1

Does Firm Headquarter Location Matter Management Team Size and

Reputation: Evidence from Taiwan Market

Abstract

This study explores whether or not the geography characteristics of a firm’s location

have impacts on the firm’s management team size and reputation (later denoted as MS&R) by

employing the hand-collected MS&R data in Taiwan market from year 2006 to 2012.

Empirical results of this study show that firm location characteristics, including distance and

non-distance related geography variables, have significant impacts on MS&R. A firm with the

headquarter location far from urban or major transportation stations (railway stations and

airports) would have small team size and poor management team reputation. Furthermore, the

results also reveal that a firm located in big five cities has more attraction than the north cities

for general managers while has the opposite effects for managers with high reputation quality.

The results are robust when controlling for other firm characteristics variables.

Keywords: Firm headquarter location; Management team size; Team reputation; Geography

characteristics

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1. Introduction

A firm’s operating performance and its competences are not only affected by

macroeconomic condition but also its top management team quality. Intuitively, managers

play the important role in a firm’s operating strategies and performance. Many studies

demonstrate that a firm’s management team quality affects its operating performance

(Haleblian and Finkelstein, 1993; Chemmanur and Paeglis, 2005), financial decisions

(Chemmanur et al., 2009), investment policies (Chemmanur et al., 2009), and credit risk

(Chen et al., 2011). Management team size and reputation are two main representatives of

management team quality (Chemmanur and Paeglis, 2005). Though many studies have been

dedicated to investigate economic consequences of management team characteristics (e.g.

leverage ratio; dividend policy; credit risk), few studies explore the causes of management

team size and reputation, especially from the perspective of firm location.

Previous studies show that a firm’s location plays an important role in determining its

board structure, investment decision, and dividend policy. Knyazeva et al. (2011) find that

firm locate near local pool has higher proportion of independent board. John et al. (2011)

show that remotely located firms farther from shareholder, which increasing the information

asymmetry for investor to observe managerial investment decision and exacerbates

managerial agency problem.1 Furthermore, these remotely firms pay higher dividends tend to

decrease agency conflict. In other words, a remotely located firm that facing free cash flow

problem precommits to mitigate agency problem by the tool of dividends. Moreover, Francis

et al. (2012), Masulis and Mobbs (2011), and Knyazeva et al. (2011) also find that firm

location affects CEO (Chief Executive Officer) power and board composition. Yang and

Chen (2012) also demonstrate that Taiwanese firm location affects its board quality. The

above results show that a firm’s location affects its board structure, investment and dividend

1 Lang and Lizenberger (1989) and Smith and Watts (1992) also show that the distance far from urban for a firm’s headquarter location exacerbates agency conflicts.

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policy. Management team quality is few discussed among the issues of firm location,

especially for Taiwan market.2 However, a firm’s location quality seems to have some

influences on its management team size and reputation. For example, General Motors (GM)

recently plans to open four new information technology centers in the U.S., which is intended

to improve GM‘s business processes and drive down costs. One of center candidate is Austin,

because Austin metropolitan area is home to a growing technology community that includes

the University of Texas at Austin and computer maker Dell Inc. The news reveals that a firm

considers the location when expanding its business due to the available human resources (e.g.

managers). Therefore, firm location seems to affect the availability and quality of managers

for a firm. Although there are many studies has been focused on local effect on board

composition, agency problem and dividend policy, few studies explore the firm location

effect on management team size and reputation.

There are many possible reasons for the local effect on management team size and

reputation, such as the availability of local manager pool relevant to the firm’s expertise. For

outside investors, local residents can acquire soft information about managerial decisions and

operating performance at lower costs (e.g., Petersen, 2004). Instead, non-local outside

investors would face more incomplete information and must take more efforts to monitor

managers effectively due to quite a long distance. For firm insiders, firms with far from large

pool of prospective managers have higher opportunity cost for potential managers to join this

firm. Consistent with the Petersen (2004), Loughran (2008) also demonstrate that costs in

generating information are higher for rural firms with few investors in their proximity, than

for urban firms with many nearby investors. In addition, firm location may also affect a

firm’s management team size and reputation because many studies demonstrate that

management team characteristics affect firm operating performance (Haleblian and

Finkelstein, 1993; Chemmanur and Paeglis, 2005), financial and investment policies 2 Chen et al. (2013) show that U.S. firm location affects its management team quality.

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(Chemmanur et al., 2009), and credit risk (Chen et al., 2011).3 However, management team

size and reputation (later denoted as MS&R) are few discussed from the geography

perspective. This study basically follows Chemmanur et al. (2009), Chen et al. (2011), Chen

et al. (2013) to define a firm’s MS&R variables, which show the size of the management

team and management team members’ prestige.4

According the above discussion, firm location seems to affect its management team

(employees) composition. However, most of existing studies focus on local effect on board

composition, agency problem and dividend policy rather than management team

characteristics. Therefore, this study focuses on relationship between firm location and

MS&R.

Loughran (2008) demonstrate that an urban firms issues equity, by employing a

higher-quality underwriter than otherwise similar rural firm. Knyazeva et al. (2011) show that

a firm located in the larger local human resource (director) pool has better board quality

(measured as independent directors).5 Yang and Chen (2012) demonstrate that Taiwanese

firm location quality positively affects its board quality. Therefore, we can reasonably

hypothesize that a firm’s distance to urban negatively relates to its management team size and

reputation.

This current study collects different kind of geography variables, and employs a

hand-collected MS&R data of 9,040 annual Taiwanese observations from year 2006 to 2012. 3 Haleblian and Finkelstein (1993) show that the bigger the management team size, the higher performance they are. Moreover, the percentage of management team members in core department also increases corporate performance. Chemmanur and Paeglis (2005) find that better MQ is positively related to IPO size and post-IPO performance. Chammanur et al. (2009) explore that better MQ tend to lower leverage ratio, dividend payout ratio, information asymmetry but higher investment level. 4 Chemmanur et al. (2009), Chen et al. (2011), and Chen et al. (2013) classify management team quality into

three dimensions: management expertise, management team structure, team size and reputation. Management expertise means management team’s knowledge and prior experience; management team structure refers to average team members’ tenure and team tenure dispersion; and management team size and reputation show the size of the management team and management team members’ prestige. 5 For another perspective, Coval and Moswitz (2001) show that money managers prefer larger firms when investing in remote stocks and smaller firms when investing locally. As a result, higher visibility seems to can resist local manager pool to recruit, even for potential manager that they would have more willingness to ignore opportunity cost. Hence, firm with more reputation would attract prospective manager and could break down limited of distance.

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Empirical results of this study show that firm location characteristics, including distance and

non-distance related geography variables, have significant impacts on MS&R. A firm with the

headquarter location far from urban would have lower team size and poor management team

reputation, such as the low percentage of corporate boards (except their own firm) that

management team members sit on (later denoted as Board) and the low percentage of

non-profit corporate boards that management team members sit on (later denoted as BNP). In

addition, the firm with the headquarter location far from major railway stations and airports

also have lower team size and poor management team reputation. Furthermore, the results

also reveal that a firm located in big five cities has more attraction than the north cities for

general managers while has the opposite effects for managers with high reputation. The

results are robust under considering other control variables.

The remainder of the paper is organized as follows. The section 2 describes various

measures of management team size, team reputations and firm quality. Section 3 presents the

theories and hypotheses. Section 4 summarizes other major variables used in the empirical

examinations. Section 5 presents and analyzes empirical results. Last, section 6 provides

concluding remarks.

2. Measures of management team size and reputation, and firm quality

2.1. Measures of management quality and reputation

As former remark, based on Chemmanur and Paeglis (2005) and Chemmanur et al.

(2009), and Chen et al. (2011), a firm’s MQ are classified into three dimensions: management

expertise, management team structure, and management team size and reputation respectively.

In this paper, we focus on the third dimension on management team size and reputation.

Table 1 exhibits the variables of the management team size and reputation dimension. The

following Table 1 shows the classifications and definitions.

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[Insert Table 1 here]

The dimension we used is team size and reputation includes three variables. The first

one is the size of the top management team (TSIZE), which is measured by number of

managers with the position of vice president or higher. Management reputation in business

communities refer to images built up by members of the management team. Management

team members who serve as other firms’ board of directors (BOARD) or sit on non-profit

boards (BNP) might have higher visibility and greater impression on outsider. As a result, the

greater value of three variables, the better is the management team size and reputation.

2.2. Proxies for other aspects of firm quality

In purpose of dealing with the effect of firms’ location on MQ might be interfered by

other characteristics of firm quality, we use two control variables to descend the possible

effects. One is the firm size (Size), defining as the natural log of the firm’s net sales at the end

of a fiscal year. The other control variable is a firm’s operating cash flow (OCF), calculated

for the operating cash flow divide total asset.

