature and across international contexts (Renneboog, 2000). There are many studies that have been able to corroborate the negative relationship between turnover and firm value (Jarboui et al., 2014). Moreover, not all studies find a significantly negative relationship between turnover and firm value. For example, Dalton and Kesner (1985) failed to document any statistically significant relationship between the two variables. Morck et al. (1988) reported a large and positive relationship between the two variables indicating that CEOs might actually be hindered from leaving the company when business goes downhill. Certain scholars claim that firm value explains only very little of the variation in CEO turnover (Brickley, 2003) and that considerable performance declines are necessary before CEOs get fired (Schwartz and Menon, 1985). It follows from this that factors other than performance such as the structure of corporate ownership, the characteristics of the top executives and possibly also the independence of corporate boards are likely to influence CEO turnover decisions. Existing literature have largely focused on directly observable characteristics such as CEO’s tenure (Kaplan and Mauldin, 2008). This literature mainly studies the effect of CEO’s tenure on firm performance and on the quality of managerial decision making. Hermalin and Weisbach (1998) link between CEO’s tenure and his/ her negotiation power. They conclude that CEO’s negotiating power increases with CEO’s tenure. Firm value may increase with tenure on referring to the CEO power hypothesis. According to this hypothesis, manager will gain power over time. Dikolli et al. (2014) departing from American firms find that the negative relationship between CEO turnover and firm performance monotonically declines in CEO tenure showing the benefits of long CEO tenure, however they also find that tenure contributes to decrease the positive effect of CEO ownership change on Tobin’s Q. Other research papers document an inverse influence of CEO tenure at the firm level. Ben Mohamed et al. (2012) found that CEO tenure can contribute to the emergence of CEO’ s optimism bias which causes some well documented problems such as investment cash flow sensitivity (Heaton, 2002) which causes under or overinvestment problems. This can reduce firm value. H3. Transport firm value decreases with CEO’s tenure. 3. Methodology Referring to Habib and Ljungqvist (2005), a firm’s value is the present value of the cash flows generated by the firm’s assets, which consist of assets in place and growth opportunities. An estimate of the firm’s value is provided by the market capitalization of its debt and equity. Tobin’s Q is the ratio of the market value of debt and equity and the replacement cost of the firm’s assets in place. If a firm operates and invests in assets that are expected to create value, then its Q will be greater than 1. The more value created, the higher is the Q. To estimate the model, we need to take a stand on what we consider to be an X variable that determines the location of the frontier and what we consider to be a Z variable that explains shortfalls from the frontier. In principle, there are two ways to partition the variable set: on the basis of an econometric criterion, such as maximizing the log likelihood, or on the basis of economic theory (Habib and Ljungqvist, 2005). We employ the parametric approach using the stochastic frontier production function for panel data proposed by Battese and Coelli (1995). The starting point of this parametric approach is to estimate a stochastic optimal value frontier. This frontier can be
3
written as follows: Y it ¼ exp ðxit b þ V it U it Þ
(1)
where Yit is the output of the i-th transport operator (i = 1, 2, . . ., N) in the t-th period (t = 1, 2,. . ., T); Xit is a (1 * k) vector of input quantities of the i-th transport operator in t-th period; b is a (k * 1) vector of unknown parameters to be estimated; Vit is a random variable which is assumed to be iid N ð0; s 2v Þ and independent of Uit; the Uit is a non-negative random variable, associated with technical inefficiency of production, which is assumed to be independently distributed as truncations at zero of the N (m; s 2U ) distribution; where m ¼ zit d and variance s 2U ; and zit s 2U is a (1 * p) vector of explanatory variables associated with technical inefficiency of public transport production industry over time; where d is a (p * 1) vector of unknown parameters. Eq. (1) specifies the stochastic frontier production function in terms of the original production values. However, the technical inefficiency effects, Uit, are assumed to be a function of a set of explanatory variables, zit, and an unknown vector of coefficients, d. The technical inefficiency effect, Uit, in the stochastic frontier model (1) is specified by Eq. (2), U it ¼ zit d þ W it
