Effects Of Public Sector Corruption On The Private Sector: Investigating The Market Value Of Political Connections
Omotoke Paul-Lawal*
Under the direction of Professor Arun Chandrasekhar
Stanford University Department of Economics
Honors Thesis
May 30, 2016
Abstract This paper exploits public announcements of corruption allegations amongst Nigerian political figures to estimate the impact of political connections on firm value. Specifically, I draw results from market reactions to 27 events to determine whether politically connected firms suffer more from financial malpractice amongst government officials. This paper finds heterogeneous impacts of corruption announcements for firms’ returns. For some events involving larger sums of misallocated funds, politically connected firms suffer greater losses in their returns than unconnected firms. For other events involving smaller amounts of missing funds, I find that unconnected firms are actually punished more than connected firms. Still for other events, there appears to be no significantly differential impact of a government corruption scandal on connected and unconnected firms. Furthermore, surprisingly, some events actually show a positive return for all firms, and when this is the case, politically connected firms experience a larger increase in returns than their unconnected counterparts. To identify an overall impact, I run a pooled regression aggregating over all the events studied. I find that on average, there is a negative albeit insignificant impact on the returns of politically connected firms relative to unconnected firms in the aftermath of a corruption scandal. Keywords: political connections, corruption, firm value, Nigeria
* Email address: [email protected]. I owe many thanks to my thesis advisor and Economics major advisor, Arun Chandrasekhar, whose advice, positivity, and endless support were invaluable in completing this work. My gratitude also goes to the Honors Director, Marcelo Clerici-Arias for organizing this program and always being available to answer questions. Special thanks go to Francisco Munoz for his comments and help in editing this paper. And last but not least, I am also greatly indebted to my family and friends without whose encouragement and confidence I could not have completed this project.
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1. Introduction
Historically, Nigeria has been infamous for its endemic fraud and corruption, and the
country’s economy has been fraught with the difficulties of overcoming the challenges posed by
these institutional problems. Nigeria ranked 136 out of 168 countries, with a score of only 26 out
of 100, on Transparency International’s widely quoted 2015 Corruptions Perceptions Index. As
a step towards combating these institutional challenges, in 2002, the Nigerian government
created an independent organization, the Economic and Financial Crimes Commission (EFCC), a
watchdog agency that acts as surveillance against money laundering, fraud, embezzlement, and
other forms of financial crime. Individuals and organizations report cases to the EFCC, who then
take up the case for research and investigation, often culminating in litigation. The importance of
the EFCC’s work is reflected in the widespread publicity of its cases – a fact that this paper
exploits.
When a case is reported to the EFCC, the organization usually takes the information to
the press, exposing details about the case, including names of the concerned individuals and their
associated organizations or government positions, as well as the amount alleged to be missing
under their control. This information often makes big news, and there has been much speculation
that the reports have large impacts on firms listed on the Nigerian Stock Exchange. This paper is
concerned with the extent to which the data supports this claim, and the extent to which investors
respond to such corruption announcements. The study focuses on high profile cases involving
public officials, with the aim of testing the impact of such notable cases of public sector
corruption on private firms. The fact that these cases involve well-known politicians, and that all
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the cases studied are reported in at least of one of the country’s major newspapers and/or online,
provides support for the belief that the information is readily available to the public and
investors. If the market response to the news of scandal is large, this informs us not only about
the importance of political connections, but also about the potential impact of corruption on the
financial economy, and the important role that fraud control agencies like the EFCC may play.
To investigate the value of political connections for firms, this paper studies 27 high-
profile corruption cases in which public officials are accused of some sort of financial
malpractice. The public officials include elected and appointed federal and state political figures.
Majority of the cases involve former governors of state, and the rest involve senators and
ministers of various government departments. This study takes a broad view of political
connections by defining a politically connected firm as a firm in which at least one member of its
board of directors had at any time in his or her past (before the event date) held a position such as
senator, member of the House of Representatives, or member of the administration, or had been a
director of an important government organization in the same party as the sitting party at the time
of the event. By taking a broad approach, I am able to examine whether the market punishes
firms with any type of link to the government, as defined above, in response to the actions of
one/a few government officials. If this is so, then there will be grave consequences of political
corruption on any scale for firms with potential access to political favors as suggested by their
connections.
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2. Literature Review
There is a growing body of empirical literature on the importance of political connections
to firm value. In different contexts, many researchers have found that political connections have
a significant positive impact on firm value. Fisman (2001) examines the impact of several news
announcements concerning the deteriorating health of President Suharto on firms in Indonesia
with political links to the Suharto family. He finds that well-connected firms suffer more than
less-connected firms in the event of a serious rumor, suggesting that a large share of a well-
connected firm’s value is derived from political connections. In her cross-country study, Faccio
(2006) finds that a company’s value increases when its large shareholders enter into politics, and
this result is most prominent in highly corrupt countries. Similarly, Do et al. (2015) find that
firms connected to elected governors experience a significant increase in their value compared to
those connected to the losing candidate. This study will enrich the existing international evidence
on the value of political connections (e.g. Faccio 2006; Fisman 2001; Prem 2015), whilst
complementing findings in the US (e.g. Do et al. 2015; Goldman, Rocholl, and So, 2009).
In addition to estimating the importance of political connections, a number of papers also
provide channels through which connections create value for politically connected firms. Several
studies have suggested that political connections are valuable to firms because they lead to
preferential access to bank finance (e.g. Gonzalez and Prem 2015; Li et al. 2008, Claessens et al.
2006; Kwhaja and Mian 2005). For example, Gonzalez and Prem (2015) argue that politically
connected firms are better able to make strategic investments when faced with a political
transition because of distortions in the credit market which favor connected firms, allowing them
to maintain their market dominance from the non-democratic regime into the new government.
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Kwhaja and Mian (2005) also find that political firms borrow more, despite having higher
default rates, and this preferential treatment occurs only in government banks and not in private
banks. Meanwhile, Faccio, Masulis, and McConnell (2006) suggest that connections create value
by showing that politically connected firms are more likely to be bailed out than their
unconnected counterparts.
This paper will also contribute to the literature that studies the value of political
connections through exploiting events that happened independently of the connections. Many
papers have exploited close election races, such as Do et al. (2015), which uses a regression
discontinuity design to examine the impact of a connected governor winning office on firm
value. Similarly, Goldman et al. (2008), Knight (2007), and Matozzi (2008) all exploit close
elections in US presidential races. Roberts (1990), Fisman et al. (2006), and Jayachandran (2006)
use events or news involving key political figures in the US, whilst Prem (2015), Fisman (2001),
and Ferguson and Voth (2008) exploit important political events in Chile, Indonesia, and Nazi
Germany, amongst other international studies of political connections (e.g. Johnson and Mitton
2003 in Malaysia; Imai and Shelton 2010 in Taiwan). This strategy of using independent or
unexpected events is favored in the literature because it provides a relatively clean solution to the
identification problem, whilst avoiding the reverse causation channel (Do et al. 2015).
Research on political connections in the Nigerian context is scarce. However, the few
existing results suggest that political connections do not play a huge role in the valuation of firms
in Nigeria. For example, Osamwonyi and Tafamel (2013) find that there is no significant
relationship between board composition, board political connections, and firm performance for
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their sample of thirty firms listed on the Nigerian Stock Exchange. Likewise, Aburime (2009)
finds that political affiliation has a positive but insignificant impact on bank profitability in
Nigeria. Some of the results of this paper seem to affirm these findings.
The current study is unique in the wealth of events it exploits to investigate the
comparative impact of bad news related to public officials on the value of politically connected
private firms and that of unconnected firms. The degree to which news of corruption affects the
health of the economy, as indicated by the value of firms that make up that economy, is
dependent on how much perceptions of government affect the willingness of investors to invest
in private Nigerian firms. This research will try to assess the magnitude of that dependence. If
the effect is significant, then this provides further impetus for government to clamp down on
instances of financial corruption, and informs corporate governance and institutional design.
3. Data and Descriptive Statistics
This paper uses two main bodies of data: it employs stock market data, and it also uses
information about the corruption events. The sample consists of all firms on the Nigerian Stock
Exchange (NSE) that remained listed between 2005 and 2015, the period of the events that this
paper studies. This leads to a total sample of 123 firms, of which I exclude those that lack
sufficient stock market and political connections data to observe returns for the corruption
announcements. This leads to a remaining base sample of 106 firms. All financial information
such as closing price, trade volumes, EPS, and market index values were retrieved from the
Nigerian Stock Exchange databases. Data about firms’ political connections was hand-collected
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by gathering the names of all members of the board in the relevant years from the firms’ annual
reports, and researching their career background in search of any political history. To aid this
process, I used online sources like Bloomberg and Reuters that detail biographies of reputable
business people, along with director introductions on the companies’ websites. I then verified the
political connections using Google searches that usually returned several articles mentioning the
political involvement of the director.
Data on the corruption events was acquired from the Economic and Financial Crimes
Commission (EFCC) in Nigeria. Relevant information about the corruption events include the
date that the case was first released to the public, the government personality involved, and the
amount alleged to be missing, stolen, or otherwise diverted. Where available, the date of EFCC’s
press release is used as day 1 of the event, or the event start date. When this was unavailable, I
used an online search to track down the first evident publication reporting the event in order to
determine the start date as this is key in identifying when the information first became publicly
accessible, allowing the stock market to react. One concern of this research is that particularly
connected investors or other insiders may have had access to the information before it became
public knowledge. If this is the case, then the public announcement may have no effect on the
firm, or the firm’s value may have adjusted earlier than the announcement date used. However, if
this is the case, then any findings here are lower-bounds on the true effects of the corruption
events. Furthermore, even if the market had anticipated the scandal, then the announcement
resolves any remaining uncertainty about whether the politician’s fraudulent actions were simply
rumors, or serious, actionable matters – a fact that should generate further market responses.