2.3. Measures of firm location geography characteristics

Based on the measurement method of local human resource pool in Knyazeva et al.

(2011), Chen et al. (2013) and Yang and Chen (2012), this study employs distance and

non-distance related geography variables. The former category includes the variables of DIST,

DIST_TPE, DIST_MTRA, DIST_TRA, DIST_HSR, DIST_NAirport, DIST_Airport, Metro,

Five, and North. The DIST/ DIST_TPE variables are the distances in kilometers to the

located city hall/ the Taipei city hall. The DIST_MTRA/ DIST_TRA/ DIST_HSR variables

represent the distances in kilometers to the closest top-fifteen railway stations/ the closest

railway station/ the closest high-speed railway station. The DIST_NAirport/ DIST_Airport

variables represent the distances in kilometers to the closest international airports in north

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areas of Taiwan/ the closest international airports. The Metro/ Five/ North variables are

dummy variables that the values equal to one if the located city of firm headquarter has metro

system/ belongs to five big cities/ belongs to the north areas of Taiwan.

The latter category includes the variables of IND_D, Bank_D, PD, BS, and PI. Industry

density (IND_D) is the number of firms per unit squared kilometer. The Bank_D variable is

the percentage of banks in the same three-digit ZIP radius of the firm’s headquarters.

Population density (PD) is the population density of the county where the firm is

headquartered (in ten thousands). Bachelor's degree (BS) is the percent of total labor forces

people who have completed a Bachelor's degree in the county. The PI variable is the personal

income of the county where the firm is headquartered (in ten thousands).

Moreover, we measure firm headquarters locations reported in financial annual reports.

Headquarters locations are generally chosen in the early life of a firm. Furthermore, Pirinsky

and Wang (2006) argue that relocations of headquarters are infrequent. Therefore, we regard

firm location as a predetermined variable and treat the concentration of companies and

organizations in the firm’s vicinity as primarily a source of exogenous variation.

3. Theories and hypotheses

In this section, we introduce the main hypotheses on the firm location effect on its

management team size and reputation.

Main Hypothesis: Remotely located firms tend to have poor management size and reputation.

Knyazeva et al. (2011) argue that firms located near the urban have higher board quality

(independence), firm value, operating performance. Francis et al. (2012) demonstrate that

rural firms may have CEO turnover rate. Moreover, opportunity cost is considerable for

directors with full-time potions in distant locations. Hence, firms located far from urban

hardly recruit prospective managers and tend to have lower MS&R.

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Loughran (2008) demonstrate that an urban firms issues equity, by employing a

higher-quality underwriter than otherwise similar rural firm. Knyazeva et al. (2011) show that

a firm located in the larger local human resource (director) pool has better board quality

(measured as independent directors). Therefore, we can reasonably hypothesize that a firm’s

distance to urban negatively relates to its management team size and reputation.

4. Data and sample selection

4.1. Data sources and selection

The sample used in this study that the dependent variable, management team size and

reputation), is mainly hand-collected from Taiwanese Security and Exchange Commission

website. The relevance information of executive officers, including present professional title,

joining date, educational background, past experiences, outside positions, are disclosed in

financial annual reports and proxy statement. Moreover, data of computing the firm quality

and control variables are from TEJ.

The independent variables of corporate location, transportation, population density and

ZIP code are hand-collected from financial annual reports, and the websites of Chung-Hwa

Post Co. and Taiwan Directorate General of Budget, Accounting and Statistics. The sample of

this study includes only those firms whose data exist in both Taiwan SEC and TEJ. We take

away that firm is financial industries. After the above screening criteria, there are a totally

fifteen different kinds of geography characteristics data and 9,040 firm-year observations

from year 2006 to 2012 with both variables of MS&R and distance to city.

4.2. Geography variables

We use several measures for geography effect. We mainly estimate that distance of firm

headquarter (DIST) to local city hall in Taiwan by the tool of GoogleMap. Similarly, other

distance measures are also calculated by the tool of GoogleMap, including the DIST_MTRA,

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DIST_TRA, DIST_HSR, DIST_NAirport, and DIST_Airport variables. These variables are

belong to the measures of transportation, similar with John et al. (2011) and Yang and Chen

(2012).

Moreover, some non-distance related geography variables are also useful in evaluating

the remoteness of the firm, such as personal income (PI), population density (PD), firm

density (IND_D), and bank density (Bank_D). In addition, we also use percentage of people

who have earned Bachelor's degree (BS) to capture the level of education in the county. We

presume higher percentage of educated people indicate close to the metropolitan area.

4.3.Other Control Variables

This study uses the some firm characteristics variables as control variables and

demonstrates these variables in the following. Financial leverage (LEV) is defined as the ratio

of total debt book value (debt in current liability plus long-term debt) to asset market value

(sum of total debt book value and equity market value). Return on assets (ROA) is the ratio of

net income divided by total assets, representing a firm's profitability. Moreover we consider

the volatility of ROA which is measured by standard deviation of past 5-year ROA data.

INTANG is defined as intangible assets divided by total assets, and OCF is measured by

operating cash flow divided by total asset. However, most of firms are not with the relevant

scale of asset, we use firm size variable to control the difference which is measured by nature

logarithm of total asset.

The LNAT variable is defined as the log of the firm’s asset. Fage is the age of the firm.

R&D intensity (R&D) is defined as R&D related expenses divided by total asset. The data for

R&D related expense is collected from TEJ. Market-to-book (MB) is the ratio of firm’s

market equity value to book equity value ratio, which the higher ratio represent firm with

higher value added to the shareholder. INST is the ratio of institutional holding to total

outstanding shares. In this study we also consider the dividend yield (DY), due to higher

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dividend payout would affect firm asset value.

4.4. Summary statistics

Table 2 shows the sample distribution. The sample includes 9,040 annual firm

observations. Table 3 presents the summary statistics of MS&R variables, geography variable,

and control variables used in the empirical analyses. On average, 68.36% and 76.26% are

respectively located in top five cities and north cities, 9.43 kilometers is distance to local city

hall, and 12.36 and 29.98 kilometers to are the distances to major TRA stations and

international airport, respectively.

Moreover, the average size of a firm’s management team (TSIZE) is 9.27, on average,

1.82% of managers sit on non-profit boards (BNP), 28.99% serve as board members in other

companies (BOARD).

[Insert Table 2 here]

[Insert Table 3 here]

5. Empirical tests and results

5.1. Panel data analyses: Single variate analyses\

This study employs Eq.(1) to explore the firm location effect on firm’s MS&R by using

9,040 Taiwanese firm-year observations from year 2006 to 2012. The fixed effects (industry

and year) and heteroskedasticity issues are considered in these results.

Reput�� = + �� ��� + ��� (1)

Reput= TSIZE, BNP, BOARD

LOC= Distance related variables v.s. Non-distance related variables

Where Distance related variables: DIST, DIST_TPE, DIST_MTRA, DIST_TRA, DIST_HSR,

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DIST_NAirport, DIST_Airport, Metro, Five, and North.

Non-distance related variables: IND_D, Bank_D, PD, BS, and PI

Table 4, 5 and 6 exhibit the results of firm location effect on managerial team size and

reputation, respectively. Panel A of Table 4, 5, and 6 respectively show that most of distance

related geography variables (DIST, DIST_TPE, DIST_MTRA, DIST_TRA, DIST_HSR,

DIST_NAirport, DIST_Airport) significantly and negatively relate to managerial team size

(TSIZE) and reputation (Board, BNP) while the dummy variables of major cities and metro

transportation (Metro, Five, and North) have the opposite impacts. The results reveal that

long distances to local city hall, major city, major railway stations, and international airports

unlikely attract managers with high reputation to join the firm. Moreover, the Five variable

significantly and positively relates to the management team size while the North variable

insignificantly relates to it. It reveals that a firm located in big five cities has more attraction

than the north cities for general managers. Furthermore, the results also reveal that a firm

located in the north cities has more attraction than big five cities for managers with high

reputation, measured by Board or BNP variables.

Panel B of Table 4, 5, and 6 respectively show that most of non-distance related

geography variables significantly and positively relate to managerial team size (TSIZE) and

reputation (Board, BNP). Especially, the empirical results point out that the BS variable and

local bank density are both positively related to the reputation of executive officer (BNP,

Board). It reveals that a firm’s location with higher educational or financial activity density

has bigger human pool and then attracts more talents to join the firm.