(2)
where the random variable Wit follows truncated normal distribution with mean zero and variance s2, such that the point of truncation is zitd that is, Wit > zitd. These assumptions are consistent with Uit being a non-negative truncation of the N (zitd, s2U) distribution (Battese and Coelli, 1995). The mean zitd of the normal distribution, which is truncated at zero to obtain the distribution of Uit, is not required to be positive for each observation (Jarboui et al., 2013a,b). In this paper we adopt a stochastic frontier approach. Similar to Habib and Ljungqvist (2005) and Pawlina and Renneboog (2005) we use a stochastic frontier model in order to generate the optimal firm value summarized by Tobin’s Q*, we assume that firm utilizes the optimal combination of its inputs. We use a package FRONTIER 4.1 as developed by Coelli (1996) to run our empirical model. We aim to compute the difference between optimal firm value Q* and its actual Q. For this reason, we use the next empirical model: MV b It b K t1 2 ¼ b0 þ b1 ln sales þ b2 ðln salest Þ þ 3 þ 4 BV K t1 salest þ b5 O perMarg t þ b6 LEV t V t þ et
(3)
in this formulation, we denote by (MV/BV) which is defined as the market to book ratio and it represents the actual observed Q.1 ln salest represents the natural logarithm of revenues. The variable (It/Kt1) is the investment of capital ratio (Kt1/salest) which represents the capital intensity. The OperMarg and LEV represent respectively the operating margin and the market leverage. K here denotes the value of tangible long-term assets (property, plant, and equipment). We follow Habib and Ljungqvist (2005) to compute the operating margin and leverage. The operating margin is defined as the ratio of operating income before depreciation to sales. Leverage is defined as the book value of long-term debt divided by market value of equity plus book value of long-term debt. vitis a random component distributed according to NðZ t m; s 2v Þ where Zt denotes the vector of variables affecting the inefficiency level of a given firm and m is a vector of unknown parameters. Following Habib and Ljungqvist (2005) and Pawlina and Renneboog (2005), the potential determinants of the inefficiency 1 In this level we use also another measure of firm value as described by Habib and Ljungqvist, 2005. The measurement of firm value as described by these authors generates the same results.
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4
Table 2 Summary of descriptive statistics.
term are described by the next model:
vit ¼ d0 þ d1 Age þ d2 Age2it þ d3 FEit þ d4 FE2it þ d5 TEit þ d6 TE2it þ d7 Tenure þ d
2 8 Tenureit
þ wit
(4)
in this formulation, we denote by (Age) the CEO’s age; (FE) is a dummy variable that indicates if a CEO has undergraduate and graduate degrees in accounting, finance, business and economics; (TE) is also a dummy variable that indicates if CEO has a graduate or undergraduate degree in engineering, physics, operations research, chemistry, mathematics, biology, pharmacy and other applied sciences and (Tenure) represents CEO’s tenure. It is measured by the number of years that he/she spent as CEO. Finally, (Wit) is the error term. 4. Data description We use the Thomson financial database in order to extract information concerning the CEO’s characteristics. This database offers a CEO biography that presents a practical tool to construct our variables around CEO characteristics. We use the World scope database for other financial variables. We use all transport firms that are included in this database. Finally, we only retain firms with all information available in our database. Our data set consists of 53 transport firms from 17 countries publically traded between 2000 and 2011. The 17 countries are namely: Brazil, Canada, China, Germany, Hong Kong, Indonesia, Japan, Jordan, Korea (south), Malaysia, Norway, Poland, Saudi Arabia, Singapore, United Kingdom, United States and Vietnam. The number of firms by country is summarized in Tables 1 and 2. We obtain thus a panel of 636 annual observations.