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As described above, this paper defines politically connected firms as those firms in which
at least one member of the board of directors had at any time in his or her past held a position
such as senator, member of the House of Representatives, or member of the administration, or
had been a director of an important government organization in the same party as the party in
government at the time of the event. These include both elected and appointed positions, but are
limited to federal and state government officials, who may be thought to be in a position to
provide significant access to political resources and favors. Furthermore, only one party has
been in government since Nigeria resumed elections in 1999 following the period of military
rule. This closed political structure, as well as the elitist club nature of politics in Nigeria, makes
this a valid definition of political connections.
Row 1 of Panel A in Table 1 reports descriptive statistics for the connections variable.
The firm with the greatest number of connections is United Bank of Africa (UBA) with a total of
7 connected board members over the studied years. In the first phase of the study, I assign a
value of 1 to all firms with any number of connected board members in the year of the event. On
average across the listed firms, the mean connections in the sample is 0.49. The average number
of connections conditional on a firm having at least one connection is 1.78.
Rows 2 to 4 in Panel A of Table 1 report basic financial information for the sample firms
from the Nigerian Stock Exchange database for 2014, supplemented by data from the financial
statements of the companies. Row 2 reports the firm’s size as the logarithm of total assets.
Return on equity is used for profitability, and leverage in Row 4 is the ratio of total debt to total
capital. Panel B reports differences in the means of these variables for connected and
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unconnected firms. As might be expected, connected firms are larger than unconnected ones (by
6.57%). Furthermore, connections appear to be significantly higher in firms in the finance and
agricultural industries. To ensure that these differences between connected and unconnected
firms are not driving the results, I conduct robustness checks that control for size and industry.
However, connected firms are not significantly more profitable, more leveraged, or older than
unconnected ones, so any differences we observe in market returns for the two types of firms
cannot be attributed to these characteristics.
4. Empirical Strategy
The econometric strategy exploits within firm variation and the corruption events as
exogenous announcements in a standard differences-in-differences analysis using panel data.
Since all firms are similar on most basic characteristics, as shown in the last section, observed
market responses reflect differences in political connections. A major concern of this research is
that the results may have been driven by other events in the macroeconomy. To control for the
fact that there were large market movements during the event windows, this paper calculates
abnormal returns for the event days. I follow Acemoglu 2013 and calculate abnormal returns
using the following market model:
Abnormal Returns or AR is calculated as
!"!" = !!" − [!!! + !!!!!"]
(1)
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where !"!" is the abnormal return for firm ! on event day t. !!" is the firm’s actual return, and
!!" is the market return, using the NSE’s All Shares Index (ASI). The parameters, !!! and !!!
are estimated from the pre-event relationship between market returns and firm returns:
!!" = !! + !!!!" + !!"
I follow the standard in the literature of using a pre-event period of 250 trading days ending 30
days before the event start date. The abnormal returns shows the actual returns to each firm
minus the predicted returns based on the firm’s performance relative to the market over the
estimation period.
After calculating abnormal returns in this way for all firms in each event window, the
main regression this research estimates is as follows:
!"!"# = !!!"#$!" × !"##!" + !!!"#$!" + !! + !! + !!"#
where !!!"# is the abnormal return for firm ! operating in industry group ! in time period !.
!"#$!" is an indicator variable that is equal to zero before the event and one in the period after
the event. Likewise, !"##!" is an indicator variable that takes the value of one if the firm was
politically connected at the time of the event. !! and !! are firm- and time-fixed effects
respectively, whilst !!"# is an error term. The main estimator of interest is !"#$!" × !"##!", the
interaction between the event and connections variables, which allows us to study the differential
impact of corruption announcements on the returns of connected and unconnected firms.
(2)
(3)
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I run two variations of equation (3). In the first, !"##!" is a simple dummy variable that
indicates whether a firm has any connections or not in the year of the event. I will refer to this as
the “simple” model. In the second specification, I classify politically connected firms according
to the number of actual connections that they have to examine the effects of varying degrees of
connections, so !"#$""%&''!" takes on this real number. I will refer to this specification as the
“extended” model.
5. Results
To estimate the impact of the events on the returns of firms, this study runs regressions
using a 5-day event window. Each column in Table 2 reports the effects of the different events
on firms’ stock prices for the simple model, with standard errors reported below the coefficients
in parentheses. The coefficient on !"#$!", the dummy variable for the period after the
announcement, is significant for many of the events, but takes on varied signs, making it unclear
in which direction the events affect firm returns by themselves. This may be due to the fact that
there appears to be more trading of stocks overall for both connected and unconnected firm on
event dates, causing prices to be more volatile when announcements are made.
Likewise, as Table 2 shows, the main regressor, !"#$!" × !"##!", takes on both negative
and positive signs depending on the event. In the simple model, only 3 out of the 27 events return
statistically significant differences for connected and unconnected firms. In one of these events,
“Alao-Akala 6Oct11” (Event 3), politically connected firms suffer an extra loss of .00554 in their
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abnormal returns, or an extra loss of 72% compared to unconnected firms. This is equivalent to
additional losses of 0.15 standard deviations for connected firms and it is statistically significant
at the 5% level. Figure 1 provides a visual representation of this result. Meanwhile, two events
(Events 7 and 25) return a positive coefficient on the main estimator, suggesting that
unconnected firms actually suffered greater losses than politically connected firms.
In the first case, all firms suffer a net loss in returns after the corruption announcement,
but politically connected firms suffered a smaller loss which is reflected by the negative
coefficient on !"#$!" and positive coefficient on the interaction term, !"#$!" × !"##!". This may
be seen in the results of Event 7 (“Botmang 18Jul08”, column 7) in which connected firms suffer
a loss of .00483 or 69% less than unconnected firms. This is equivalent to 0.13 standard
deviations in the returns of connected firms, and is significant at the 10% level. The second case
involving a positive interaction term is a particularly interesting finding of this study and is
observed in the results of Event 25 in the simple model. In this case, the coefficient on !"#$!" is
also positive, so all firms experienced an increase in returns following the corruption
announcement, with connected firms observing 3 times more gains in abnormal returns than
unconnected firms (0.15 standard deviations). Since all firms observe positive abnormal returns,
with connected firms seeing an extra gain, I will refer to this outcome as the “double positive”
result. These results are statistically significant at the 5% level. In the next section, I will discuss
some likely causes of this surprising result, and offer suggestions of possible mechanisms.
To examine the effects of varied amounts of political connections, I ran a variation of
equation (3) in which political connections is defined as the actual number of politically
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connected board members a firm has at the time of the event. Thus, I define a modified political
connections estimator, !"#$""%&''!", which takes the degree of connectedness into account
and is not a simple dummy variable like !"##!". Table 3 shows the results of the extended
model. Overall, the results here support the findings of the simple model, echoing existing
conclusions, whilst generating more statistically significant impacts. In this model, 12 of the 27
events show statistically significant variations of impact between connected and unconnected
firms. The three significant results from the simple model discussed above retain significance
and in fact, gain power in this extended model specification. As Table 3 shows, out of the 12
significant events in the extended model, six of them return a negative coefficient on the main
estimator as expected, where politically connected firms suffer a greater loss in their returns
compared to other firms. Two events return a positive coefficient on the main estimator,
suggesting that connected firms actually suffered slightly less in these events. The remaining
four significant events show a “double positive” coefficient where all the firms saw an increase
in abnormal returns after the event, but connected firms experienced a relatively greater increase
in returns. I offer possible explanations for these findings in the discussion section.
Given the heterogeneity in these results, I ran a pooled regression in order to examine the
average impact of a corruption event on the returns of politically connected and unconnected
firms. The equation that estimates this overall impact, aggregating over all events, is as follows:
!"!"# = !!!"#$!" × !"##!" + !!!"#$!" + !! !"#!" + !! + !! + !! + !!"#
(4)
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The main difference between this regression and the individual event regressions is the addition
of !!, a set of events-fixed effects. As before, !!!"# is the abnormal return for firm ! operating in
industry group ! in time period !. !"#$!" is an indicator variable that is zero before the event and
one after the event. !"##!"is an indicator variable that is equal to one if the firm was politically
connected at the time of the event. !! is a matrix of firm-fixed effects, and !! are time-fixed
effects. Lastly, !!"# is the error term. The key regressor is still the interaction between the event
and connections variables (!"#$!" × !"##!"), which provides the differential impact of the
corruption announcements on the returns of connected and unconnected firms.
Table 4 shows the results of this aggregate regression with clustered standard errors.
Columns 3 and 4 adjust for the size of the firms since this was found to be a key difference
between connected and unconnected firms. Using this refined specification, we observe that,
overall, politically connected firms suffer a negative albeit insignificant impact on their returns
of between 19.1% and 64.9% more than unconnected firms, based on the simple and extended
models respectively. In terms of magnitude, this corresponds to an extra decline of about 0.003
standard deviations for connected firms. The insignificance of the main estimator in the pooled
regression is unsurprising given the number of insignificant individual events in the sample, and
conflicting signs. However, the negative coefficient in the pooled regression is more in line with
previous research findings that politically connected firms suffer more relative to unconnected
firms in reaction to bad news.