According the above discussions, these empirical results preliminarily confirm the main

hypothesis.

[Insert Table 4 here]

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[Insert Table 5 here]

[Insert Table 6 here]

5.2. Panel data analyses: Controlling for firm size and other variables

To tell apart the effects of management team size and reputation from those of firm size,

we add twelve control variables. This section employs Eq.(2) to explore the local effect on

management team reputation by using 9,040 Taiwanese firm observations from year 2006 to

2012. The fixed effects (industry and year) and heteroskedasticity issues are considered in

these results.

������� = + ��� ��� + �������� + ��� ��� + �� ���� + ���� �� + �!"#�� + �$%&��

+ �'()*�)+�� + �,�"�� + ��-�)*��� + ��� "(���

+���().*�� + ���� � �� + ��� (2)

Reput= TSIZE, BNP, BOARD

GEO= Distance related variables, Non-distance related variables

Where Distance related variables: DIST, DIST_TPE, DIST_MTRA, DIST_TRA, DIST_HSR,

DIST_NAirport, DIST_Airport, Metro, Five, and North.

Non-distance related variables: IND_D, Bank_D, PD, BS, and PI

Equation (2) explores the local effects on management team reputation with adding the

control variables. As above mentioned, we use the variables of TSIZE, Board, and BNP.

Table 7, 8, 9 present the empirical results of we used based on equation (2), which present the

location effect on TSIZE, Board, and BNP with control variables, respectively.

Panel A of Table 7, 8, and 9 respectively show that most of distance related geography

variables still significantly and negatively relate to managerial team size (TSIZE) and

reputation (Board, BNP) while the dummy variables of major cities and metro transportation

(Metro, Five, and North) have the opposite impacts. In addition, Panel B of Table 7, 8, and 9

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respectively show that most of non-distance related geography variables still significantly and

positively relate to managerial team size (TSIZE) and reputation (Board, BNP). These results

provide the more robust evidences for our main hypothesis.

[Insert Table 7 here]

[Insert Table 8 here]

[Insert Table 9 here]

6. Conclusion

This study explores whether or not the firm location geography effects on

management team size and reputation by employing the 9,040 firm-year observations of

hand-collected MS&R data from year 2006 to 2012. Empirical results of this study show that

firm location characteristics, including distance and non-distance related geography variables,

have significant impacts on MS&R. A firm with the headquarter location far from urban

would have small team size and poor management team reputation, such as the low

percentage of corporate boards (except their own firm) that management team members sit on

(later denoted as Board) and the low percentage of non-profit corporate boards that

management team members sit on (later denoted as BNP). In addition, the firm with the

headquarter location far from major railway stations and airports also have lower team size

and poor management team reputation. Furthermore, the results also reveal that a firm located

in big five cities has more attraction than the north cities for general managers while has the

opposite effects for managers with high reputation. Moreover, the results are robust when

controlling for other well-known variables.

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References

Bushee, B., Noe, C., 2000. Corporate disclosure practices, institutional investors, and stock

return volatility. Journal of Accounting Research 38, 171-202.

Chemmanur, T.J., Paeglis, I., 2005. Management quality, certification, and initial public

offerings. Journal of Financial Economics 76, 331–368.

Chemmanur, T.J., Paeglis, I., Simonyan, K., 2009. Management quality, financial and

investment policies, and asymmetric information. Journal of Financial and Quantitative

Analysis 44, 1045–1079.

Chen, T.K, Liao, H.H., Zeng, Y.H., 2011. Management quality and corporate credit rating:

Structural credit model perspectives. Working paper.

Chen, T.K., Liao, H.H., Cheng, C.W., 2013. Firm location and management team quality.

Working paper.

Coval, J., Moskowitz, T., 2001. The geography of investment: Informed trading and asset

prices. Journal of Political Economy 109, 811–841.

Francis, B., Hasan, I., John, K., Waisman, M., 2012. Urban agglomeration and CEO

compensation. Bank of Finland Research Discussion Paper 17.

Haleblian, J. and Finkelstein, S., 1993. Top management team size, CEO dominance, and

firm performance: The moderating roles of environmental turbulence and discretion.

Academy of Management Journal 36, 844-863.

Ivkovi c, Z., Weisbenner, S., 2005. Local does as local is: Information content of the

geography of individual investors' common stock investments. Journal of Finance 60,

267–306

John, K., Knyazeva, A., Knyazeva, D., 2011. Does geography matter? Firm location and

corporate payout policy. Journal of Financial Economics 101, 533-551.

Knyazeva, A., Knyazeva, D., Masulis, R., 2011. Effects of local director markets on corporate

boards. Working paper.

15

Lang, L., Litzenberger, R., 1989. Dividend announcements: cash flow signaling vs. free cash

flow hypothesis. Journal of Financial Economics 24, 181–191.

Loughran, T., Schulz, P., 2006. Asymmetric information, firm location, and equity issuance,

Working paper.

Loughran, T., 2008. The impact of firm location on equity issuance. Financial Management

37, 1-21.

Masulis, R., Mobbs, S., 2011. Are all inside directors the same? Journal of Finance 66,

823-872.

Merton, R.C., 1974. On the pricing of corporate debt: The risk structure of interest rates.

Journal of Finance 29, 449-470.

Petersen, M.A., 2004. Information: Hard and soft. Working paper, Northwestern University.

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

approaches. Review of Financial Studies 22, 435-480.

Pirinsky, C., Wang, Q., 2006. Does corporate headquarters location matter for stock returns?

Journal of Finance 61, 1991-2015.

Smith, C., Watts, R., 1992. The investment opportunity set and corporate financing, dividend,

and compensation policies. Journal of Financial Economics 32, 263–292.

Yang, H.L., Chen, Y.S., 2012. The geography effect on corporate board quality. Working

paper.

Yu, F., 2005. Accounting transparency and the term structure of credit spreads. Journal of

Financial Economics 75, 53-84.

.

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Table 1. Proxies of Management Team Size and Reputation

This study follows Chemmanur et al. (2009), Chen et al. (2011), and Chen et al. (2013) to employ the variables of team size and reputation as the main proxies of management team quality. The variables in each dimension are as follows.

Dimensions Measures Description

Team Size and

Reputation

TSIZE The number of executive officers (above vice president) on a

firm’s management team

BOARD The percentage of corporate boards (except their own firm)

that management team members sit on

BNP The percentage of non-profit corporate boards that

management team members sit on

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Table 2. Sample Distribution

The sample period is yearly between 2006 and 2012. During the sample period, the sample includes 9,040 annual firm observations. Tables reported are the numbers of pooled observations in the given years. The located subsamples are sorted by Taiwanese cities and counties.