BV/MVt ln salest (ln sales)2 It/Kt1 Kt1/salest opermargt Levt Age CEO Financial education Technical education Tenure
Mean
Standard deviation
Minimum
Maximum
1.931 18.724 354.262 0.263 1.070 0.029 0.249 50.695 0.842 0.512 2.708
19.034 1.916 70.698 0.459 1.204 0.500 0.262 9.349 0.365 0.500 3.200
386.347 13.632 185.837 0.000 0.024 8.703 0.000 26.000 0.000 0.000 0.000
98.179 23.056 531.577 7.095 14.650 0.525 0.848 75.000 1.000 1.000 12.000
firms with larger growth opportunities, suggesting an underinvestment problem. Our hypotheses concerning the CEO’s education are partially validated. We find that only CEO’s technical background can arrive to reduce distortions in firm value (Q* Q) however the financial education seems having no impact on firm value. Our finding can be due to the nature of our firms that are all in the transport domain which needs technical skills rather than a financial one. This result shows that CEO’s education is not significantly related to firm value, that education is a poor proxy for CEO ability. Nevertheless education does play an important role in CEO hiring Table 3 Estimation results. Variables
Parameters
Estimated MLE coefficients Model
Constant
b0
ln salest
b1
40.169 (33.398)*** 5.135 (24.228)*** 0.141 (14.880)*** 0.857 (1.073) 0.254 (1.134) 0.047 (0.101) 3.106 (3.661)*** 0.601 (0.596) 7.075 (16.966)*** 0.057 (9.043)*** 0.071 (0.067) 0.072 (0.067) 1.895 (1.918)** 1.895 (1.918)** 3.038 (2.890)*** 0.242 (2.287)***
5. Results and discussion Table 1 reports results of estimation of our model. Our results are consistent with those of Habib and Ljungqvist (2005) and Pawlina and Renneboog (2005) concerning the effect of variables in the frontier function. CEO’s age has a positive effect on transport firm value. This hypothesis is validated at the one percent level of significance (see Table 3). The estimated coefficient of the CEO’s age is negative, which indicates that CEO’s age affects negatively the inefficiency term. This result shows that CEO’s characteristics have an impact on firm efficiency: CEO’s age has a positive and significant impact on transport firm value, revealed by the fact that older CEOs invest less than younger CEOs, and that this finding is concentrated in
2
(ln sales)
b2
It/Kt1
b3
Kt1/salest
b4
Opermargt
b5
LEVt
b6
Constant
d0 d1
Age CEO (Age CEO)
Table 1 Distribution of public transport firms by countries. Countries
Number of firms
Brazil Canada China Germany Hong Kong Indonesia Japan Jordan Korea (south) Malaysia Norway Poland Saudi Arabia Singapore United kingdom United states Vietnam
1 1 8 4 3 4 8 5 2 2 1 1 2 2 4 1 4
2
d2
Financial education
d3
(Financial education)2
d4
Technical education
d5
(Technical education)2
d6
Tenure
d7
(Tenure)
2
d8
Sigma-squared
s 2 ¼ s 2V þ s 2U
Gamma
g ¼ s2 þUs2
s2
V
Log likelihood function *
U
740.469 (706.004)*** 0.986 (793.692)*** 1534.403
denotes significance at the 10% level. denotes significance at the 5% level. denotes significance at the 1% level.
**
***
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decisions; boards still use educational qualifications as criteria in evaluating potential CEOs. Finally, CEO’s tenure positively affects inefficiency term and so we can say that CEO with long tenure can negatively influence firm value. This hypothesis is validated at the one percent level of significance (see Table 3). The estimated coefficient of the CEO tenure is positive, which indicates that CEO tenure affects positively the inefficiency term. This result shows that CEO tenure has a negative and significant impact on transport firm value, which we clarify by stating that transport firm value is viewed as a proxy for CEO effort and CEO turnover is expected to increase following financial distress and efficiency declines. Also, we can say that the relationship between CEO turnover and company performance is viewed as mirroring the transport firm’s value, since the turnover-performance sensitivity is hypothesized to be stronger when owners and their representatives on corporate boards assume their monitoring role vigilantly. Also, a negative relation between firm value and the probability of forced chief executive officer (CEO) turnover was well documented by Parrino (1997) and Warner et al. (1988). This can be explained by the fact that a large tenure can create agency problems and other entrenchment problems. A CEO with long tenure is entrenched and so they can take sub-optimal decisions that aim to increase firm size but not firm value. They search to maximize firm size in order to escape from shareholder’s control.