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6. Robustness Checks
a) Firm Size
In order to be confident that my findings are due to the causal effect of political
connections, I run a series of robustness checks. Since I found that politically connected firms
are larger in size than unconnected firms, it was necessary to run a test to check that my results
are driven by political connections and not the size of the firm. To do this, I run variations of the
main regression, equation (3), for each significant event, controlling for size as follows:
!"!"# = !!!"#$!" × !"#$""!"##!" + !!!"#$""%&''!" + !!!"#$!" + !!!"#$! + !! + !!"#
!"#$! is the total assets for firm ! in 2014. !"#$!" is the dummy variable for the period after the
event as before, !"#$""!"##!" is the extended model definition of political connections – the
number of politically connected members in the board of directors of firm ! at the time of the
event. Lastly, !! are time fixed-effects, and !!"# is the error term.
Columns (b) of Table 5 show the results of this robustness check. 8 of the 12 events that
showed statistically significant differences in returns for connected and unconnected firms retain
significance at the 5% and 10% levels after controlling for size, suggesting that size is unlikely to
be an omitted variable driving the results for these events.
(5)
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b) Industry
I also found that politically connected firms tended to be concentrated in the finance and
agriculture industries. The differences in industry composition amongst connected and
unconnected firms was significant at the 5% level, generating concerns that any statistical
differences in returns between the two groups of firms was due to industry-based reactions, and
were not necessarily driven by differences in political connections. To ensure the validity of my
original results, I run the following specification that controls for industry:
!"!"# = !!!"#$!" × !"#$""!"##!" + !!!"#$""%&''!" + !!!"#$!" + !!!"#$%&'(! + !!
+ !!"#
Columns (c) of Table 5 show the results of this regression. As we can observe,
controlling for industry has barely any effect on the magnitude of the coefficients across all
events, so the events are robust to industry specifications. Columns (d) control for both size and
industry, and we can see that whilst results are slightly attenuated for some events, the main
estimator remains statistically significant for most events, so results are robust to these different
specifications.
7. Discussion & Analysis
This study found that the effect of a corruption announcement on the returns of firms is
largely event-specific. In some cases, all firms suffer a loss in their returns when news about
corruption allegations involving public officials is released. This reflects the fact that public
(6)
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corruption scandals trigger fear and uncertainty in financial markets, making investors less
confident in the business climate because they perceive that the investment environment is more
risky. In line with much of the past literature, I find that in most of these cases politically
connected firms suffer an extra loss in their returns to the tunes of 0.15 standard deviations
compared to unconnected firms. It makes sense that connected firms are punished more when
such scandals are announced because the scandal reflects instabilities in government, the source
of their advantage. This uncertainty in the coffers of power is associated with a reduced ability to
receive political favors such as preferential access to government contracts, loans, or bailouts.
Nonetheless, the results also show that in some cases, whilst all firms see a loss in their returns,
politically connected firms actually suffer slightly less. This may be the case if investors believe
that politically connected firms are more shielded from economic uncertainties associated with
the corruption case. Indeed, in such environments with weak political and legal institutions, the
advantage of political connections is exactly that it offers opportunities to benefit from the
corrupt system and to engage in more profitable rent-seeking. Thus, firms without such an
advantage may be more vulnerable when the political environment is uncertain.
However, in still other cases, I find the surprising “double positive” result where the
announcement of a political scandal actually increased the returns of all firms, and politically
connected firms experienced a disproportionately larger increase relative to other firms. This
result is indeed unexpected, but several factors unique to the events studied in this paper and to
the Nigerian context may help to explain this fact. As noted earlier, the events studied in this
paper are drawn from cases under investigation by the EFCC, an agency created with the aim of
tackling financial crimes. The agency has largely been hailed for its work, especially as a
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deterrence force. Thus, when the EFCC releases new cases of corruption scandals to the press, it
may be that investors actually interpret the news as evidence that the agency is efficiently
clamping down on corruption, and this generates greater confidence in the investment climate.
Hence, what appears to be bad news on its face, is actually good news to the financial markets,
leading to the positive results we observe in some cases. If this is the case, then politically
connected firms will see an even greater increase in their returns than other firms because they
have closer ties to a political environment that is perceived to be working more efficiently.
Another factor that may explain the “double positive” result is the “immunity clause” in
Nigeria’s constitution. The immunity clause shields the president and governor from prosecution
while in office, so the EFCC does not bring cases against most public officials until after they
have left government. At this point, the agency’s prosecution of the (ex-)official may be
interpreted by the markets as effective governance reclaiming justice from those who abused
public office, leading to positive reactions in the stock market. Furthermore, there may have been
rumors about the corruption allegations before the news is officially released, so investors with
inside knowledge may have acted upon their beliefs about government corruption before the
news became public. This means that, for some events, when EFCC takes the news to the press,
the actual corruption allegation or amount involved may be less serious than the initial
expectations of investors, also leading to positive impacts on stock prices.
Lastly, we saw from the pooled regression aggregating over all events that overall,
corruption scandals lead to negative abnormal returns for all firms, and politically unconnected
firms suffer greater but insignificant losses than their unconnected counterparts. Indeed, this
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apparent insignificance of political connections is in line with prior findings of other political
connection studies in Nigeria (Osamwonyi and Tafamel, 2013; Aburime, 2009) as discussed in
the literature review above. Furthermore, the average number of connections in this paper was
0.49, with 52 out of 106 firms being politically connected. This is a rather large fraction, and
leads to questions about what it actually means to be politically connected in Nigeria. Since
connections are so common, it may be the case that they seize to confer any special advantage
beyond the dignitary to connected firms. In addition, not only are political connections common,
but announcements of public scandals are also not a rare event in Nigeria. Therefore, it may be
the case that investors are fairly immune to the political environment and have already factored
the difficult institutional climate into their initial investment decision. A final explanation for the
insignificance of the overall results is that general perceptions of government may not be critical
to the daily investment decisions of investors in Nigerian firms. Instead, it may be that close
personal connections to the government officials involved in these cases is more important than
general access to political resources as was studied in this paper. It would be interesting for
future research to investigate this hypothesis.
Whilst it is possible to explain the different impacts we observe in the results, an
important question for this research is to understand the root of the heterogeneity, and why
certain events have more of a differential impact on politically connected firms than others do.
One common factor of all the events with a statistically significant main estimator is that they
involve larger amounts of looted or missing funds, with a mean of 25 billion Naira, compared to
the whole sample mean of 12 billion Naira. This difference is statistically significant at the 5%
level. The regressions in Table 6 investigate how the sign of the coefficient on the main
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estimator (!!) and its significance change as the amount involved in the scandal varies. Columns
(1) and (2) show that !!becomes more negative as the amount involved in the scandal increases,
and this relationship is significant at the 10% level for all events, and at the 1% level for events
with a significant !!in the main regression. This result is also depicted in Figure 3. The amount
involved in a scandal might be seen as a measure of the intensity of the corruption event, so this
finding suggests that politically connected firms suffer more relative to unconnected firms when
there is a more serious scandal. Similarly, columns (3) and (4) show that !! and !! from the
main regression (equation (3)) are more likely to both be negative when the amount involved in a
scandal is larger, suggesting that all firms suffer greater losses in returns when there is a more
serious scandal. Lastly, column (5) of Table 6 shows the intuitive result that an event is more
likely to have a significant impact on firms’ returns when the amount involved in the scandal is
larger.
In sum, there are several possible explanations for the varied effects of corruption
allegations on firm value that this study finds. The impacts are event-specific, and may depend
on how much inside knowledge existed about the case before it became public, the nature of this
knowledge and its relation to the truth, and other network effects. Overall, I pose two main
opposing effects that may explain the heterogeneous findings in this paper. The first is the
reputation effect of the EFCC’s work, which has a tendency to increase the returns to firms when
a corruption scandal is uncovered because it signals the competency of that agency, and
increases confidence in the investment climate. The second effect is the political effect, which
reduces the returns to firms when a public scandal is announced due to increased uncertainty
regarding access to political favors. The results suggest that the first effect is dominant when the
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amount involved in the scandal is small, but as the amount of diverted funds increases and the
scandal is perceived as being more serious, the second effect prevails, increasing the tendency of
firms to suffer reductions in their value. In both cases, politically connected firms tend to
experience a larger impact than unconnected firms.
8. Conclusion
This paper has studied the effect of different corruption announcements on the market
returns of firms listed on the Nigerian Stock Exchange. Exploiting within-firm variation and
exogenous events, I was able to analyze how politically connected and unconnected firms are
impacted by news of corruption scandals concerning public figures. I found that for some events,
firms that are politically connected experience greater impacts on their stock returns than
unconnected firms. For other events, there were no statistically significantly results. These
findings are robust to different specifications and are not dependent on firm characteristics.
The loss in returns associated with some events may reflect the market’s beliefs that
during a period of uncertainty and instability in government created by missing funds, firms
whose business depend upon access to political resources would be the first to lose out if such
events make government less willing to distribute favors or more likely to implement tighter
regulatory controls. Yet, many events did not appear to have a significantly different impact on
politically connected firms. This may reflect that the broad measure of political connections
investigated in this paper does not capture some important intricacies of firm connections in
OmotokePaul-Lawal
22
Nigeria; perhaps close personal ties to politicians as examined in a large part of the prior
literature on connections is more important than general access to political resources.