City/Year 2006 2007 2008 2009 2010 2011 2012 Total

Taipei City 390 399 402 407 419 420 431 2,868

New Taipei City 225 228 233 239 242 247 259 1,673

Keelung City 4 4 4 4 4 4 4 28

Taoyuan County 147 153 157 163 166 168 171 1,125

Yilan County 1 1 1 1 1 1 1 7

Hsinchu City 100 107 108 110 113 113 114 765

Hsinchu County 51 57 62 61 65 65 67 428

Miaoli County 18 18 19 19 19 19 19 131

Taichung City 74 75 77 81 83 85 89 564

Nantou County 9 10 10 10 10 10 10 69

Changhua County 20 21 23 24 24 25 26 163

Yunlin County 9 10 10 10 10 10 11 70

Chiayi County 4 4 4 4 4 4 4 28

Tainan City 66 66 68 69 72 72 75 488

Pingtung County 5 5 5 6 6 6 6 39

Kaohsiung City 79 82 84 84 85 85 88 587

Taitung County 1 1 1 1 1 1 1 7

Total 1,203 1,241 1,268 1,293 1,324 1,335 1,376 9,040

18

Table 3. Summary Statistics of Major Variables This table presents the mean, median, standard deviation, maximum and minimum of major variables used in empirical analyses. TSIZE is a firm’s management team size, defined as executive officers with a rank of vice president or higher. BNP is the percentage of non-profit board management team members serve as. Board is percentage of other firms’ boards that management team members serve as. The DIST/ DIST_TPE variables are the distances in kilometers to the located city hall/ the Taipei city hall. The DIST_MTRA/ DIST_TRA/ DIST_HSR variables represent the distances in kilometers to the closest top-fifteen railway stations/ the closest railway station/ the closest high-speed railway station. The DIST_NAirport/ DIST_Airport variables represent the distances in kilometers to the closest international airports in north areas of Taiwan/ the closest international airports. The Metro/ Five/ North variables are dummy variables that the values equal to one if the located city of firm headquarter has metro system/ belongs to five big cities/ belongs to the north areas of Taiwan. Industry density (IND_D) is the number of firms per unit squared kilometer. The Bank_D variable is the percentage of banks in the same three-digit ZIP radius of the firm’s headquarters. Population density (PD) is the population density of the county where the firm is headquartered (in ten thousands). Bachelor's degree (BS) is the percent of total labor forces people who have completed a Bachelor's degree in the county. The PI variable is the personal income of the county where the firm is headquartered (in ten thousands). ROA is the ratio of net income to book value of assets. ROAV is the volatility of ROA. LEV is financial leverage, defined as the book value of debt divided by total asset market value. Fage is the age of firm. INTANG is defined as intangible assets divided by total assets. MB is the market-to-book ratio, defined as the market value of equity divided by the book value of equity. LNAT is the natural logarithm of the book value of the firm’s assets at the end of the fiscal year. R&D intensity (RD) is defined as R&D related expense divided by total asset. OCF is used by operating cash flow divided by total asset. Dividend yield (DY) is dividend yield. INST and ODIR are the institutional investors’ holdings and the number of outside directors, respectively. Variables Mean Median Std. dev Min Max Panel A: Summary Statistics of Management Quality Variables TSIZE 9.2659 8.0000 6.3447 1.0000 77.0000 BNP 0.0182 0.0000 0.0833 0.0000 1.0000 Board 0.2899 0.2500 0.2456 0.0000 1.0000 Variables Mean Median Std. dev Min Max Panel B: Summary Geography Variables DIST 9.4250 6.6000 8.2538 0.1000 98.7000 DIST_TPE 87.7544 37.0000 115.6706 0.2300 467.7000 Metro 0.4622 0.0000 0.4986 0.0000 1.0000 DIST_MTRA 12.3617 8.2000 17.0668 0.5500 97.4000 DIST_TRA 4.8582 3.8000 4.0255 0.1500 45.3000 DIST_HSR 10.0471 8.2000 8.5979 0.0000 116.0000 DIST_Airport 29.9816 22.1000 23.9031 3.9300 187.3000 DIST_NAirport 78.1057 25.1000 106.4149 3.9300 439.7000 Five 0.6913 1.0000 0.4620 0.0000 1.0000 North 0.7583 1.0000 0.4281 0.0000 1.0000 BS 0.3015 0.2955 0.1132 0.0778 0.5621 PI 39.7802 35.8283 8.2200 24.3559 52.4598 PD 0.4200 0.1887 0.3835 0.0064 0.9835 IND_D 217.6556 57.5781 257.8320 0.2816 615.9713 Bank_D 0.0171 0.0124 0.0148 0.0000 0.0494 Variables Mean Median Std. dev Min Max Panel C: Summary Statistics of Control Variables Fage 25.3232 23.2890 12.5790 0.2082 66.7151 ODIR 2.7264 3.0000 1.7208 0.0000 16.0000 OCF 0.0676 0.0676 0.1197 -0.9764 1.5692 LNTA 15.2151 15.0309 1.4446 9.7953 21.4384 INST 0.3632 0.3280 0.2261 0.0000 1.0000 LEV 0.2095 0.1795 0.1876 0.0000 1.7548 ROA 4.2701 4.6600 11.2959 -438.8600 85.7600 ROAV 5.5677 4.0118 5.8014 0.1109 191.7515 INTANG 0.0148 0.0049 0.0336 0.0000 0.4988 MB 1.7279 1.2800 3.0255 0.0700 192.9900 DY 3.6879 3.4300 3.4987 0.0000 47.4700 RD 0.0266 0.0109 0.0440 0.0000 0.6424

19

Table 4. The Effects of Firm Location on Management Team Size

This table includes two panels, panel A and panel B. Panel A shows the results of ten different panel regression model with the variables of management team size (TSIZE) as the dependent variable against various explanatory variables of distance-related geography characteristics variables (DIST, DIST_TPE, DIST_MTRA, DIST_TRA, DIST_HSR, DIST_NAirport, DIST_Airport, Metro, Five, and North) using data of 9,040 annual firm observations from year 2006 to 2012. Panel B shows the results of five different panel regression model with the variables of management team size (TSIZE) as the dependent variable against various explanatory variables of non-distance related geography characteristics variables (IND_D, Bank_D, PD, BS, and PI). The fixed effects (industry and year) and heteroskedasticity issues are considered in these results. This table presents the model coefficients and Adjusted R-squared. The t-statistics calculated by heteroskedasticity-consistent standard errors for each coefficient appears immediately underneath. The signs of “*, **, ***” represent the significance of 10%, 5%, and 1%, respectively.

Panel A. The effects of distance-related geography characteristics variables on management team size (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) TSIZE TSIZE TSIZE TSIZE TSIZE TSIZE TSIZE TSIZE TSIZE TSIZE DIST -0.0358***

(-5.15)

DIST_TPE

-0.0013** (-2.46)

Metro

1.1245*** (7.97)

DIST_MTRA

-0.0474*** (-6.07)

DIST_TRA

-0.0056 (-0.38)

DIST_HSR

-0.0278*** (-3.96)

DIST_Airport

-0.0205*** (-7.76)

DIST_NAirport

-0.0011* (-1.87)

Five

1.0133*** (7.30)

North

0.1404 (1.00)

Constant 9.5446*** (50.10)

9.3219*** (51.09)

8.7262*** (49.39)

9.5798*** (50.98)

9.2334*** (49.09)

9.5299*** (47.72)

9.8218*** (49.70)

9.2900*** (51.18)

8.5055*** (44.85)

9.1001*** (45.74)

Observations 9040 9040 9040 9040 9040 7402 9040 9040 9040 9040 R2 0.0030 0.0014 0.0084 0.0037 0.0008 0.0020 0.0067 0.0011 0.0061 0.0009

20

Table 4. The Effects of Firm Location on Management Team Size (Cont.)

Panel B. The effects of non-distance related geography characteristics variables on management team size Industry density (IND_D) is the number of firms per unit squared kilometer. The Bank_D variable is the percentage of banks in the same three-digit ZIP radius of the firm’s headquarters. Population density (PD) is the population density of the county where the firm is headquartered (in ten thousands). Bachelor's degree (BS) is the percent of total labor forces people who have completed a Bachelor's degree in the county. The PI variable is the personal income of the county where the firm is headquartered (in ten thousands). (1) (2) (3) (4) (5) TSIZE TSIZE TSIZE TSIZE TSIZE BS 3.0325***

(5.30)

PI

0.0672*** (7.33)

PD

1.6527*** (7.99)

IND_D

0.0025*** (7.88)

Bank_D

20.0956*** (3.83)

Constant 8.2736*** (34.54)

6.5525*** (17.05)

8.5112*** (46.18)

8.6518*** (48.40)

8.8594*** (46.80)

Observations 9040 9040 9040 9040 9040 R2 0.0037 0.0079 0.0101 0.0102 0.0029

21

Table 5. The Effects of Firm Location on Management Team Reputation (Measured by Board variable)

This table includes two panels, panel A and panel B. Panel A shows the results of ten different panel regression model with the variables of management team reputation (Board) as the dependent variable against various explanatory variables of distance-related geography characteristics variables (DIST, DIST_TPE, DIST_MTRA, DIST_TRA, DIST_HSR, DIST_NAirport, DIST_Airport, Metro, Five, and North) using data of 9,040 annual firm observations from year 2006 to 2012. Panel B shows the results of five different panel regression model with the variables of management team reputation (Board) as the dependent variable against various explanatory variables of non-distance related geography characteristics variables (IND_D, Bank_D, PD, BS, and PI). The fixed effects (industry and year) and heteroskedasticity issues are considered in these results. This table presents the model coefficients and Adjusted R-squared. The t-statistics calculated by heteroskedasticity-consistent standard errors for each coefficient appears immediately underneath. The signs of “*, **, ***” represent the significance of 10%, 5%, and 1%, respectively.

Panel A. The effects of distance-related geography characteristics variables on management team reputation (measured by Board variable) (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Board Board Board Board Board Board Board Board Board Board DIST -0.0022***

(-7.53)

DIST_TPE

-0.0001*** (-3.78)

Metro

0.0322*** (6.06)

DIST_MTRA

-0.0023*** (-6.78)

DIST_TRA

-0.0035*** (-6.01)

DIST_HSR

-0.0014*** (-4.90)

DIST_Airport

-0.0006*** (-5.59)

DIST_NAirport

-0.0001*** (-3.52)

Five

0.0235*** (4.19)

North

0.0330*** (5.20)

Constant 0.2901*** (39.15)

0.2773*** (38.85)

0.2557*** (35.57)

0.2874*** (39.09)

0.2862*** (38.29)

0.2870*** (37.35)

0.2878*** (37.91)

0.2766*** (38.84)

0.2533*** (32.24)

0.2445*** (29.29)

Observations 9036 9036 9036 9036 9036 7400 9036 9036 9036 9036 R2 0.0105 0.0068 0.0093 0.0097 0.0084 0.0064 0.0087 0.0066 0.0071 0.0082

22

Table 5. The Effects of Firm Location on Management Team Reputation (Measured by

Board variable) (Cont.)

