Political connections, board of directors and firm
performance: The case of Tanzania
Abstract
In this article I use firm-level dataset to offer the first systematic assessment of the effects of
political connections on firm performance in Tanzania. My sample consists of all listed firms
in Dar-es-saalam Stock Exchange (DSE). I define politically connected firms as follows. A
board member who satisfies any of the following two criteria is considered to be a politically
connected director: (1) a current or former government official; and (2) a current or former
member of a political party in the country. My results indicate that political connections in
the board of directors hurt firm performance measured by changes in return on assets and
return on sales. This finding is robust in various alternative regression estimated. This
suggests that firms with politically-connected directors exhibit significantly worse firm
performance than non-connected counterparts.Overall, the results indicate that the
appointment of politically-connected directors does not enhance firm efficacy butrather fulfil
political goals of politicians.
Key words: Political connections, board of directors, firm performance
1
Introduction
The board of directors is a critical corporate governance mechanism, however our knowledge
of what makes boards effective is quite limited. Previous research has much focused on how
board structure or ownership structure affects firm performance. To date, relatively little
research has investigated the impact of politically connected boards on firm performance.
This research is motivated by two main issues. First, the literature that links politically
connected boards and firm performance is very scarce. Second, to my knowledge there is no
systematic study that has analysed the relationship between the two issues in the Tanzanian
context.The primary objective of this research is to investigate the nature and extent of
politically connected board members and their effect on the firm performance.
The main findings and contributions of this paper are as follows. First, I find that political
connections and firm performance are negatively correlated.Both measures of political
connections exhibits significant negative relationship with firm performance measures
namely changes in return on assets and return on sales. This finding is robust in various
alternative regression estimated. This suggests that firms with politically-connected directors
exhibit significantly worse financial performance than non-connected counterparts.As far as I
know, this paper is the first systematic study that analyse the effect of politically-connected
directors on corporate performance. This study extends the limited literature that examines
the link between politically-connected directors and firm performance.
Secondly, I find that to some extend blockholders ownership affects firm performance
adversely. This is consistent with “Type ІІ agency problem” which argues that controlling
shareholders expropriate minority shareholders through tunneling. That means blockholders
collude with firm managers to expropriate company assets at their own benefit. On the other
hand, this is contrary to the notion that blockholders are monitors of managers and helps to
improve firm performance.This finding extends the previous literature that investigates the
link between corporate governance attributes and firm performance.
The rest of the paper is organised as follows. Section 2 discusses the literature review on
politically-connected directors and firm performance. Section 3presents definition of
variables and econometric specification used in the regression analysis. Section 4 discusses
descriptive statistics of the variables used in the paper. In Section 5, we present and discuss
main findings of the paper. Section 6 discusses robustness results. Finally, in Section 7 we
present conclusion and future research opportunities.
2
Relevant literature
The literature that links political connections and corporate governance issues is limited. The
perception that political control over firms’ decision making is detrimental to firm
performance is logical and, to some extent, the corporate governance literature has
demonstrated evidence on this link. Most theoretical arguments rely on the assumption that
politicians use firms to pursue political and social objectives such as reducing income and
regional inequality, correcting market failures and providing excessive employment (Boycko
et al., 1996, Shleifer and Vishny, 1994, Shleifer and Vishny, 1998). All these factors are
rationally detrimental to the firm’s financial performance. In fact, it is mostly assumed in the
literature that, in the absence of political control, shareholders and managers have an
incentive to maximize profits.
On the other hand, Brada (1996) viewed politicians as individuals who have incentive to
prevent controlling shareholders and managers from engaging in behaviour that reduces the
amount of corporate’s resources. In this context, the net effect of political control on firm
performance depends on the balance between political costs and incentive problems of
managers and shareholders.