6. Conclusion The aim of our study is to determine the incidence of CEOs’ personal characteristics on transport firm value. We propose an estimate of the distance to the optimal frontier of firm value of a sample of 53 firms in public road transport, during the period 2000–2011. This estimation is based on a stochastic frontier model. We also relate these optimal scores of transport firm value and the CEOs’ personal characteristics. This paper is an original essay that investigates the effect of CEOs personal characteristics’ influences on their firms’ value, a subject that is largely ignored by the transport literature. Using an SFA approach and a package Frontier 4.1, we empirically demonstrate that CEOs’ age, technical education and their tenures can largely explain the shortfall on firm value (Q* Q). The CEO’s age and their technical background can succeed to increase firm value while a long tenure increases inefficiency term and reduces firm value. Transport firms should take care about their CEOs characteristics; especially they should avoid a CEO with long tenure and select those with a technical education and relatively older in order to reduce inefficiency and increase their firm value. This study has some limitations that should be noted. First, the sample used is of relatively small size. Second is the number of factors that we use in order to explain the level of transport firm value. Nevertheless, this work can be considered a starting point for further research. More specifically, two main research perspectives can be outlined. The first is to add other variables, such as macroeconomic variables and structural variables in the inefficiency of firm value term, given the importance of these variables in explaining the level of firm value. A comparison could also be conducted with what is practiced in other countries, particularly emerging countries. Regarding the speed development of the behavioral economics and finance literature which demonstrate that managerial psychology is for interest, we can investigate the effect of behavioral factors such as managerial optimism and overconfidence on his/her firm value. In fact, these variables have a great impact on corporate decisions and so they can affect firm value.
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References Baccar, A., Ben Mohamed, E., Bouri, A., 2013. Managerial overconfidence and board characteristics: toward a new role of corporate governance. Aust. Basic Appl. Sci. 7 (7), 287–301. Baker, V.L., Muller, G.C., 2002. CEO characteristics and firm R&D spending. Manage. Sci. 48 (6), 782–801. Barker, V.L., Mueller, G.C., 2002. CEO characteristic and firm R&D spending. Manage. Sci. 48 (1), 782–801. Battese, G.E., Coelli, T., 1995. A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empir. Econ. 20, 325–332. Ben Fatma, E., Ben Mohamed, E., Boudabbous, S., 2015. Does entrepreneur dispositional optimism bias affect small firms’ technical efficiency. Int. J. Enterpren. Small Bus. (in press). Ben Fatma, E., Ben Mohamed, E., Boudabouss, S., 2013. Entrepreneurship theory: from rational to behavioral theory. Int. J. Contemp. Bus. Stud. 4 (5), 72–89. Ben Mohamed, E., Fairchild, R., Bouri, A., 2014a. Investment cash flow sensitivity under managerial optimism: new evidence from NYSE panel data firms. J. Econ. Financ. Adm. Sci. 19 (36), 11–18. Ben Mohamed, E., Mohamed Naceur, R., Baccar, A., Bouri, A., 2014b. CEO’s personal characteristics, ownership and investment cash flow sensitivity: evidence from NYSE panel data firms. J. Econ. Financ. Adm. Sci. 19 (37), 98–103. Ben Mohamed, E., Baccar, A., Fairchild, R., Bouri, A., 2012. Does corporate governance affect managerial optimism: evidence from NYSE panel data firms. Int. J. Euro-Mediterr. Stud. 5 (1), 41–56. Bertrnard, M., Schoar, A., 2003. Managing with style: the effect of managers on firm policies. Q. J. Econ. 118 (4), 1169–1208. Brickley, J.A., 2003. Empirical research on CEO turnover and firm-performance: a discussion. J. Acc. Econ. 36, 227–233. Campbell, C., Jhonson, S., Rutherford, J., Stanley, B., 2011. CEO confidence and forced turnover. J. Financ. Econ. 101 (3), 695–712. Chen, J., Ezzamel, M., Cai, Z., 2011. Managerial power theory, tournament theory, and executive pay in China. J. Corp. Finance 17, 1176–1199. Coelli, T.J., 1996. A Guide to Frontier Version 4. 