Panel B. The effects of non-distance related geography characteristics variables on management team reputation (measured by Board variable) Industry density (IND_D) is the number of firms per unit squared kilometer. The Bank_D variable is the percentage of banks in the same three-digit ZIP radius of the firm’s headquarters. Population density (PD) is the population density of the county where the firm is headquartered (in ten thousands). Bachelor's degree (BS) is the percent of total labor forces people who have completed a Bachelor's degree in the county. The PI variable is the personal income of the county where the firm is headquartered (in ten thousands). (1) (2) (3) (4) (5) Board Board Board Board Board BS 0.1240***

(5.47)

PI

0.0024*** (7.37)

PD

0.0452*** (6.35)

IND_D

0.0001*** (6.10)

Bank_D

1.0787*** (5.87)

Constant 0.2314*** (23.77)

0.1741*** (11.89)

0.2505*** (33.55)

0.2550*** (35.21)

0.2509*** (33.60)

Observations 9036 9036 9036 9036 9036 R2 0.0085 0.0114 0.0098 0.0095 0.0092

23

Table 6. The Effects of Firm Location on Management Team Reputation (Measured by BNP variable)

This table includes two panels, panel A and panel B. Panel A shows the results of ten different panel regression model with the variables of management team reputation (BNP) as the dependent variable against various explanatory variables of distance-related geography characteristics variables (DIST, DIST_TPE, DIST_MTRA, DIST_TRA, DIST_HSR, DIST_NAirport, DIST_Airport, Metro, Five, and North) using data of 9,040 annual firm observations from year 2006 to 2012. Panel B shows the results of five different panel regression model with the variables of management team reputation (BNP) as the dependent variable against various explanatory variables of non-distance related geography characteristics variables (IND_D, Bank_D, PD, BS, and PI). The fixed effects (industry and year) and heteroskedasticity issues are considered in these results. This table presents the model coefficients and Adjusted R-squared. The t-statistics calculated by heteroskedasticity-consistent standard errors for each coefficient appears immediately underneath. The signs of “*, **, ***” represent the significance of 10%, 5%, and 1%, respectively.

Panel A. The effects of distance-related geography characteristics variables on management team reputation (measured by BNP variable) (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) BNP BNP BNP BNP BNP BNP BNP BNP BNP BNP DIST -0.0002***

(-3.20)

DIST_TPE

-0.0000*** (-3.02)

Metro

-0.0087*** (-4.91)

DIST_MTRA

-0.0001 (-1.06)

DIST_TRA

-0.0007*** (-4.07)

DIST_HSR

-0.0002 (-1.38)

DIST_Airport

0.0003*** (5.83)

DIST_NAirport

-0.0000*** (-4.55)

Five

-0.0211*** (-7.83)

North

0.0102*** (5.28)

Constant 0.0189*** (7.97)

0.0180*** (7.93)

0.0201*** (8.27)

0.0173*** (7.51)

0.0198*** (8.33)

0.0185*** (7.40)

0.0086*** (3.57)

0.0188*** (8.18)

0.0310*** (9.82)

0.0089*** (3.59)

Observations 8219 8219 8219 8219 8219 6732 8219 8219 8219 8219 R2 0.0008 0.0008 0.0029 0.0004 0.0013 0.0008 0.0057 0.0014 0.0135 0.0028

24

Table 6. The Effects of Firm Location on Management Team Reputation (Measured by

BNP variable) (Cont.)

Panel B. The effects of non-distance related geography characteristics variables on management team reputation (measured by BNP variable) Industry density (IND_D) is the number of firms per unit squared kilometer. The Bank_D variable is the percentage of banks in the same three-digit ZIP radius of the firm’s headquarters. Population density (PD) is the population density of the county where the firm is headquartered (in ten thousands). Bachelor's degree (BS) is the percent of total labor forces people who have completed a Bachelor's degree in the county. The PI variable is the personal income of the county where the firm is headquartered (in ten thousands). (1) (2) (3) (4) (5) BNP BNP BNP BNP BNP BS 0.0457***

(4.81)

PI

0.0003*** (3.17)

PD

-0.0029 (-1.21)

IND_D

-0.0000* (-1.92)

Bank_D

0.2192*** (3.31)

Constant 0.0026 (0.79)

0.0050 (1.19)

0.0177*** (7.21)

0.0179*** (7.51)

0.0129*** (5.48)

Observations 8219 8219 8219 8219 8219 R2 0.0041 0.0011 0.0005 0.0007 0.0017

25

Table 7. The Effects of Firm Location on Management Team Size When Controlling for Other Determinant Variables This table includes two panels, panel A and panel B. Panel A shows the results of ten different panel regression model with the variables of management team size (TSIZE) as the dependent variable against various explanatory variables of distance-related geography characteristics variables (DIST, DIST_TPE, DIST_MTRA, DIST_TRA, DIST_HSR, DIST_NAirport, DIST_Airport, Metro, Five, and North) using data of 9,040 annual firm observations from year 2006 to 2012. Panel B shows the results of five different panel regression model with the variables of management team size (TSIZE) as the dependent variable against various explanatory variables of non-distance related geography characteristics variables (IND_D, Bank_D, PD, BS, and PI). ROA is the ratio of net income to book value of assets. ROAV is the volatility of ROA. LEV is financial leverage, defined as the book value of debt divided by total asset market value. Fage is the age of firm. INTANG is defined as intangible assets divided by total assets. MB is the market-to-book ratio, defined as the market value of equity divided by the book value of equity. LNAT is the natural logarithm of the book value of the firm’s assets at the end of the fiscal year. R&D intensity (RD) is defined as R&D related expense divided by total asset. OCF is used by operating cash flow divided by total asset. Dividend yield (DY) is dividend yield. INST and ODIR are the institutional investors’ holdings and the number of outside directors, respectively.. The fixed effects (industry and year) and heteroskedasticity issues are considered in these results. This table presents the model coefficients and Adjusted R-squared. The t-statistics calculated by heteroskedasticity-consistent standard errors for each coefficient appears immediately underneath. The signs of “*, **, ***” represent the significance of 10%, 5%, and 1%, respectively.

Panel A. The effects of distance-related geography characteristics variables on management team size (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) TSIZE TSIZE TSIZE TSIZE TSIZE TSIZE TSIZE TSIZE TSIZE TSIZE DIST -0.0194***

(-3.05)

DIST_TPE

-0.0012** (-2.36)

Metro

0.9309*** (7.48)

DIST_MTRA

-0.0405*** (-6.00)

DIST_TRA

-0.0360*** (-2.65)

DIST_HSR

-0.0365*** (-5.41)

DIST_Airport

-0.0159*** (-6.63)

DIST_NAirport

-0.0009* (-1.67)

Five

1.0835*** (8.75)

North

0.1260 (0.95)

26

Fage -0.0048 (-0.70)

-0.0042 (-0.62)

-0.0093 (-1.36)

-0.0068 (-1.00)

-0.0044 (-0.65)

0.0012 (0.16)

-0.0106 (-1.54)

-0.0037 (-0.55)

-0.0103 (-1.50)

-0.0033 (-0.49)

ODIR 0.0313 (0.83)

0.0318 (0.85)

0.0543 (1.44)

0.0334 (0.89)

0.0290 (0.77)

0.0333 (0.80)

0.0421 (1.12)

0.0305 (0.81)

0.0495 (1.33)

0.0295 (0.79)

OCF -0.0743 (-0.15)

-0.0662 (-0.14)

0.1725 (0.36)

-0.0337 (-0.07)

-0.0996 (-0.21)

0.0127 (0.02)

0.1077 (0.23)

-0.0837 (-0.17)

0.1793 (0.38)

-0.0981 (-0.20)

LNTA 1.9448*** (27.39)

1.9477*** (27.47)

1.9537*** (27.50)

1.9465*** (27.36)

1.9547*** (27.48)

1.9550*** (25.16)

1.9516*** (27.44)

1.9482*** (27.48)

1.9742*** (27.61)

1.9501*** (27.48)

INST 0.4373 (1.41)