Nonetheless, corruption scandals amongst public officials do seem to impact the private
sector in some significant ways. This impact appears to be greater for politically connected firms
in certain cases, and creates instability in the stock markets in all cases as investors adjust to the
news and seek a portfolio that minimizes exposure to the uncertainty and confusion in the public
sector. Hence, this paper provides impetus for Nigeria and other countries to implement tighter
controls against fraud in the high seats of power, as it damages not only the country’s reputation,
but also affects the private sector, tarnishing its attractiveness for investment. To my knowledge,
this is the first paper studying such corruption events in the Nigerian context, and the first study
returning a multitude of heterogeneous impacts. Future research is needed to see if these results
hold amongst other sets of events. In addition, future research should also aim to investigate the
mechanisms through which political connections may be important in Nigeria, and should further
investigate the characteristics of economically significant public scandals.
OmotokePaul-Lawal
23
CHARTS&TABLES
TABLE1:DESCRIPTIVESTATISTICS
Asterisksdenotesignificancelevelsofatwo-tailedt-test
***p<0.01,**p<0.05,*p<0.1
PanelA:SummaryStatistics Mean Min Median Max St.Dev. N
(1)Connections
(2)Size
(3)Profitability
(4)Leverage
(5)Age
.49
7.08
.073
.295
49.5
0
4
-4.04
-5.39
18
0
7.04
.101
.244
48.5
1
9.64
2.05
4.63
122
.50
1.09
.515
.778
16.57
106
103
103
103
106
PanelB:Politicallyconnectedvs.Unconnected Connected Unconnected Diff
(1)Size
(2)Profitability
(3)Leverage
(4)Age
(5)Industry
Observations
7.30
-.022
.202
49.6
5.5
52
6.85
.167
.379
49.5
6.58
54
0.45*
-.189
.6
0.1
-1.58**
Notes:Firmsizeisreportedasthelogarithmoftotalassets.Returnonequityisusedforprofitability,andleverageistheratiooftotaldebttototalcapital.PanelBreportsdifferencesin
themeansofthesevariablesforconnectedandunconnectedfirms.
OmotokePaul-Lawal
24
TABLE2:REGRESSIONOFABNORMALRETURNSONPOLITICALCONNECTIONS(SIMPLEMODEL)
(1) (2) (3) (4) (5) (6) (7)
VARIABLESAbdullahi22Feb10
Abubakar8Apr14
Alao-Akala11Oct11
Audu18Mar13
Bafarawa9Dec09
Borishade1Jul08
Botmang18Jul08
PostXConn -0.00153 -0.000801 -0.00554** -0.000720 -2.59e-05 -0.00539 0.00482*
(0.00223) (0.00214) (0.00237) (0.00255) (0.00238) (0.00403) (0.00279)
Post 0.00221 0.000740 -0.00765*** -0.00546* 0.00341 0.00489 -0.00701***
(0.00268) (0.00274) (0.00281) (0.00323) (0.00297) (0.00498) (0.00183)
Constant 0.00192 -0.00128 0.00611*** 0.00130 0.000460 -0.0303*** -0.0240***
(0.00175) (0.00177) (0.00183) (0.00211) (0.00195) (0.00330) (0.00102)
Observations 1,000 1,111 1,000 1,122 1,111 1,111 1,100
R-squared 0.148 0.102 0.185 0.089 0.095 0.880 0.955
(8) (9) (10) (11) (12) (13) (14)
VARIABLESDaniel13Oct11
Dariye20Jan06
Doma19Oct11
Electrification19May09
George7Aug08
Goje7Oct11
Grange-Aduku27Mar08
PostXConn 0.000637 0.00234 0.00264 0.00359 -0.00644 -0.00307 0.00525
(0.00181) (0.00246) (0.00173) (0.00319) (0.00443) (0.00227) (0.00450)
Post 0.000800 -0.00616** 0.00729*** -0.00826** -0.00799 -0.00383 0.00719
(0.00228) (0.00298) (0.00217) (0.00398) (0.00546) (0.00270) (0.00488)
Constant -0.00104 0.000688 -0.00400*** 0.0218*** -0.0154*** 0.00446** -0.0197***
(0.00150) (0.00199) (0.00143) (0.00262) (0.00362) (0.00175) (0.00316)
Observations 1,111 1,133 1,111 1,111 1,100 1,010 909
R-squared 0.226 0.130 0.256 0.333 0.894 0.173 0.848
Standarderrorsinparentheses***p<0.01,**p<0.05,*p<0.1
Standarderrorsinparentheses***p<0.01,**p<0.05,*p<0.1
OmotokePaul-Lawal
25
TABLE2(CONT’D):REGRESSIONOFABNORMALRETURNSONPOLITICALCONNECTIONS(SIMPLEMODEL)
(15) (16) (17) (18) (19) (20) (21)
VARIABLESHaruna8Aug08
Igbinedion21Jan08
Ladoja29Aug08
Lamido4Feb14
Lamido-Abubakar9Jul15
Lawal&Co11May11
Nyako-Aliyu-Abba8Jul15
PostXConn -0.000781 0.00275 0.000404 -0.00386 0.00339 -0.00313 0.00278
(0.00436) (0.00345) (0.00309) (0.00257) (0.00223) (0.00210) (0.00218)
Post -0.00520 -0.000844 0.00318 0.00154 0.00256 -0.00322 0.00242
(0.00540) (0.00425) (0.00383) (0.00329) (0.00285) (0.00264) (0.00279)
Constant -0.0179*** -0.0154*** -0.0266*** -0.00385* -0.00602*** 0.00557*** -0.0248***
(0.00357) (0.00282) (0.00253) (0.00212) (0.00184) (0.00173) (0.00179)
Observations 1,111 1,100 1,111 1,122 1,122 1,111 1,122
R-squared 0.903 0.946 0.963 0.100 0.166 0.136 0.203
Standarderrorsinparentheses ***p<0.01,**p<0.05,*p<0.1
(22) (23) (24) (25) (26) (27)
VARIABLESNyame23Jul07
Obasanjo-Bello8Apr08
Okokuro-Opuala24Mar10
Suswan-Okolobia3Nov15
Sylvia24Feb12
Turaki11Jul07
PostXConn 0.00104 -0.0109 0.00127 0.00554** -0.00309 -0.00119
(0.00462) (0.00749) (0.00273) (0.00223) (0.00193) (0.00418)
Post 0.00324 -0.0121 0.00510 0.00178 0.00421* 0.00782
(0.00572) (0.00923) (0.00342) (0.00285) (0.00242) (0.00517)
Constant -0.0331*** -0.0214*** -0.000887 -0.0139*** 0.00233 -0.0271***
(0.00378) (0.00612) (0.00225) (0.00184) (0.00159) (0.00342)
Observations 1,111 1,100 1,100 1,122 1,111 1,111R-squared 0.902 0.755 0.201 0.184 0.281 0.882Standarderrorsinparentheses
***p<0.01,**p<0.05,*p<0.1
OmotokePaul-Lawal
26
TABLE3:REGRESSIONOFABNORMALRETURNSONPOLITICALCONNECTIONS(EXTENDEDMODEL)
(1) (2) (3) (4) (5) (6) (7)
VARIABLESAbdullahi22Feb10
Abubakar8Apr14
Alao-Akala11Oct11
Audu18Mar13
Bafarawa9Dec09
Borishade1Jul08
Botmang18Jul08
PostXDegreeConn -0.000982 8.28e-07 -0.00539*** 0.000303 -0.000476 -0.00476*** 0.00242**
(0.000946) (0.000884) (0.00106) (0.00101) (0.00107) (0.00178) (0.00123)
Post 0.00233 0.000320 -0.00573** -0.00610* 0.00377 0.00622 -0.0120***
(0.00260) (0.00263) (0.00270) (0.00313) (0.00289) (0.00485) (0.00335)
Constant 0.00192 -0.00128 0.00611*** 0.00130 0.000460 -0.0303*** -0.0194***
(0.00175) (0.00177) (0.00181) (0.00211) (0.00195) (0.00329) (0.00227)
Observations 1,000 1,111 1,000 1,122 1,111 1,111 1,100
R-squared 0.149 0.102 0.203 0.089 0.095 0.881 0.956
Standarderrorsinparentheses ***p<0.01,**p<0.05,*p<0.1
(8) (9) (10) (11) (12) (13) (14)
VARIABLESDaniel13Oct11
Dariye20Jan06
Doma19Oct11
Electrification19May09
George7Aug08 Goje7Oct11
Grange-Aduku
27Mar08
PostXDegreeConn -0.000238 0.000818 0.00230*** 0.00386*** -0.00338* -0.00444*** 0.00131
(0.000776) (0.00129) (0.000783) (0.00144) (0.00195) (0.00102) (0.00200)
Post 0.00130 -0.00574* 0.00660*** -0.00968** -0.00815 -0.00156 0.00846*
(0.00221) (0.00293) (0.00211) (0.00386) (0.00533) (0.00260) (0.00473)
Constant -0.00104 0.000688 -0.00400*** 0.0218*** -0.0154*** 0.00446** -0.0197***
(0.00150) (0.00199) (0.00142) (0.00261) (0.00362) (0.00174) (0.00316)
Observations 1,111 1,133 1,111 1,111 1,100 1,010 909
R-squared 0.226 0.129 0.260 0.337 0.894 0.189 0.848
Standarderrorsinparentheses ***p<0.01,**p<0.05,*p<0.1
OmotokePaul-Lawal
27
TABLE3(CONT’D):REGRESSIONOFABNORMALRETURNSONPOLITICAL
CONNECTIONS(EXTENDEDMODEL)
(15) (16) (17) (18) (19) (20) (21)
VARIABLES Haruna8Aug08Igbinedion21Jan08
Ladoja29Aug08
Lamido4Feb14
Lamido-Abubakar9Jul15
Lawal&Co11May11
Nyako-Aliyu-Abba8Jul15
PostXDegreeConn -0.000304 0.00217 -0.000556 -0.00148 0.00169* -0.00296*** 0.00154*
(0.00194) (0.00152) (0.00137) (0.00106) (0.000919) (0.000950) (0.000898)
Post -0.00531 -0.00133 0.00379 0.000863 0.00271 -0.00221 0.00240
(0.00527) (0.00415) (0.00374) (0.00315) (0.00276) (0.00256) (0.00270)