1: A Computer Program. University of New England, Armidale, Australia. Dalton, D.R., Kesner, I.F., 1985. Organizational performance as an antecedent of inside/outside chief executive succession: an empirical assessment. Acad. Manage. J. 28 (4), 749–762. Davidson, W.N., Nemec, C., Worrell, D.L., 2006. Determinants of CEO age at succession. J. Manage. Gov. 10, 35–57. Deary, I.J., 2004. Intelligence: A Very Short Introduction. Oxford University Press. http://ukcatalogue.oup.com/product/9780192893215.do. Dikolli, S.S., Maye, W.J., Nanda, D., 2014. CEO tenure and the performanceturnover relation. Rev. Account. Stud. 19 (1), 281–327, http://dx.doi.org/ 10.1007/s11142-013-9247-6. http://link.springer.com/journal/11142/19/1/ page/1. Finkelstein, S., Hambrick, D.C., 1996. Strategic Leadership: Top Executives and their Effects on Organizations. West Publishing Company, St. Paul, MN. Frey, M.C., Detterman, D.K., 2004. Scholastic assessment or g? The relationship between the scholastic assessment test and general cognitive ability. Psychol. Sci. 15 (6), 373–378. Gottesman, A.A., Morey, M.R., 2010. CEO educational background and firm financial performance. J. Appl. Finance 20 (2), 70–82. Graham, J., Harvey, C., Rajgopal, S., 2005. The Economic Implications of Corporate Financial Reporting, Working Paper. Duke University. Graham, J., Harvey, C., 2001. The theory and practice of corporate finance: evidence from the field. J. Financ. Econ. LX, 187–243. Graham, J., Harvey, C., 2002. How do CFOs make capital budgeting and capital structure decisions? J. Appl. Corp. Finance 15, 8–23, http://dx.doi.org/10.1111/ j.1745-6622.2002.tb00337.x. Habib, M.A., Ljungqvist, A.P., 2005. Firm value and managerial incentives. J. Bus. 78 (6), 2053–2094. Hackbarth, D., 2008. Managerial traits and capital structure decisions. J. Financ. Quant. Anal. 43 (4), 843–882. Heaton, J.B., 2002. Managerial optimism and corporate finance. Financ. Manage. 31 (Summer (2)), 33–45. Hermalin, B.E., Weisbach, S.M., 1998. Endogenously chosen boards of directors and their monitoring of the CEO. Am. Econ. Rev. LXXXVIII, 96–118. Holmstrom, B., 1999. Managerial Incentive Problems: a Dynamic Perspective. Rev. Econ. Stud. 66, 169–182. Huang, W., et al., 2011. Agency cost, top executives’ overconfidence, and investment-cash flow sensitivity: evidence from listed companies in China. PacificBasin Financ. J. 19, 261–277. Jarboui, S., Forget, P., Boujelbene, Y., 2013b. Public road transport efficiency: a stochastic frontier analysis. J. Transp. Syst. Eng. Inf. Technol. 13 (5), 64–71. Jarboui, S., Forget, P., Boujelbene, Y., 2012. Public road transport efficiency: a literature review via the classification scheme. Public Transport 4 (2), 101–128. Jarboui, S., Forget, P., Boujelbene, Y., 2013a. Efficiency evaluation in public road transport: a stochastic frontier analysis. Transport, http://dx.doi.org/10.3846/ 16484142.2013.785019 (in press). Jarboui, S., Forget, P., Boujelbene, Y., 2014. Inefficiency of public road transport and internal corporate governance mechanisms. Case Stud. Transp. Policy, http://dx.doi.org/10.1016/j.cstp.2014.05.004. Jensen, M., 1986. Agency costs of free cash flow, corporate finance and takeovers. Am. Econ. Rev. 76, 323–329.