0.4847 (1.55)

0.2902 (0.94)

0.4089 (1.31)

0.4402 (1.41)

0.4325 (1.23)

0.3842 (1.24)

0.4810 (1.54)

0.3694 (1.19)

0.4706 (1.51)

LEV -1.6600*** (-4.42)

-1.6266*** (-4.31)

-1.3966*** (-3.77)

-1.5765*** (-4.20)

-1.6900*** (-4.50)

-1.7796*** (-4.15)

-1.5300*** (-4.11)

-1.6626*** (-4.40)

-1.5819*** (-4.25)

-1.6947*** (-4.47)

ROA -0.0223*** (-3.89)

-0.0222*** (-3.89)

-0.0210*** (-3.62)

-0.0219*** (-3.81)

-0.0225*** (-3.94)

-0.0220*** (-3.55)

-0.0222*** (-3.84)

-0.0223*** (-3.91)

-0.0226*** (-3.97)

-0.0224*** (-3.93)

ROAV -0.0436*** (-4.46)

-0.0431*** (-4.40)

-0.0431*** (-4.39)

-0.0436*** (-4.45)

-0.0427*** (-4.38)

-0.0429*** (-4.13)

-0.0446*** (-4.55)

-0.0430*** (-4.40)

-0.0433*** (-4.44)

-0.0427*** (-4.37)

INTANG 15.2542*** (6.21)

14.9604*** (6.14)

14.3519*** (5.96)

15.6337*** (6.36)

15.2424*** (6.22)

15.9855*** (6.17)

14.7989*** (6.14)

14.9615*** (6.13)

14.0703*** (5.85)

14.9186*** (6.11)

MB 0.0530*** (2.63)

0.0521** (2.58)

0.0485** (2.51)

0.0516*** (2.58)

0.0531** (2.56)

0.0495** (2.55)

0.0494*** (2.63)

0.0525*** (2.58)

0.0476*** (2.60)

0.0529** (2.58)

DY 0.0532*** (2.73)

0.0552*** (2.83)

0.0509*** (2.63)

0.0529*** (2.71)

0.0562*** (2.88)

0.0598*** (2.77)

0.0520*** (2.67)

0.0551*** (2.82)

0.0495** (2.57)

0.0548*** (2.81)

RD 2.0826 (1.58)

2.4076* (1.84)

3.0720** (2.38)

1.9643 (1.49)

2.2500* (1.71)

1.1435 (0.81)

3.5389*** (2.74)

2.3712* (1.81)

3.8213*** (2.98)

2.3397* (1.79)

Constant -20.2181*** (-20.53)

-20.4004*** (-20.77)

-20.9542*** (-21.05)

-20.0746*** (-20.36)

-20.3965*** (-20.63)

-20.5442*** (-14.27)

-19.9570*** (-20.33)

-20.4377*** (-20.81)

-21.5313*** (-21.46)

-20.6264*** (-20.57)

Observations 8844 8844 8844 8844 8844 7255 8844 8844 8844 8844 R2 0.1972 0.1970 0.2014 0.1986 0.1971 0.1979 0.1998 0.1968 0.2022 0.1966

27

Table 7. The Effects of Firm Location on Management Team Size When Controlling for

Other Determinant Variables (Cont.)

Panel B. The effects of non-distance related geography characteristics variables on management team size Industry density (IND_D) is the number of firms per unit squared kilometer. The Bank_D variable is the percentage of banks in the same three-digit ZIP radius of the firm’s headquarters. Population density (PD) is the population density of the county where the firm is headquartered (in ten thousands). Bachelor's degree (BS) is the percent of total labor forces people who have completed a Bachelor's degree in the county. The PI variable is the personal income of the county where the firm is headquartered (in ten thousands). (1) (2) (3) (4) (5) TSIZE TSIZE TSIZE TSIZE TSIZE BS 0.5770

(1.10)

PI

0.0393*** (4.89)

PD

1.0449*** (5.80)

IND_D

0.0016*** (5.90)

Bank_D

2.3615 (0.50)

Fage -0.0031 (-0.46)

-0.0065 (-0.95)

-0.0081 (-1.19)

-0.0084 (-1.24)

-0.0035 (-0.52)

ODIR 0.0283 (0.75)

0.0379 (1.01)

0.0414 (1.10)

0.0427 (1.14)

0.0294 (0.78)

OCF -0.1097 (-0.23)

-0.0018 (-0.00)

0.0711 (0.15)

0.0878 (0.18)

-0.1022 (-0.21)

LNTA 1.9457*** (27.43)

1.9374*** (27.40)

1.9381*** (27.43)

1.9407*** (27.45)

1.9491*** (27.50)

INST 0.4449 (1.43)

0.3354 (1.09)

0.2995 (0.97)

0.2880 (0.94)

0.4579 (1.48)

LEV -1.7136*** (-4.59)

-1.4869*** (-4.04)

-1.4510*** (-3.95)

-1.4604*** (-3.98)

-1.7221*** (-4.63)

ROA -0.0222*** (-3.90)

-0.0214*** (-3.74)

-0.0209*** (-3.63)

-0.0209*** (-3.63)

-0.0224*** (-3.94)

ROAV -0.0429*** (-4.39)

-0.0431*** (-4.40)

-0.0430*** (-4.39)

-0.0430*** (-4.40)

-0.0429*** (-4.39)

INTANG 14.8344*** (6.08)

14.5822*** (6.02)

14.2521*** (5.91)

14.2335*** (5.91)

14.8749*** (6.12)

MB 0.0528** (2.56)

0.0496** (2.47)

0.0483** (2.44)

0.0480** (2.44)

0.0530** (2.57)

DY 0.0554*** (2.84)

0.0558*** (2.87)

0.0556*** (2.86)

0.0552*** (2.84)

0.0548*** (2.81)

RD 2.1078 (1.56)

1.9414 (1.47)

2.4413* (1.87)

2.7838** (2.14)

2.3255* (1.75)

Constant -20.6198*** (-20.75)

-21.8196*** (-20.86)

-20.6997*** (-20.92)

-20.6502*** (-20.89)

-20.5361*** (-20.78)

Observations 8844 8844 8844 8844 8844 R2 0.1967 0.1988 0.2000 0.2003 0.1966

28

Table 8. The Effects of Firm Location on Management Team Reputation (Board) When Controlling for Other Determinant Variables

This table includes two panels, panel A and panel B. Panel A shows the results of ten different panel regression model with the variables of management team reputation (Board) as the dependent variable against various explanatory variables of distance-related geography characteristics variables (DIST, DIST_TPE, DIST_MTRA, DIST_TRA, DIST_HSR, DIST_NAirport, DIST_Airport, Metro, Five, and North) using data of 9,040 annual firm observations from year 2006 to 2012. Panel B shows the results of five different panel regression model with the variables of management team reputation (Board) as the dependent variable against various explanatory variables of non-distance related geography characteristics variables (IND_D, Bank_D, PD, BS, and PI). ROA is the ratio of net income to book value of assets. ROAV is the volatility of ROA. LEV is financial leverage, defined as the book value of debt divided by total asset market value. Fage is the age of firm. INTANG is defined as intangible assets divided by total assets. MB is the market-to-book ratio, defined as the market value of equity divided by the book value of equity. LNAT is the natural logarithm of the book value of the firm’s assets at the end of the fiscal year. R&D intensity (RD) is defined as R&D related expense divided by total asset. OCF is used by operating cash flow divided by total asset. Dividend yield (DY) is dividend yield. INST and ODIR are the institutional investors’ holdings and the number of outside directors, respectively.. The fixed effects (industry and year) and heteroskedasticity issues are considered in these results. This table presents the model coefficients and Adjusted R-squared. The t-statistics calculated by heteroskedasticity-consistent standard errors for each coefficient appears immediately underneath. The signs of “*, **, ***” represent the significance of 10%, 5%, and 1%, respectively.