Constant -0.0179*** -0.0154*** -0.0266*** -0.00385* -0.00604*** 0.00557*** -0.0248***
(0.00357) (0.00282) (0.00253) (0.00212) (0.00185) (0.00172) (0.00181)
Observations 1,111 1,100 1,111 1,122 1,111 1,111 1,111
R-squared 0.903 0.947 0.963 0.100 0.166 0.143 0.203
Standarderrorsinparentheses ***p<0.01,**p<0.05,*p<0.1
(22) (23) (24) (25) (26) (27)
VARIABLESNyame23Jul07
Obasanjo-Bello8Apr08
Okokuro-Opuala24Mar10
Suswan-Okolobia3Nov15
Sylvia24Feb12
Turaki11Jul07
PostXDegreeConn 5.41e-05 -0.000603 0.000973 0.00362*** -0.00174** 0.000120
(0.00226) (0.00331) (0.00116) (0.000915) (0.000823) (0.00204)
Post 0.00366 -0.0163* 0.00487 0.00130 0.00424* 0.00722
(0.00559) (0.00903) (0.00332) (0.00275) (0.00235) (0.00506)
Constant -0.0331*** -0.0214*** -0.000887 -0.0139*** 0.00233 -0.0271***
(0.00378) (0.00613) (0.00225) (0.00185) (0.00159) (0.00342)
Observations 1,111 1,100 1,100 1,111 1,111 1,111R-squared 0.902 0.755 0.201 0.191 0.282 0.882Standarderrorsinparentheses
***p<0.01,**p<0.05,*p<0.1
OmotokePaul-Lawal
28
TABLE4:AGGREGATEREGRESSIONOFABNORMALRETURNSONPOLITICAL
CONNECTIONS
(1) (2) (3) (4)
VARIABLES Simple Extended Simple Extended
PostXConn -0.000242
-0.000131
(0.000751)
(0.000773)
Post -4.50e-05 -5.42e-05 -0.000110 -7.40e-05
(0.000417) (0.000397) (0.000431) (0.000411)
Conn -0.000522
0.00801
(0.00633)
(0.00629)
PostXDegreeConn
-0.000127
-0.000122
(0.000272)
(0.000292)
DegreeConn
-0.00105
0.00278
(0.00189)
(0.00226)
Size
0.000611 0.000626
(0.000553) (0.000536)
Constant 0.000574 0.00121 -0.0131 -0.0119
(0.00486) (0.00443) (0.00956) (0.00916)
Observations 29,494 29,461 28,326 28,293
R-squared 0.290 0.290 0.036 0.035
Robuststandarderrorsinparentheses ***p<0.01,**p<0.05,*p<0.1
OmotokePaul-Lawal
29
TABLE5:ROBUSTNESSCHECKSNotes:Thetableshowstheresultsofrobustnesschecksforeventsfoundtohavesignificantresults.Thespecificationincolumnslabeled(a) is a replication of the standard regression of the extendedmodel in Table 3. Columns labeled (b)control for size, (c)controls forindustry,andcolumnslabeled(d)controlforbothsizeandindustry. (1a) (1b) (1c) (1d) (2a) (2b) (2c) (2d)
Alao-Akala11Oct11
Alao-Akala11Oct11
Alao-Akala11Oct11
Alao-Akala11Oct11
Borishade1Jul08
Borishade1Jul08
Borishade1Jul08
Borishade1Jul08
PostXDegreeConn -0.00539*** -0.00605*** -0.00539*** -0.00605*** -0.00476*** -0.00478 -0.00476 -0.00478
(0.00106) (0.00113) (0.00109) (0.00113) (0.00178) (0.00522) (0.00488) (0.00522)
DegreeConn
0.00447*** 0.00413*** 0.00457***
0.0118*** 0.0113*** 0.0121***
(0.000876) (0.000846) (0.000879)
(0.00386) (0.00363) (0.00387)
Post -0.00573** -0.00445 -0.00573** -0.00445 0.00622 0.00689 0.00622 0.00689
(0.00270) (0.00282) (0.00278) (0.00281) (0.00485) (0.0138) (0.0133) (0.0138)
Size
-0.000185
-0.000148
0.00151
0.00163
(0.000241)
(0.000243)
(0.00114)
(0.00115)
Industry
0.000252 0.000269
0.000745 0.000848
(0.000213) (0.000215)
(0.000974) (0.00101)
Constant 0.00611*** 0.00502 0.00113 0.00268 -0.0303*** -0.0650*** -0.0436*** -0.0725***
(0.00181) (0.00439) (0.00242) (0.00478) (0.00329) (0.0210) (0.0113) (0.0227)
Observations 1,000 960 1,000 960 1,111 1,067 1,111 1,067
R-squared 0.203 0.066 0.065 0.067 0.881 0.021 0.019 0.022
Standarderrorsinparentheses***p<0.01,**p<0.05,*p<0.1
(3a) (3b) (3c) (3d) (4a) (4b) (4c) (4d)
Botmang18Jul08
Botmang18Jul08
Botmang18Jul08
Botmang18Jul08
Doma19Oct11
Doma19Oct11
Doma19Oct11
Doma19Oct11
PostXDegreeConn 0.00242** 0.00224 0.00242 0.00224 0.00230*** 0.00258*** 0.00230*** 0.00258***
(0.00123) (0.00596) (0.00557) (0.00596) (0.000783) (0.000864) (0.000852) (0.000864)
DegreeConn
0.00636 0.00567 0.00696
-0.00193*** -0.00172*** -0.00191***
(0.00440) (0.00414) (0.00442)
(0.000638) (0.000633) (0.000641)
Post -0.0120*** -0.0115 -0.0120 -0.0115 0.00660*** 0.00555** 0.00660*** 0.00555**
(0.00335) (0.0158) (0.0152) (0.0158) (0.00211) (0.00227) (0.00230) (0.00227)
Size
0.00102
0.00126
0.000290
0.000299
(0.00131)
(0.00132)
(0.000187)
(0.000189)
Industry
0.00169 0.00170
2.37e-06 5.83e-05
(0.00111) (0.00115)
(0.000170) (0.000167)
Constant -0.0194*** -0.0408* -0.0341*** -0.0556** -0.00400*** -0.00665* -0.00259 -0.00716*
(0.00227) (0.0240) (0.0130) (0.0260) (0.00142) (0.00343) (0.00197) (0.00373)
Observations 1,100 1,056 1,100 1,056 1,111 1,067 1,111 1,067
R-squared 0.956 0.008 0.008 0.010 0.260 0.041 0.038 0.041Standarderrorsinparentheses***p<0.01,**p<0.05,*p<0.1
OmotokePaul-Lawal
30
TABLE5(Cont’d):ROBUSTNESSCHECKSNotes:Thetableshowstheresultsofrobustnesschecksforeventsfoundtohavesignificantresults.Thespecificationincolumnslabeled(a)isareplicationofthestandardregressionoftheextendedmodelinTable3.Columnslabeled(b)controlforsize,(c)controlsforindustry,
andcolumnslabeled(d)controlforbothsizeandindustry. (5a) (5b) (5c) (5d) (6a) (6b) (6c) (6d)
Electrification19May09
Electrification19May09
Electrification19May09
Electrification19May09
George7Aug08
George7Aug08
George7Aug08
George7Aug08
PostXDegreeConn 0.00386*** 0.00422** 0.00386** 0.00422** -0.00338* -0.00268 -0.00338 -0.00268
(0.00144) (0.00169) (0.00161) (0.00169) (0.00195) (0.00612) (0.00571) (0.00612)
DegreeConn
0.00107 0.000829 0.000973
0.00763* 0.00777* 0.00788*
(0.00125) (0.00119) (0.00125)
(0.00452) (0.00424) (0.00454)
Post -0.00968** -0.0107** -0.00968** -0.0107** -0.00815 -0.00813 -0.00815 -0.00813
(0.00386) (0.00442) (0.00433) (0.00442) (0.00533) (0.0162) (0.0156) (0.0162)
Size
9.63e-05
5.96e-05
0.000790
0.000892
(0.000365)
(0.000368)
(0.00134)
(0.00135)
Industry
-0.000395 -0.000257
0.000707 0.000711
(0.000316) (0.000322)
(0.00114) (0.00118)
Constant 0.0218*** 0.0196*** 0.0235*** 0.0219*** -0.0154*** -0.0347 -0.0257* -0.0409
(0.00261) (0.00668) (0.00369) (0.00725) (0.00362) (0.0246) (0.0133) (0.0267)
Observations 1,111 1,067 1,111 1,067 1,100 1,056 1,100 1,056
R-squared 0.337 0.088 0.086 0.088 0.894 0.006 0.006 0.006
Standarderrorsinparentheses ***p<0.01,**p<0.05,*p<0.1
(7a) (7b) (7c) (7d) (8a) (8b) (8c) (8d)
Goje7Oct11
Goje7Oct11
Goje7Oct11
Goje7Oct11
Lamido-Abu.9Jul15
Lamido-Abu.9Jul15
Lamido-Abu.9Jul15
Lamido-Abu.9Jul15
PostXDegreeConn -0.00444*** -0.00466*** -0.00444*** -0.00466*** 0.00169* 0.00183* 0.00169* 0.00183*
(0.00102) (0.00109) (0.00104) (0.00109) (0.000919) (0.000935) (0.000910) (0.000935)
DegreeConn
0.00272*** 0.00268*** 0.00277***
-0.00128* -0.00122* -0.00120*
(0.000842) (0.000813) (0.000846)
(0.000691) (0.000679) (0.000697)
Post -0.00156 -0.000834 -0.00156 -0.000834 0.00271 0.00227 0.00271 0.00227
(0.00260) (0.00270) (0.00266) (0.00270) (0.00276) (0.00281) (0.00274) (0.00281)
Size
-0.000324
-0.000304
-8.81e-05
-6.75e-05
(0.000231)
(0.000233)
(0.000228)
(0.000229)
Industry
0.000149 0.000132
0.000202 0.000188
(0.000205) (0.000207)
(0.000204) (0.000207)
Constant 0.00446** 0.00724* 0.00132 0.00608 -0.00604*** -0.00265 -0.00612*** -0.00421
(0.00174) (0.00422) (0.00232) (0.00459) (0.00185) (0.00417) (0.00236) (0.00451)
Observations 1,010 970 1,010 970 1,111 1,067 1,111 1,067
R-squared 0.189 0.057 0.058 0.058 0.166 0.105 0.102 0.105
Standarderrorsinparentheses ***p<0.01,**p<0.05,*p<0.1
OmotokePaul-Lawal
31
TABLE5(Cont’d):ROBUSTNESSCHECKS
Notes:Thetableshowstheresultsofrobustnesschecksforeventsfoundtohavesignificantresults.Thespecificationincolumnslabeled
(a)isareplicationofthestandardregressionoftheextendedmodelinTable3.Columnslabeled(b)controlforsize,(c)controlsforindustry,andcolumnslabeled(d)controlforbothsizeandindustry.