Please cite this article in press as: Ben Mohamed, E., et al., On the effect of CEOs’ personal characteristics in transport firm value? A stochastic frontier model. Case Stud. Transp. Policy (2015), http://dx.doi.org/10.1016/j.cstp.2015.01.001
G Model
CSTP-53; No. of Pages 6 6
E. Ben Mohamed et al. / Case Studies on Transport Policy xxx (2015) xxx–xxx
Jensen, M., Meckling, W., 1976. Theory of the firm: managerial behavior, agency costs, and ownership structure. J. Financ. Econom. 3, 305–360. Kaplan, S.E., Mauldin, E.G., 2008. Auditor rotation and the appearance of independence: evidence from non-professional investors. J. Account. Public Policy 27, 177–192. Karlaftis, M.G., Tsamboulas, D., 2012. Efficiency measurement in public transport: are findings specification sensitive? Transp. Res. A 46, 392–402. Lin, Y., Hu, S., Chen, M., 2005. Managerial optimism and corporate investment: some empirical evidence from Taiwan. Pacific-Basin Financ. J. 13, 523–546. Malmendier, U., Tate, G., 2005a. CEO overconfidence and corporate investment. J. Financ. 60 (6), 2661–2700. Malmendier, U., Tate, G., 2005b. Does overconfidence affect corporate investment? CEO overconfidence measures revisited. Eur. Financ. Manage. 11 (5), 649–659. Malmendier, U., Tate, G., 2008. Who makes acquisitions? CEO overconfidence and the market’s reaction. J. Financ. Econ. 89, 20–43. Morck, R., Shleifer, A., Vishny, R., 1988. Management ownership and market valuation: an empirical analysis. J. Financ. Econ. 20, 293–315. Myers, S.C., Majluf, N.S., 1984. Corporate financing and investment decisions when firms have information those investors do not have. J. Financ. Econ. 3, 2. Parrino, R., 1997. CEO turnover and outside succession: a cross-sectional analysis. J. Financ. Econ. 46, 165–197.
Pawlina, G., Renneboog Luc, D.R., 2005. Is investment-cash flow sensitivity caused by agency costs or asymmetric information? Evidence from the UK. Eur. Financ. Manage. 11 (4), 483–513. Renneboog, L., 2000. Ownership, managerial control and the governance listed on the Brussels stock exchange. J. Bank. Financ. 24 (12), 1959–1995. Scharfstein, D.S., Stein, J.C., 1990. Herd behavior and investment. Am. Econ. Rev. 80 (3), 465–479. Schwartz, K., Menon, K., 1985. Auditor switches by failing firms. Account. Rev. 14 (2), 248–260. Shefrin, H., 2001. Behavioral corporate finance. J. Appl. Corp. Financ. 14, 3. Taylor, R., 1975. Age and experience as determinants of managerial information processing and decision making performance. Acad. Manage. J. 18 (1), 74–81. Tonello, M., Torok, J., 2011. The 2011 U.S. Director Compensation and Board Practices Report (The Conference Board, New York). Tyler, B.B., Steensma, H.K., 1998. The effects of executives’ experiences and perceptions on their assessment of potential technical alliances. Strateg. Manage. J. 19 (10), 939–965. Warner, J.B., Watts, R.L., Wruck, K.H., 1988. Stock Prices and Top Management Changes. J. Financ. Econ. 20 (1), 461–692. Wiersema, M.F., Bantel, K.A., 1992. Top Management Team Demography and Corporate Strategic Change. Acad. Manage. J. 35, 91–121.
Please cite this article in press as: Ben Mohamed, E., et al., On the effect of CEOs’ personal characteristics in transport firm value? A stochastic frontier model. Case Stud. Transp. Policy (2015), http://dx.doi.org/10.1016/j.cstp.2015.01.001