Chang and Wong (2004) examined the impact of political control on Chinese listed
corporations. These authors documented two major findings. First, they observed that party
political control restrains the largest shareholders from expropriation but that the existing
level of party control is unsatisfactory to control the largest shareholders. Second, their
findings revealed that decision-making power of local party committees relative to managers
is negatively related to firm performance. Another study by Leuz and Oberholzer-Gee (2006)
examined firms with political connections in relation to global financing and corporate
transparency. Their main findings revealed that politically connected firms are less likely to
have publicly traded foreign securities. In this paper, they find that politically related firms
influence long-run performance and alter firms’ financing strategies.
The study by Faccio et al. (2006) have analysed the probability of government bailouts of 450
politically connected firms from 35 countries during 1997–2002. Their major finding showed
that politically connected firms are significantly more likely to be bailed out than similar nonconnected firms. Besides, their findings revealed that politically connected firms are
disproportionately more likely to be bailed-out when the International Monetary Fund or the
World Bank provides financial assistance to the firm’s home government. In addition, among
bailed-out firms, those that are politically connected exhibit significantly worse financial
performance than their non-connected counterparts at the time of and following the bailout.
The main implication of these findings is that politically connected firms are associated with
poor financial performance.
Fan et al. (2007) investigated politically connected CEOs with post-IPO performance in
newly partially privatised Chinese firms. In this paper, CEO’s political connection is defined
as serving as a current or former government bureaucrat. Their finding revealed two
interesting findings. Firstly, they find that firms with politically connected CEOs
underperform those without politically connected CEOs by almost 18% based on three-year
post-IPO stock returns and have poorer three-year post-IPO earnings growth, sales growth,
and change in returns on sales. Secondly, firms led by politically connected CEOs do not
consider professional backgrounds in directors’ recruitment. These firms are more likely to
appoint incompetent officials to the board of directors rather than directors with relevant
qualifications.
The study by Boubakri et al. (2008) explored the extent of political connections in newly
privatised firms in 27 developing, and 14 developed countries over the period 1980 to 2002.
First, they observed that 87 firms have a politician or a former politician on their board of
directors. These politically connected firms are characterised by high leverage and operate in
regulated industries. Second, the authors revealed that the probability of seeing political
connections in these firms is positively linked to government residual ownership, and
negatively linked to foreign ownership. Finally, they found that politically connected firms
have poor accounting performance compared to non-connected firms.
Another paper by Frye and Iwasaki (2011) examined whether the government sends directors
to weak or strong firms. In this study the performance of the firms is based on the frequency
of their dividend payments. The comparison of the frequency of dividend payments by firms
with and without government directors reveals that the frequency of dividend payments by
firms with government directors is higher than that by firms without them. Furthermore, the
number of dividend payments is positively related with the total number of state
representatives on a board. This finding is inconsistent with the managerial discipline ideal
type which suggests that state directors are sent to poorly performing firms, but is consistent
with the rent-extraction and collusion ideal types.
Recent research by Menozzi et al. (2012) revealed that politically connected directors exert a
positive and significant effect on employment, while they impact negatively on firm
performance. This negative effect on corporate performance is consistent to many previous
studies such asFaccio et al. (2006),Fan et al. (2007) and Boubakri et al. (2008). Overall, the
extant literature on this topic has provided insufficient systematic evidence that politically
connected boards worsens corporate performance. For this reason, further organised
empirical analysis is important to enhance our understanding on the topic and corroborate
findings from the previous studies.
3
3.1
Methodology
Data
The data used in this paper is collected at firm-level for all companies listed in the Dar
essalaam Stock Exchange (DSE). Despite being listed at the DSE notall companies deposit
annual reports in the African Financial Database or even their websites. For this reason, I
have used two main techniques to hand collect these data. First, I have collected data from
annual reports for each company from the time being listed to the financial year ended in
2012. Second, I have constructed questionnaire that asks information required for those
companies that do not deposit their annual reports in the African Financial Database and
websites.
3.2
Dependant variable
Corporate performance is the dependent variable used in this paper. However, the concept of
performance is a contentious issue in finance largely due to its multidimensional meanings.