Panel A. The effects of distance-related geography characteristics variables on management team reputation (Board) (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Board Board Board Board Board Board Board Board Board Board DIST -0.0015***

(-5.14)

DIST_TPE

-0.0001** (-2.19)

Metro

0.0217*** (4.09)

DIST_MTRA

-0.0016*** (-4.92)

DIST_TRA

-0.0033*** (-5.80)

DIST_HSR

-0.0013*** (-4.34)

DIST_Airport

-0.0004*** (-3.25)

DIST_NAirport

-0.0001** (-1.98)

Five

0.0192*** (3.42)

North

0.0260*** (4.09)

29

Fage 0.0010*** (3.81)

0.0011*** (4.13)

0.0010*** (3.80)

0.0010*** (3.74)

0.0010*** (3.83)

0.0008*** (2.76)

0.0010*** (3.64)

0.0011*** (4.17)

0.0010*** (3.83)

0.0011*** (4.10)

ODIR -0.0077*** (-4.52)

-0.0077*** (-4.56)

-0.0073*** (-4.29)

-0.0077*** (-4.54)

-0.0078*** (-4.62)

-0.0075*** (-4.04)

-0.0076*** (-4.47)

-0.0078*** (-4.58)

-0.0075*** (-4.42)

-0.0077*** (-4.52)

OCF -0.0011 (-0.04)

-0.0018 (-0.07)

0.0029 (0.12)

-0.0006 (-0.03)

-0.0030 (-0.12)

0.0000 (0.00)

0.0013 (0.05)

-0.0023 (-0.09)

0.0015 (0.06)

-0.0018 (-0.07)

LNTA 0.0414*** (18.40)

0.0417*** (18.51)

0.0419*** (18.68)

0.0417*** (18.53)

0.0422*** (18.76)

0.0423*** (17.06)

0.0419*** (18.60)

0.0417*** (18.50)

0.0423*** (18.85)

0.0417*** (18.55)

INST -0.0432*** (-3.29)

-0.0401*** (-3.06)

-0.0449*** (-3.43)

-0.0432*** (-3.30)

-0.0435*** (-3.33)

-0.0505*** (-3.44)

-0.0427*** (-3.26)

-0.0401*** (-3.06)

-0.0425*** (-3.25)

-0.0407*** (-3.11)

LEV -0.0723*** (-4.64)

-0.0734*** (-4.65)

-0.0704*** (-4.51)

-0.0718*** (-4.61)

-0.0738*** (-4.75)

-0.0571*** (-3.23)

-0.0736*** (-4.72)

-0.0740*** (-4.69)

-0.0756*** (-4.88)

-0.0690*** (-4.38)

ROA -0.0006* (-1.84)

-0.0006* (-1.87)

-0.0005* (-1.77)

-0.0006* (-1.82)

-0.0006* (-1.92)

-0.0006* (-1.81)

-0.0006* (-1.87)

-0.0006* (-1.88)

-0.0006* (-1.92)

-0.0006* (-1.88)

ROAV -0.0010** (-2.18)

-0.0010** (-2.07)

-0.0010** (-2.05)

-0.0010** (-2.11)

-0.0010** (-2.04)

-0.0008 (-1.63)

-0.0010** (-2.13)

-0.0010** (-2.07)

-0.0010** (-2.07)

-0.0010** (-2.00)

INTANG -0.0352 (-0.56)

-0.0599 (-0.94)

-0.0754 (-1.19)

-0.0331 (-0.53)

-0.0311 (-0.50)

-0.0476 (-0.71)

-0.0649 (-1.03)

-0.0590 (-0.93)

-0.0773 (-1.22)

-0.0579 (-0.91)

MB -0.0001 (-0.16)

-0.0001 (-0.22)

-0.0002 (-0.33)

-0.0001 (-0.25)

-0.0001 (-0.15)

-0.0001 (-0.24)

-0.0002 (-0.30)

-0.0001 (-0.21)

-0.0002 (-0.32)

-0.0001 (-0.23)

DY 0.0005 (0.54)

0.0006 (0.70)

0.0005 (0.57)

0.0005 (0.59)

0.0007 (0.84)

0.0001 (0.14)

0.0005 (0.60)

0.0006 (0.70)

0.0005 (0.57)

0.0006 (0.70)

RD 0.2442*** (3.80)

0.2691*** (4.18)

0.2845*** (4.41)

0.2513*** (3.91)

0.2546*** (3.97)

0.2591*** (3.73)

0.2948*** (4.54)

0.2670*** (4.15)

0.2941*** (4.54)

0.2548*** (3.95)

Constant -0.3195*** (-9.51)

-0.3375*** (-10.04)

-0.3525*** (-10.58)

-0.3252*** (-9.75)

-0.3309*** (-9.92)

-0.3524*** (-5.82)

-0.3303*** (-9.84)

-0.3379*** (-10.05)

-0.3613*** (-10.73)

-0.3623*** (-10.84)

Observations 8840 8840 8840 8840 8840 7253 8840 8840 8840 8840 R2 0.0684 0.0667 0.0679 0.0683 0.0689 0.0631 0.0672 0.0666 0.0673 0.0679

30

Table 8. The Effects of Firm Location on Management Team Reputation (Board) When

Controlling for Other Determinant Variables (Cont.)

Panel B. The effects of non-distance related geography characteristics variables on management team reputation (Board) Industry density (IND_D) is the number of firms per unit squared kilometer. The Bank_D variable is the percentage of banks in the same three-digit ZIP radius of the firm’s headquarters. Population density (PD) is the population density of the county where the firm is headquartered (in ten thousands). Bachelor's degree (BS) is the percent of total labor forces people who have completed a Bachelor's degree in the county. The PI variable is the personal income of the county where the firm is headquartered (in ten thousands). (1) (2) (3) (4) (5) Board Board Board Board Board BS 0.0708***

(3.10)

PI

0.0015*** (4.57)

PD

0.0244*** (3.43)

IND_D

0.0000*** (3.27)

Bank_D

0.4413** (2.41)

Fage 0.0011*** (4.28)

0.0010*** (3.84)

0.0010*** (3.88)

0.0010*** (3.89)

0.0011*** (3.95)

ODIR -0.0079*** (-4.66)

-0.0075*** (-4.45)

-0.0076*** (-4.47)

-0.0076*** (-4.46)

-0.0077*** (-4.54)

OCF -0.0039 (-0.16)

0.0004 (0.02)

0.0006 (0.02)

0.0006 (0.03)

-0.0027 (-0.11)

LNTA 0.0412*** (18.25)

0.0413*** (18.36)

0.0415*** (18.46)

0.0416*** (18.50)

0.0415*** (18.46)

INST -0.0438*** (-3.35)

-0.0459*** (-3.50)

-0.0447*** (-3.41)

-0.0447*** (-3.40)

-0.0430*** (-3.29)

LEV -0.0751*** (-4.84)

-0.0687*** (-4.41)

-0.0716*** (-4.60)

-0.0724*** (-4.65)

-0.0750*** (-4.81)

ROA -0.0006* (-1.81)

-0.0005* (-1.77)

-0.0005* (-1.78)

-0.0005* (-1.79)

-0.0006* (-1.85)

ROAV -0.0010** (-2.08)

-0.0010** (-2.08)

-0.0010** (-2.06)

-0.0010** (-2.06)

-0.0010** (-2.12)

INTANG -0.0701 (-1.10)

-0.0747 (-1.18)

-0.0777 (-1.22)

-0.0770 (-1.21)

-0.0664 (-1.04)

MB -0.0001 (-0.22)

-0.0002 (-0.38)

-0.0002 (-0.34)

-0.0002 (-0.34)

-0.0001 (-0.20)

DY 0.0007 (0.78)

0.0006 (0.72)

0.0006 (0.69)

0.0006 (0.68)

0.0006 (0.70)

RD 0.2321*** (3.57)

0.2512*** (3.90)

0.2699*** (4.18)

0.2772*** (4.30)

0.2535*** (3.92)

Constant -0.3544*** (-10.60)

-0.3918*** (-11.25)

-0.3474*** (-10.44)

-0.3464*** (-10.41)

-0.3440*** (-10.33)

Observations 8840 8840 8840 8840 8840 R2 0.0671 0.0684 0.0674 0.0673 0.0668

31

Table 9. The Effects of Firm Location on Management Team Reputation (BNP) When Controlling for Other Determinant Variables

This table includes two panels, panel A and panel B. Panel A shows the results of ten different panel regression model with the variables of management team reputation (BNP) as the dependent variable against various explanatory variables of distance-related geography characteristics variables (DIST, DIST_TPE, DIST_MTRA, DIST_TRA, DIST_HSR, DIST_NAirport, DIST_Airport, Metro, Five, and North) using data of 9,040 annual firm observations from year 2006 to 2012. Panel B shows the results of five different panel regression model with the variables of management team reputation (BNP) as the dependent variable against various explanatory variables of non-distance related geography characteristics variables (IND_D, Bank_D, PD, BS, and PI). ROA is the ratio of net income to book value of assets. ROAV is the volatility of ROA. LEV is financial leverage, defined as the book value of debt divided by total asset market value. Fage is the age of firm. INTANG is defined as intangible assets divided by total assets. MB is the market-to-book ratio, defined as the market value of equity divided by the book value of equity. LNAT is the natural logarithm of the book value of the firm’s assets at the end of the fiscal year. R&D intensity (RD) is defined as R&D related expense divided by total asset. OCF is used by operating cash flow divided by total asset. Dividend yield (DY) is dividend yield. INST and ODIR are the institutional investors’ holdings and the number of outside directors, respectively.. The fixed effects (industry and year) and heteroskedasticity issues are considered in these results. This table presents the model coefficients and Adjusted R-squared. The t-statistics calculated by heteroskedasticity-consistent standard errors for each coefficient appears immediately underneath. The signs of “*, **, ***” represent the significance of 10%, 5%, and 1%, respectively.