(9a) (9b) (9c) (10d) (10a) (10b) (10c) (10d)
VARIABLESLawal&Co11May11
Lawal&Co11May11
Lawal&Co11May11
Lawal&Co11May11
Nyako-Aliyu-Abba8Jul15
Nyako-Aliyu-Abba8Jul15
Nyako-Aliyu-Abba8Jul15
Nyako-Aliyu-Abba8Jul15
PostXDegreeConn -0.00296*** -0.00334*** -0.00296*** -0.00334*** 0.00154* 0.00141 0.00154* 0.00141
(0.000950) (0.00101) (0.000965) (0.00101) (0.000898) (0.000916) (0.000890) (0.000916)
DegreeConn
0.00282*** 0.00261*** 0.00276***
-0.00114* -0.00123* -0.00107
(0.000744) (0.000717) (0.000748)
(0.000677) (0.000664) (0.000682)
Post -0.00221 -0.00223 -0.00221 -0.00223 0.00240 0.00242 0.00240 0.00242
(0.00256) (0.00265) (0.00260) (0.00265) (0.00270) (0.00275) (0.00268) (0.00275)
Size
3.93e-05
1.93e-05
-7.88e-05
-6.11e-05
(0.000218)
(0.000220)
(0.000223)
(0.000225)
Industry
-0.000143 -0.000138
0.000163 0.000161
(0.000192) (0.000195)
(0.000200) (0.000203)
Constant 0.00557*** 0.00274 0.00428* 0.00395 -0.0248*** -0.0225*** -0.0246*** -0.0238***
(0.00172) (0.00400) (0.00223) (0.00435) (0.00181) (0.00409) (0.00231) (0.00442)
Observations 1,111 1,067 1,111 1,067 1,111 1,067 1,111 1,067
R-squared 0.143 0.027 0.029 0.028 0.203 0.140 0.139 0.140Standarderrorsinparentheses***p<0.01,**p<0.05,*p<0.1
(11a) (11b) (11c) (11d) (12a) (12b) (12c) (12d)
VARIABLESSuswan-Oko3Nov15
Suswan-Oko3Nov15
Suswan-Oko3Nov15
Suswan-Oko3Nov15
Sylvia24Feb12
Sylvia24Feb12
Sylvia24Feb12
Sylvia24Feb12
PostXDegreeConn 0.00362*** 0.00346*** 0.00362*** 0.00346*** -0.00174** -0.00162* -0.00174* -0.00162*
(0.000915) (0.000872) (0.000897) (0.000872) (0.000823) (0.000933) (0.000904) (0.000930)
DegreeConn
-0.00228*** -0.00255*** -0.00236***
0.00342*** 0.00333*** 0.00358***
(0.000645) (0.000669) (0.000650)
(0.000689) (0.000671) (0.000690)
Post 0.00130 -8.88e-05 0.00130 -8.88e-05 0.00424* 0.00422 0.00424 0.00422
(0.00275) (0.00262) (0.00270) (0.00262) (0.00235) (0.00260) (0.00258) (0.00259)
Size
2.08e-05
1.63e-07
6.51e-05
0.000135
(0.000213)
(0.000214)
(0.000213)
(0.000214)
Industry
-0.000189 -0.000188
0.000495*** 0.000491**
(0.000201) (0.000193)
(0.000191) (0.000191)
Constant -0.0139*** -0.0120*** -0.0103*** -0.0105** 0.00233 -0.00240 -0.00351 -0.00669
(0.00185) (0.00389) (0.00233) (0.00421) (0.00159) (0.00391) (0.00221) (0.00424)
Observations 1,111 1,067 1,111 1,067 1,111 1,067 1,111 1,067
R-squared 0.191 0.163 0.146 0.163 0.282 0.051 0.051 0.057
OmotokePaul-Lawal
32
TABLE6:INVESTIGATINGTHEDIRECTIONOFIMPACT
Regressionofmainestimatorsignsonamountinvolvedinscandal
(1) (2) (3) (4) (5)
VARIABLES Sign Sign BothNeg BothNeg Significance
Amount -0.0820* -0.265*** 0.0745* 0.196** 0.0732*
(0.0421) (0.0791) (0.0367) (0.0835) (0.0420)
Constant 2.172*** 3.916*** -0.367 -1.547* 0.792**
(0.366) (0.743) (0.319) (0.785) (0.365)
Observations 27 11 27 11 27
R-squared 0.132 0.555 0.141 0.379 0.108
Standarderrorsinparentheses ***p<0.01,**p<0.05,*p<0.1 Notes:ThetableshowsunivariateregressionsusingthelogarithmoftheNairaamountinvolvedin
thescandalastheindependentvariable.Column(1)showshowtheNairaamountchangesthe
signof!! themainestimator,PostXDegreeConn;Column(2)showshowthenairaamountaffectstheestimator’ssignforeventswithasignificantcoefficientonthemainestimator.Column(3)showshowtheamountaffectsthetendencyofbothPostandPostXDegreeConntobenegative.Column(4)runsthesametestforthesignificanteventsfromTable3,whilstthelastcolumnrunsaregressionofhoweventsignificancedependsonamountinvolvedinthescandal.
OmotokePaul-Lawal
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FIGURE1:ABNORMALANDCUMULATIVERETURNS(NEGATIVEINTERACTION)ALAO-AKALA6OCT11
-0.006
-0.004
-0.002
0
0.002
0.004
0.006
0.008
0.01
0.012
-4 -3 -2 -1 0 1 2 3 4 5
EVENT DAY
Panel A: Abnormal Returns
Connected
Unconnected
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
-4 -3 -2 -1 0 1 2 3 4 5 EVENT DAY
Panel B: Cumulative Abnormal Returns
Connected
Unconnected
Notes:PanelApresentsdailystockmarketabnormalreturnsforfirmswithpoliticalconnections(redline)andthosewithnone(blueline)atthetimeoftheeventonOctober6th,2011.Meanwhile,PanelBshowsthecumulativeabnormalreturnsaroundtheeventperiod.Cumulativeabnormalreturnsaredefinedas!"# (0,!)! = ∑ !"!"!
!!! . Thedashedverticallinesinbothpanelsdenotethestartsoftheevent.
OmotokePaul-Lawal
34
FIGURE2:ABNORMALANDCUMULATIVERETURNS(“DOUBLEPOSITIVE”)DANIEL16APR12
-0.006
-0.004
-0.002
0
0.002
0.004
0.006
0.008
0.01
0.012
0.014
-5 -4 -3 -2 -1 0 1 2 3 4 5
EVENT DAY
Panel A: Abnormal Returns
Unconnected
Connected
-0.01
0
0.01
0.02
0.03
0.04
0.05
-5 -4 -3 -2 -1 0 1 2 3 4 5
EVENT DAY
Panel B: Cumulative Abnormal Returns
Unconnected
Connected
Notes:PanelApresentsdailystockmarketabnormalreturnsforfirmswithpoliticalconnections(redline)andthosewithnone(blueline)atthetimeoftheeventonApril16th,2012.Meanwhile,PanelBshowsthecumulativeabnormalreturnsaroundtheeventperiod.Cumulativeabnormalreturnsaredefinedas!"# (0,!)! = ∑ !"!"!