Generally, performance measures can be categorised as being either accounting-based or
stock market-based metrics. The most commonly used accounting-based performance proxies
are return on assets (ROA) and return on equity (ROE) or return on investment (ROI). There
are various previous studies that have employed these measures, e.g. (Gorton and Rosen,
1995, Mehran, 1995, Ang et al., 2002). On the other hand, the frequently used stock marketbased performance measures are market value of equity to book value of equity (MBVR) and
Tobin’s Q, e.g. (McConnell and Servaes, 1990, Morck et al., 1988, Zhou, 2001).
However, it should be noted that the choice of performance measure entirely depends on the
objective of the analysis. The focus of this research is to investigate the impact of politicallyconnected board members on corporate performance. In this paper, I employed changes in
return on assets (ROA) andchanges in return on sales (ROS). Consistent to Megginson et al.
(1994), I considered changes in return on sales as an appropriate measure because it is less
sensitive to inflation and accounting conventions. In addition, manyprevious studies used
these measures of firm performance, e.g. (Fan et al., 2007, Boubakri et al., 2008). I then
define return on assets (ROA) as the ratio of the company’s earnings before interest and
taxation divided by total assets, whereas return on sales (ROS) isdefined as the ratio of the
company’s earnings before interest and taxation divided by sales.
3.3
Independent variables
3.3.1 Political connections
This is the key independent variable of this paper. The criterion for a political connected
board member is as follows. A board member who satisfies any of the following two criteria
is defined as a politically connected director: (1) a current or former government official; and
(2) a current or former member of a political party in the country. This definition closely
follows the recent study byChen et al. (2011). This information is collected from the directors’
biography in the annual report of each firm. I then measure political connection in two ways.
First, the variable is computed as the number of politicians who are board members at the end
of the year. Second, the variable is computed as the percentage of politically-connected
members on the board of directors at the end of the year.
3.3.2 Control variables
I controlled for several variables. I have included various corporate governance variables
since they have great impact on firm performance. Directors’ remuneration is defined as the
logarithm of total emoluments paid to all directors at the end of the year. The extant literature
suggests that firm performance is highly influenced by directors’ remuneration(Ozkan, 2011,
Brick et al., 2006).Blockholders ownership is defined as the percentage of shares owned by
larger organisations or individuals with substantial shareholding amounting to or exceeding
three percent of the firms’ share capital. The ownership is disclosed if it amounts to three
percent or more of the shares outstanding. I aggregate all such three percent (or more)
holding for a specific firm. Goyal and Park (2002) andDenis et al. (1997) used similar
technique in defining ownership structure variables.
The proportion of non-executive directors is another variable that is included in the
regression specifications. I use this approach as non-executive directors are regarded as
corporate monitors and most corporate governance codes recommends boards should include
non-executives to enhance shareholders wealth e.g.(Greenbury, 1995, Cadbury, 1992).
Additionally, the inclusion of this variable is common in practice, e.g.(Conyon and Peck,
1998, Doucouliagos et al., 2007).Following the standard literature on firm performance, I
have included firm size measured by logarithms of total assets.
3.4
Econometric specification
Initially, I performed Hausman specification test to identify more efficient models between
fixed and random effects regressions. I then estimated fixed effects regression following the
results of the test performed.
4
Descriptive statistics
The mean of the return on assets is 0.19, with a standarddeviation of 0.39. The range of return
on assets is from -0.05 to 3.19.The mean of the return on sales is 0.22, with a standard
deviation of 0.35. The highest return on sales is 2.88 while the lowest is -0.06. The data
analysed reveals that, on average about 25% of board members are politicians. A firm with
larger number of politicians accounts to about 57% of the board members. The data also
showed that two firms had no politicians on their boards at certain time during the sample
period.
The mean for blockholders is 69.08% with standard deviation of 21.23. The highest
blockholder is 100% while the lowest is 34.80%. The average proportional of non-executive
directors is 85.13%, and its standard deviation is also 9.36. The range of proportional of nonexecutive directors is from 50% to 100%. The mean of logarithm of directors’ remuneration
is 4.22 and also has a standard deviation of 1.38. The value ranges from 1.10 to 6.71.For firm
size, measured by logarithm of total assets, the mean reported is 11.24 and the range is from a
lowest 8.03 to the highest 14.81. The standard deviation for firm size is 1.86.
