1. This study examines the relationship between a firm's headquarter location characteristics and its management team size and reputation (MS&R) using data from Taiwanese firms from 2006-2012.
2. The results show that firms located farther from urban centers, major transportation hubs like railway stations and airports, have smaller management teams and poorer reputations, as measured by the percentage of corporate and non-profit boards that managers sit on.
3. The study also finds that firms located in bigger cities have an advantage over those in northern cities in attracting general managers, but the opposite is true for high-reputation managers.
Firm Headquarter Location and Management Team Reputation_TW_20131031_20140113
1. 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
2. 2
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.
3. 3
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.
4. 4
(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.
5. 5
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.
6. 6
[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
7. 7
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.
8. 8
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,
9. 9
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
10. 10
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,
11. 11
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]
12. 12
[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
13. 13
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.
14. 14
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.
16. 16
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
17. 17
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. 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. 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. 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. 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. 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. 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. 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. 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)
27. 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. 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)
30. 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. 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)
33. 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