!!! . Thedashedverticallinesinbothpanelsdenotethestartsoftheevent.
OmotokePaul-Lawal
35
FIGURE3:GRAPHOFPOSTXPOLITICALCONNECTIONSCOEFFICIENT(!!)AGAINSTAMOUNTINVOLVED
Notes:Thegraphplotsthevaluesof!!,thecoefficientonthemainestimator–theinteractionoftheeventdummyandconnectionsdummyvariables(!"#$!" ×!"##!")–againstthelogarithmofthemonetaryamountinvolvedinthecorruptionscandal.
OmotokePaul-Lawal
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APPENDIX
TABLEA1:SUMMARYOFEVENTSSTUDIED
PublicOfficial Title/PositionCurrentpublic
officeCharge
(statecounts)AmountInvolved
(Naira)CaseInheritedbyEFCCon
EarliestPressDate
AdenikeGrange,GabrielAduku FormerMinisterofHealth N/A 56 300million 3-Apr-08 27-Mar-08
JoshuaDariye FormerGovernor,PlateauStateSenatorforPlateauCentral 23 700million 13-Jul-07 20-Jan-06
SaminuTuraki FormerGovernor,JigawaState N/A 32 36billion 13-Jul-07 11-Jul-07
IyaboObasanjo-Bello ServingSenator,OgunState N/A 56 10million 2-Apr-08 8-Apr-08
LuckyIgbinedion FormerGovernor,EdoState N/A 191 4.3billion 23-Jan-08 21-Jan-08
JollyNyame FormerGovernor,TarabaState N/A 21 180million 13-Jul-07 23-Jul-07
MichaelBotmang FormerGovernor,PlateauState N/A 31 1.5billion 18-Jul-08 18-Jul-08
BabalolaBorishade,FemiFani-Kayode FormerMinistersofAviation N/A - 19.5billion June2008. 1-Jul-08
BoniHaruna FormerGovernor,AdamawaState
MinisterforYouthDevelopment 28 254million Aug-08 8-Aug-08
BodeGeorge Chieftainoftherulingparty,PDP N/A 163 84billion Aug-08 7-Aug-08
RasheedLadoja FormerGovernor,OyoState N/A 33 6billion - 29-Aug-08NicholasUgbane,Hon.NdudiElumeluHon.MohammedJibo,Hon.PaulinusIgwe(servingmembersofHouseofRepresentatives)DrAliyuAbdullahi(servingfed.perm.sec)Mr.SamuelIbi.Mr.SimonNanle,MrLawrenceOrekoya,MrKayodeOyedeji,Mr.A.GarbaJahun
"RuralElectrificationAgencyCase"involvingthreeservingmembersoftheHouseofRepresentatives,thepermanentsecretaryoftheministryofpowerandotherhighprofilepublicofficers
-
130 5.2billion 15-May-09 19-May-09
OmotokePaul-Lawal
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AdamuAbudullahi FormerGovernor,NasarawaStateSenatorforNasarawaWest 149 15billion
Feb22/March32010 22-Feb-10
AttahiruBafarawa FormerGovernor,SokotoState N/A 47 15billion 9-Dec-09 9-Dec-09
FrancisOkokuro,Dr.CharlesSilvaOpuala
AccountantGeneralandCommissionerforFinanceandBudgetofBayelsaStaterespectively N/A 6 2.4billion 24-Mar-10 24-Mar-10
AliyuAkweDoma FormerGovernor,NasarawaState N/A 17 15billion 18-Oct-11 19-Oct-11
DanjumaGoje FormerGovernor,GombeState N/A 18 52.9billion
7-Oct-11
DrHassanLawal(and9others) FormerMinisterofWorksandHousing N/A 44 75.7billion 11-May-11 11-May-11
AdebayoAlao-Akala FormerGovernor,OyoState N/A 11 11.5billion 11-Oct-11 11-Oct-11
MrTimipreSylvia FormerGovernor,BayelsaState N/A 6 6.46billion 5-Jun-12 24-Feb-12
GbengaDaniel FormerGovernor,OgunState N/A 13 58billion 12-Oct-11 13-Oct-11
AbubakarAudu FormerGovernor,KogiState N/A 36 11billion 18-Mar-13 19-Mar-13
MohammedBelloAbubakarandAbubakarAbdullahiAhmed
PermanentSecretaryandDeputyDirectorofSokotoStateMinistryofEducationrespectively
43 100million 7-Apr-14 10-Apr-14
MurtalaNyako,Sen.Abdul-AzizNyako,AbubakarAliyu,andZulkifikkAbba
FormerGovernorofAdamawaState,hisson,andothers N/A 37 29billion 8-Jul-15 9-Jul-15
SuleLamido,AminuLamido,MustaphaLamido,andAminuWadaAbubakar
FormerGovernorofJigawaState,hissons,andanother N/A 28 1.35billion 8-Jul-15 8-Jul-15
GabrielSuswam,andOmodachiOkolobiaFormerGovernorofBenueState,andhisFinanceCommissioner N/A
3.1billion 10-Nov-15 3-Nov-15
SanusiLamido Ex-governorofcentralbank N/A N/A 20billion(USD) N/A 4-Feb-14
OmotokePaul-Lawal
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TABLEA2:FIRMSSTUDIEDLISTEDONTHENIGERIANSTOCKEXCHANGE(NSE)
Symbol FirmName ISO_ISIN Industry7UP 7-UpBottlingComp.Plc. NG7UP0000004 ConsumerGoodsACADEMY AcademyPressPlc. NGACADEMY008 ServicesACCESS AccessBankPlc. NGACCESS0005 FinancialServicesAFRPAINTS AfricanPaints(Nigeria)Plc. NGAFRPAINTS8 IndustrialGoodsAGLEVENT A.G.LeventisNigeriaPlc. NGAGLEVENT01 ConglomeratesAIICO AiicoInsurancePlc. NGAIICO00006 FinancialServicesALEX AluminiumExtrusionInd.Plc. NGALEX000003 NaturalResourcesALUMACO AluminiumManufacturingCompanyPlc NGALUMACO008 NaturalResourcesANINO AninoInternationalPlc. NGANINO00003 OilandgasARBICO ArbicoPlc. NGARBICO0007 Construction/RealEstateASHAKACEM AshakaCemPlc NGASHAKACEM8 IndustrialGoodsAVONCROWN AvonCrowncaps&Containers NGAVONCROWN7 IndustrialGoodsBERGER BergerPaintsPlc NGBERGER0000 IndustrialGoodsBETAGLAS BetaGlassCoPlc. NGBETAGLAS04 IndustrialGoodsBOCGAS B.O.C.GasesPlc. NGBOCGAS0008 NaturalResourcesCADBURY CadburyNigeriaPlc. NGCADBURY001 ConsumerGoodsCAP CapPlc NGCAP0000009 IndustrialGoodsCCNN CementCo.OfNorth.Nig.Plc NGCCNN000003 IndustrialGoodsCHAMPION ChampionBrew.Plc. NGCHAMPION00 ConsumerGoodsCHELLARAM ChellaramsPlc. NGCHELLARAM5 ConglomeratesCILEASING C&ILeasingPlc. NGCILEASING2 ServicesCONOIL ConoilPlc NGCONOIL0003 OilandgasCORNERST CornerstoneInsuranceCompanyPlc. NGCORNERST03 FinancialServicesCOSTAIN Costain(WA)Plc. NGCOSTAIN006 Construction/RealEstateCUTIX CutixPlc. NGCUTIX00002 IndustrialGoodsDNMEYER DnMeyerPlc. NGDNMEYER001 IndustrialGoodsDUNLOP DnTyre&RubberPlc NGDUNLOP0005 ConsumerGoodsELLAHLAKES EllahLakesPlc. NGELLAHLAKE8 AgricultureENAMELWA NigerianEnamelwarePlc.