Table І: Descriptive statistics
This table reports descriptive statistics of the variables used in this paper. These variables are defined as follows.
Return on assets (ROA) is defined as the ratio of the company’s earnings before interest and taxation divided by
total assets. ROS is defined as the ratio of the company’s earnings before interest and taxation divided by sales.
No of politician is the number of politicians who are board members at the end of the year. Detailed explanation
of identifying politician is included in the main text of this paper. Politician (%) is the proportion of politician
available in the board at the end of the year. Blockholders ownership is defined as the percentage of shares
owned by larger organisations or individuals with substantial shareholding amounting to or exceeding three
percent of the firms’ share capital. I aggregate all such three percent (or more) holding for a specific firm. Nonexecutive directors (%) is the proportion of non-executive directors available in the board at the end of the year.
Log (directors’ remuneration) is the logarithm of the total emoluments paid to all directors at the end of the
year.Firm size is defined as common logarithm of company's total assets at the end of the year.
Variables
Observations
Mean
Std
Minimum
Maximum
ROA
82
0.19
0.39
-0.05
3.19
ROS
66
0.22
0.35
-0.06
2.88
No of politicians
83
2.13
1.24
0.00
4.00
Politician (%)
80
24.97
12.60
0.00
57.14
Blockholder (%)
68
69.08
21.23
34.80
100.00
Non-executive directors (%)
80
85.13
9.36
50.00
100.00
Log (Directors’ remuneration)
75
4.22
1.38
1.10
6.71
Firm size
82
11.24
1.86
8.03
14.81
Table ІІ reports the Pearson correlation matrix for the variables. Among the independent
variables, no correlation exceeds 0.42, which is far below the typical threshold of 0.7 that is
used to identify the presence of multicollinearity(Lind et al., 2005). To further ensure that
multicollinearity is not a problem, I calculate variance inflation factors (VIF) for each
independent variable. The VIFs, which are reported in the last two rows of Table ІІ, never
exceed 9.34 which is lower than the typical threshold of 10(Belsley et al., 1980).
Table ІІ: Correlation matrix
This table reports correlation matrix of all variables used in the regression analysis. These variables are defined
as follows.Return on assets (ROA) is defined as the ratio of the company’s earnings before interest and taxation
divided by total assets. ROS is defined as the ratio of the company’s earnings before interest and taxation
divided by sales. No of politician is the number of politicians who are board members at the end of the year.
Detailed explanation of identifying politician is included in the main text of this paper. Politician (%) is the
proportion of politician available in the board at the end of the year. Blockholders ownership is defined as the
percentage of shares owned by larger organisations or individuals with substantial shareholding amounting to or
exceeding three percent of the firms’ share capital. I aggregate all such three percent (or more) holding for a
specific firm. Non-executive directors (%) is the proportion of non-executive directors available in the board at
the end of the year. Log (directors’ remuneration) is the logarithm of the total emoluments paid to all directors
at the end of the year Firm size is defined as common logarithm of company's total assets at the end of the
year.The last two rows shows the Variance Inflation Factor (VIF) as a key measure of multicollinearity problem.
In theestimation, none of the explanatory variables in the regression equations revealed VIF coefficient above
9.34. This figure is below the conventional rule of thumb of 10. This suggests multicollinearity is not a problem
in the whole analysis. Bolded figures indicate significant correlations between variables.