Panel A. The effects of distance-related geography characteristics variables on management team reputation (BNP) (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) BNP BNP BNP BNP BNP BNP BNP BNP BNP BNP DIST -0.0001*

(-1.75)

DIST_TPE

-0.0000** (-2.14)

Metro

-0.0096*** (-5.62)

DIST_MTRA

0.0000 (0.02)

DIST_TRA

-0.0005*** (-3.10)

DIST_HSR

-0.0001 (-0.43)

DIST_Airport

0.0002*** (5.85)

DIST_NAirport

-0.0000*** (-3.48)

Five

-0.0196*** (-7.97)

North

0.0072*** (3.84)

32

Fage -0.0001 (-1.08)

-0.0001 (-1.09)

-0.0000 (-0.38)

-0.0001 (-0.96)

-0.0001 (-1.16)

-0.0001 (-1.15)

0.0000 (0.24)

-0.0001 (-1.15)

0.0000 (0.32)

-0.0001 (-1.15)

ODIR -0.0007 (-1.61)

-0.0007 (-1.59)

-0.0010** (-2.25)

-0.0007* (-1.65)

-0.0007 (-1.64)

-0.0004 (-0.82)

-0.0009** (-2.10)

-0.0007 (-1.56)

-0.0011** (-2.54)

-0.0007 (-1.53)

OCF -0.0086 (-0.97)

-0.0084 (-0.94)

-0.0120 (-1.34)

-0.0088 (-1.00)

-0.0088 (-0.99)

-0.0115 (-1.22)

-0.0123 (-1.36)

-0.0082 (-0.92)

-0.0142 (-1.60)

-0.0082 (-0.92)

LNTA 0.0011* (1.73)

0.0011* (1.71)

0.0012* (1.84)

0.0012* (1.79)

0.0012* (1.91)

0.0017** (2.37)

0.0012* (1.81)

0.0011* (1.66)

0.0008 (1.23)

0.0011* (1.69)

INST 0.0074 (1.44)

0.0078 (1.52)

0.0093* (1.81)

0.0076 (1.49)

0.0073 (1.44)

0.0057 (1.01)

0.0087* (1.69)

0.0079 (1.55)

0.0094* (1.84)

0.0078 (1.52)

LEV -0.0133** (-2.52)

-0.0126** (-2.37)

-0.0179*** (-3.33)

-0.0139*** (-2.64)

-0.0132** (-2.45)

-0.0172*** (-2.85)

-0.0173*** (-3.26)

-0.0119** (-2.23)

-0.0168*** (-3.15)

-0.0109** (-2.03)

ROA -0.0002** (-2.01)

-0.0002** (-2.00)

-0.0002** (-2.17)

-0.0002** (-2.02)

-0.0002** (-2.02)

-0.0002** (-2.16)

-0.0002** (-2.07)

-0.0002** (-1.99)

-0.0002** (-2.02)

-0.0002** (-1.99)

ROAV -0.0003** (-2.31)

-0.0003** (-2.29)

-0.0003** (-2.25)

-0.0003** (-2.28)

-0.0003** (-2.27)

-0.0003 (-1.64)

-0.0003** (-2.09)

-0.0003** (-2.30)

-0.0003** (-2.21)

-0.0003** (-2.22)

INTANG 0.0012 (0.05)

-0.0009 (-0.04)

0.0041 (0.18)

-0.0018 (-0.08)

0.0036 (0.15)

0.0084 (0.34)

-0.0004 (-0.02)

0.0002 (0.01)

0.0144 (0.64)

0.0000 (0.00)

MB 0.0001 (0.58)

0.0001 (0.53)

0.0002 (0.74)

0.0001 (0.57)

0.0001 (0.57)

0.0001 (0.48)

0.0002 (0.75)

0.0001 (0.51)

0.0002 (0.90)

0.0001 (0.51)

DY 0.0001 (0.35)

0.0001 (0.42)

0.0001 (0.50)

0.0001 (0.39)

0.0001 (0.47)

0.0002 (0.66)

0.0001 (0.55)

0.0001 (0.45)

0.0002 (0.74)

0.0001 (0.45)

RD 0.2022*** (5.67)

0.2046*** (5.75)

0.1983*** (5.65)

0.2046*** (5.74)

0.2022*** (5.68)

0.2125*** (5.61)

0.1890*** (5.39)

0.2038*** (5.72)

0.1804*** (5.19)

0.2011*** (5.64)

Constant 0.0042 (0.43)

0.0035 (0.36)

0.0054 (0.56)

0.0020 (0.21)

0.0036 (0.37)

-0.0018 (-0.11)

-0.0070 (-0.71)

0.0045 (0.46)

0.0198** (1.96)

-0.0029 (-0.31)

Observations 8041 8041 8041 8041 8041 6599 8041 8041 8041 8041 R2 0.0146 0.0147 0.0177 0.0144 0.0150 0.0182 0.0191 0.0151 0.0266 0.0158

33

Table 9. The Effects of Firm Location on Management Team Reputation (BNP) When

Controlling for Other Determinant Variables (Cont.)

Panel B. The effects of non-distance related geography characteristics variables on management team reputation (BNP) Industry density (IND_D) is the number of firms per unit squared kilometer. The Bank_D variable is the percentage of banks in the same three-digit ZIP radius of the firm’s headquarters. Population density (PD) is the population density of the county where the firm is headquartered (in ten thousands). Bachelor's degree (BS) is the percent of total labor forces people who have completed a Bachelor's degree in the county. The PI variable is the personal income of the county where the firm is headquartered (in ten thousands). (1) (2) (3) (4) (5) BNP BNP BNP BNP BNP BS 0.0312***

(3.21)

PI

0.0001 (1.55)

PD

-0.0046** (-2.15)

IND_D

-0.0000*** (-2.68)

Bank_D

0.1662*** (2.75)

Fage -0.0001 (-1.03)

-0.0001 (-1.08)

-0.0001 (-0.76)

-0.0001 (-0.71)

-0.0001 (-1.38)

ODIR -0.0007* (-1.65)

-0.0007 (-1.56)

-0.0008* (-1.78)

-0.0008* (-1.83)

-0.0007 (-1.52)

OCF -0.0088 (-0.99)

-0.0084 (-0.94)

-0.0098 (-1.10)

-0.0100 (-1.12)

-0.0083 (-0.93)

LNTA 0.0009 (1.40)

0.0011* (1.70)

0.0012* (1.90)

0.0012* (1.90)

0.0010 (1.60)

INST 0.0063 (1.22)

0.0071 (1.41)

0.0082 (1.61)

0.0084* (1.65)

0.0069 (1.34)

LEV -0.0125** (-2.38)

-0.0129** (-2.44)

-0.0152*** (-2.89)

-0.0154*** (-2.90)

-0.0126** (-2.43)

ROA -0.0002* (-1.89)

-0.0002** (-1.99)

-0.0002** (-2.09)

-0.0002** (-2.10)

-0.0002* (-1.95)

ROAV -0.0003** (-2.34)

-0.0003** (-2.29)

-0.0003** (-2.27)

-0.0003** (-2.27)

-0.0003** (-2.38)

INTANG -0.0036 (-0.15)

-0.0026 (-0.11)

0.0008 (0.03)

0.0014 (0.06)

-0.0020 (-0.09)

MB 0.0001 (0.48)

0.0001 (0.52)

0.0001 (0.66)

0.0001 (0.68)

0.0001 (0.53)

DY 0.0002 (0.58)

0.0001 (0.42)

0.0001 (0.35)

0.0001 (0.35)

0.0001 (0.44)

RD 0.1887*** (5.21)

0.2029*** (5.69)

0.2046*** (5.76)

0.2029*** (5.73)

0.1988*** (5.57)

Constant -0.0029 (-0.29)

-0.0024 (-0.24)

0.0027 (0.28)

0.0026 (0.27)

0.0021 (0.22)

Observations 8041 8041 8041 8041 8041 R2 0.0163 0.0146 0.0149 0.0151 0.0153