Variables
ROA-1
ROS-2
No of politicians-3
Politician (%)-4
Blockholder (%)-5
Non-executive directors (%)-6
Directors’ remuneration-7
Firm size-8
VIF (ROA as dependent variable)
VIF (ROS as dependent variable)
1
-
2
0.23
(0.07)
-0.22
(0.05)
0.00
(0.99)
0.35
(0.00)
-0.26
(0.03)
0.25
(0.03)
-0.09
(0.41)
0.14
(0.27)
0.16
(0.24)
0.23
(0.11)
-0.06
(0.64)
0.07
(0.62)
0.29
(0.02)
3
4
5
6
7
8
0.89
(0.00)
-0.06
(0.62)
-0.21
(0.06)
-0.14
(0.25)
0.30
(0.01)
7.13
9.34
0.16
(0.19)
-0.23
(0.04)
-0.16
(0.20)
0.09
(0.44)
6.97
7.90
-0.14
(0.26)
0.19
(0.12)
-0.14
(0.26)
1.72
1.70
-0.42
(0.00)
-0.20
(0.09)
1.54
1.54
0.01
(0.94)
1.48
1.53
1.45
1.36
Note: P-values in parentheses
5
Main results
In this section, I present regression results on the impact ofpolitically-connected directors on
the performance of listed firms in Tanzania.Consistent with the results of someearlier studies,
I find a negative and significant effect of politically-connected directors on the changes of
ROA and ROS regressions reported in Table І. Despite using alternative measures of
politically-connected board of directors, all models estimated demonstrated a significant
negative relationship of the 1% level.
Table ІІІ: Main results
This table reports fixed effects regression results. The variables used are defined as follows.Return on assets
(ROA) is defined as the ratio of the company’s earnings before interest and taxation divided by total assets. ROS
is defined as the ratio of the company’s earnings before interest and taxation divided by sales. No of politician is
the number of politicians who are board members at the end of the year. Detailed explanation of identifying
politician is included in the main text of this paper. Politician (%) is the proportion of politician available in the
board at the end of the year. Blockholders ownership is defined as the percentage of shares owned by larger
organisations or individuals with substantial shareholding amounting to or exceeding three percent of the firms’
share capital. I aggregate all such three percent (or more) holding for a specific firm. Non-executive directors (%)
is the proportion of non-executive directors available in the board at the end of the year. Log (directors’
remuneration) is the logarithm of the total emoluments paid to all directors at the end of the year. Firm size is
defined as common logarithm of company's total assets at the end of the year. Regulated industry is the dummy
variable recorded as one if the firm is from the banking or insurance industry, zero otherwise. Year dummies are
also included in all regression estimated. Models (1) and (2) use ROA as the dependant variable while Model (3)
and (4) use ROS as the dependent variable. Other items in the table are self-explanatory.
Dependent variable
Changes in ROA
Changes in ROS
Variables
Model (1)
Model (2)
Model (3)
Model (4)
Constant
0.002*
0.003*
0.200***
0.218**
(1.932)
(2.022)
(2.752)
(2.446)
-0.001***
-
-0.033***
-
No of politicians
(-6.011)
Politician (%)
-
(-4.967)
-0.000***
-
(-3.770)
Blockholder (%)
-0.001***
(-2.904)
-0.000
-0.000
-0.000
-0.001
(-0.826)
(-1.446)
(-1.387)
(-1.633)
-0.000
-0.000
-0.000
-0.001
(-0.633)
(-1.421)
(-0.780)
(-1.315)
-0.000
0.000
0.003
0.005
(-0.089)
(0.259)
(0.518)
(0.776)
-0.000
-0.000
-0.007
-0.008
(-0.128)
(-0.311)
(-0.995)
(-1.028)
Regulated industry
Yes
Yes
Yes
Yes
Year dummies
Yes
Yes
Yes
Yes
Observations
45
45
44
44
Number of firms
9
9
9
9
R-squared
0.594
0.397
0.529
0.329
Adj. R-squared
0.423
0.144
0.324
0.039
Non-executive directors
Log (Directors’ remuneration)
Firm size
t-statistics in parentheses
*** p<0.01, ** p<0.05, * p<0.10
The coefficients reveal that politically-connected boards reduce firm performance by about
0.001 to 0.033. This implies that firms managed by politically connected directors have lower
performance than non-connected firms suggesting that political connections might impose a
cost on the firms. My finding is similar to many recent studies such as Fan et al. (2007),
Boubakri et al. (2008) and Menozzi et al. (2012). Overall, the results indicate that the
appointment of politically-connected directors does not enhance firm efficacy but rather fulfil
political goals of politicians.
6
Robustness checks
To confirm results discussed in the previous section, I repeated the same set of regressions
while excluding some corporate governance variables (i.e. proportional of non-executive
directors and directors’ remuneration). This technique assists in scrutinising the impact of
politically-connected boards on firm performance without the influence of these important
corporate governance factors. The findings on politically-connected boards remained
qualitatively similar with minor changes in coefficients levels. In fact, the results indicated
politically connected boards reduce firm performance by about 0.001 to 0.035. This shows
that there is a minimal increment in economic relationship between the two investigated
variables. Surprisingly, I find little evidence that blockholder ownership substantially reduces
firm performance as measured by return on sales. In the previous estimation, the impact of
blockholders was statistically insignificant.
Table ІѴ: Robustness checks
This table reports fixed effects regression results while excluding some corporate governance variables. The
variables used are defined as follows.Return on assets (ROA) is defined as the ratio of the company’s earnings
before interest and taxation divided by total assets. ROS is defined as the ratio of the company’s earnings before
interest and taxation divided by sales. No of politician is the number of politicians who are board members at
the end of the year. Detailed explanation of identifying politician is included in the main text of this paper.
Politician (%) is the proportion of politician available in the board at the end of the year. Blockholders
ownership is defined as the percentage of shares owned by larger organisations or individuals with substantial
shareholding amounting to or exceeding three percent of the firms’ share capital. I aggregate all such three
percent (or more) holding for a specific firm. Firm size is defined as common logarithm of company's total
assets at the end of the year. Regulated industry is the dummy variable recorded as one if the firm is from the
banking or insurance industry, zero otherwise. Year dummies are also included in all regression estimated.
Models (1) and (2) use ROA as the dependant variable while Model (3) and (4) use ROS as the dependent
variable. Other items in the table are self-explanatory.
Dependent variable
Changes in ROA
Changes in ROS
Variables
Model (1)
Model (2)
Model (3)
Model (4)
Constant
0.002**
0.001
0.150***
0.114*
(2.131)
(1.336)
(2.808)
(1.732)
-0.001***
-
-0.035***
-
No of politicians
(-6.822)
Politician (%)
-
(-5.687)
-0.000***
-
(-4.050)
Blockholder (%)
-0.001***
(-3.118)
-0.000
-0.000
-0.001
-0.001*
(-0.860)
(-1.660)
(-1.674)
(-1.992)
-0.000
-0.000
-0.004
-0.003
(-0.445)
(-0.216)
(-1.156)
(-0.741)
Regulated industry
Yes
Yes
Yes
Yes
Year dummies
Yes
Yes
Yes
Yes
Observations
47
47
46
46
Number of firms
9
9
9
9
R-squared
0.588
0.346
0.510
0.256
Adj. R-squared
0.459
0.141
0.351
0.015
Firm size
t-statistics in parentheses
*** p<0.01, ** p<0.05, * p<0.10
7
Conclusion and future research avenues
In this paper, I examine the impact of politically-connected board of directors on corporate
performance for Tanzanian listed firms. I documented that politically-connected board of
directors is associated negatively with firm performance.My finding implies that firms with
politically-connected directors exhibit significantly worse financial performance than nonconnected counterparts. Overall, the results indicate that the appointment of politicallyconnected directors does not enhance firm efficacy but rather fulfil political goals of
politicians. The future avenues for this research are as follows. First, this paper creates a
roadmap that might involve investigation of several listed corporations in East Africa or
Africa at large. The analysis of larger sample would result to more reliable findings. Second,
it would be interesting forfuture research to explore the link between politically-connected
board of directors and firm performance using alternative regression techniques. Such
researchwould help understand the relationships and corroborate previous findings.
8
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