ConsumerGoods
EQUITYASUR EquityAssurancePlc. NGEQUITYASS2 FinancialServicesETERNA EternaPlc. NGETERNAOIL1 OilandgasEVANSMED EvansMedicalPlc. NGEVANSMED04 HealthcareFBNH FbnHoldingsPlc NGFBNH000009 FinancialServicesFCMB FcmbGroupPlc. NGFCMB000005 FinancialServicesFIRSTALUM FirstAluminiumNigeriaPlc NGFIRSTALUM7 IndustrialGoodsFLOURMILL FlourMillsNig.Plc. NGFLOURMILL0 ConsumerGoodsFO ForteOilPlc. NGAP00000004 OilandgasGLAXOSMITH GlaxoSmithklineConsumerNig.Plc. NGGLAXOSMTH8 HealthcareGUARANTY GuarantyTrustBankPlc. NGGUARANTY06 FinancialServicesGUINEAINS GuineaInsurancePlc. NGGUINEAINS0 FinancialServicesGUINNESS GuinnessNigPlc NGGUINNESS07 ConsumerGoodsINTBREW InternationalBreweriesPlc. NGINTBREW005 ConsumerGoodsINTERLINK InterlinkedTechnologiesPlc NGINTERLINK3 ServicesIPWA IpwaPlc NGIPWA000006 IndustrialGoodsJBERGER JuliusBergerNig.Plc. NGJBERGER009 Construction/RealEstateJOHNHOLT JohnHoltPlc. NGJOHNHOLT05 ConglomeratesJULI JuliPlc. NGJULI000003 ServicesLASACO LasacoAssurancePlc. NGLASACO0002 FinancialServicesLAWUNION LawUnionAndRockIns.Plc. NGLAWUNION02 FinancialServices
OmotokePaul-Lawal
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LEARNAFRCA LearnAfricaPlc NGLONGMAN007 ServicesLINKASSURE LinkageAssurancePlc NGLINKASSUR7 FinancialServicesLIVESTOCK LivestockFeedsPlc. NGLIVESTOCK5 AgricultureMANDRID P.S.MandridesPlc
ConsumerGoods
MAYBAKER May&BakerNigeriaPlc. NGMAYBAKER01 HealthcareMBENEFIT MutualBenefitsAssurancePlc. NGMBENEFT000 FinancialServicesMOBIL MobilOilNigPlc. NGMOBIL00007 OilandgasMORISON MorisonIndustriesPlc. NGMORISON000 HealthcareMRS MrsOilNigeriaPlc. NGCHEVRON008 OilandgasNASCON NasconAlliedIndustriesPlc NGNASCON0005 ConsumerGoodsNB NigerianBrew.Plc. NGNB00000005 ConsumerGoodsNCR Ncr(Nigeria)Plc. NGNCR0000008 ICTNEIMETH NeimethInternationalPharmaceuticalsPlc NGNEIMETH001 HealthcareNEM N.E.MInsuranceCo(Nig)Plc. NGNEM0000005 FinancialServicesNESF NigeriaEnerygySectorFund NGNESF000003 FinancialServicesNESTLE NestleNigeriaPlc. NGNESTLE0006 ConsumerGoodsNIG-GERMAN Nigeria-GermanChemicalsPlc. NGNIGGERMAN3 HealthcareNIGERINS NigerInsuranceCo.Plc. NGNIGERINS04 FinancialServicesNIGROPES NigerianRopesPlc NGNIGROPES04 IndustrialGoodsNNFM NorthernNigeriaFlourMills
ConsumerGoods
OANDO OandoPlc NGOANDO00002 OilandgasOKOMUOIL OkomuOilPalmPlc. NGOKOMUOIL00 AgriculturePHARMDEKO Pharma-DekoPlc. NGPHARMDEKO7 HealthcarePREMPAINTS PremierPaintsPlc. NGPREMPAINT2 NaturalResourcesPRESCO PrescoPlc NGPRESCO0005 AgriculturePRESTIGE PrestigeAssuranceCo.Plc. NGPRESTIGE00 FinancialServicesPZ PZCussonsNigeriaPlc. NGPZ00000005 ConsumerGoodsRAKUNITY PaintsAndCoatingsManufacturesPlc NGPAINTCOM0 OilandgasROADS RoadsNigPlc. NGROADS00004 Construction/RealEstateROKANA RokanaIndustriesPlc. NGROKANA0001 ConsumerGoodsROYALEX RoyalExchangePlc. NGROYALEX007 FinancialServicesRTBRISCOE RTBriscoePlc. NGRTBRISCOE9 ServicesSCOA SCOANig.Plc. NGSCOA000009 ConglomeratesSMURFIT SmartProductsNigeriaPlc NGSMURFIT002 Construction/RealEstateSTDINSURE StandardAllianceInsurancePlc. NGSTDINSURE7 FinancialServicesSTERLNBANK SterlingBankPlc. NGSTERLNBNK7 FinancialServicesTHOMASWY ThomasWyattNig.Plc. NGTHOMASWY07 NaturalResourcesTOTAL TotalNigeriaPlc. NGTOTAL00001 OilandgasTOURIST TouristCompanyOfNigeriaPlc. NGTOURIST009 ServicesTRANSEXPR Trans-NationwideExpressPlc. NGTRANSEXPR4 ServicesTRIPPLEG TrippleGeeAndCompanyPlc. NGTRIPPLEG04 ICTUAC-PROP UacnPropertyDevelopmentCo.Limited NGUACPROP006 Construction/RealEstateUACN UACNPlc. NGUACN000006 ConglomeratesUBA UnitedBankForAfricaPlc NGUBA0000001 FinancialServicesUBN UnionBankNig.Plc. NGUBN0000004 FinancialServicesUNIC UnicInsurancePlc. NGUNIC000008 FinancialServicesUNILEVER UnileverNigeriaPlc. NGUNILEVER07 ConsumerGoodsUNIONDICON UnionDiconSaltPlc. NGUNIONDICO1 ConsumerGoodsUPL UniversityPressPlc. NGUPL0000008 ServicesUTC UTCNig.Plc. NGUTC0000009 ConsumerGoodsVANLEER VitafoamNigPlc. NGVITAFOAM00 NaturalResourcesVITAFOAM VonoProductsPlc. NGVONO000005 ConsumerGoodsVONO WAGlassInd.Plc. NGWAGLASS003 ConsumerGoods
OmotokePaul-Lawal
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WAPCO WestAfricanPortlandCement
NaturalResourcesWAPIC WapicInsurancePlc NGWAPIC00004 FinancialServicesWEMABANK WemaBankPlc. NGWEMABANK07 FinancialServicesZENITHBANK ZenithInternationalBankPlc NGZENITHBNK9 FinancialServices
TABLEA3:DESCRIPTIVESTATISTICSBYINDUSTRY
No. Industry ConnectedFirms
UnconnectedFirms
AverageConnections
Observations
1 Agriculture 3 1 0.75 42 Conglomerates 3 2 0.6 5
3Construction/RealEstate 3 2 0.6 5
4 ConsumerGoods 11 9 0.55 205 FinancialServices 14 11 0.56 256 Healthcare 4 3 0.57 77 ICT 2 0 1 28 IndustrialGoods 3 9 0.25 129 NaturalResources 2 5 0.29 710 Oilandgas 3 5 0.38 811 Services 4 5 0.44 9
Total 52 52
104
OmotokePaul-Lawal
41
TABLEA4:LISTOFCONNECTEDFIRMS&THEIRCONNECTIONS
Firm BoardMember GovtPosition7UP DrAdekunleOjora LagosHighChief
MrsOluwatoyinOjoraSaraki
WifeofformerGovernorofKwaraStateandCurrentPresidentoftheSenateofNigeria,SenatorBukolaSaraki
ACCESS ObaS.A.Sule TraditionalrulerofOdonselu-AlaroinIjebu,North-EastOgunState
AIAig-Imoukhuede ChairthePresidentialCommitteeontheVerificationofFuelSubsidies(2012)
ErnestNdukwe
FormerChiefExecutiveOfficeroftheNigerianCommunicationsCommission,NCC
AjoritsedereAwosika
PermanentSecretaryattheFederalMinistryofInternalAffairs,theFederalMinistryofScience&TechnologyandtheFederalMinistryofPowerrespectively
AFROILChristopherEhikhuemenOlu
ChristopherEkpenyong
AGLEVENT
AmbassadorHamzatAhmadu
PrincipalSecretarytothreeofNigerian'sHeadofState,MajorGeneralAguiyi-Ironsi,GeneralYakubuGowon,andGeneralMurtalaMuhammed.
AIICO SenatorTokunboOgunbanjo SenatoroftheNationalAssemblyrepresentingOgunEastSenatorialDistrict
ChiefEugeneOkwor CommissionerforInsurance
ALUMACO JosephOyeyaniMakoju SpecialAdviseronElectricPower
ASHAKACEMEngrMuhammedMustaphahDaggash HonourableCommissionerforWorksandHousin
AlhajiBubaYerima Adamawastateex-Governor,MurtalaNyako'sAdviser
MrsHamraImam CommissionerandPermanentSecretaryinBornoState
Sen.MuhammedMumhammedOFR Bauchistatesenator
DrAbubakarAliGombe MinisterofStateforHealth
ChiefKolawoleBabalolaJamodu MinisterofIndustry
CADBURY Mr.AtedoPeterside HonorarySpecialAdvisortotheExecutiveGovernorofRiversState.
CHELLARAMOtunbaRichardAdeniyiAdebayo GovernorofEkitiState
AlhajiAhmedAdamuAbdulkadir
GovernorofCBN;SpecialAdvisertothePresidentonManufacturingandPrivateSector;PresidentialCommitteeonTariffandIncentives.
CORNERST ChristopherKoladeNigerianHighCommissionertotheUK;ChairmanoftheSubsidyReinvestmentandEmpowermentProgramme
DNMEYER EreluAngelaAdebayo FirstladyofEkitiState
ELLAHLAKES ZamaniLekwot MilitaryGovernorofRiversState
FrankChukwudiEllah SenatorofRiversstate
ENAMELWA AlhajiInuwaWada MinisterofDefence
ETERNA AdetoyeSode MilitaryAdministratorof
MahmudTukur dformerministerforCommerceandIndustry
AfolabiAdeola(Mr)
ChairmanoftheNationalPensionCommission,Vice-PresidentialcandidateoftheActionCongressofNigeria(ACN)candidateoftheActionCongressofNigeria(ACN)
EVANSMED AlhajiIbrahimDamcidaFBNH UmaruAbdulMutallab Federalminister
AyoolaOOtudeko ChairmanoftheNationalMaritimeAuthority;OFR
GarbaDuba Governor,Sokoto&BauchiState
AjibolaAfonja FederalMinisterofLabourandProductivityandNationalPoliticalConference
OmotokePaul-Lawal
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AmbroseFeese FederalMinisterofStateforWorksandHousing
AdebolaOsibogun
MemberofthePresidentialCommitteeonHousingandUrbanDevelopment,thePresidentialCommitteeonMortgageFinanceandtheNigerianRealEstateDevelopersAssociation
FCMB AlhajiIbrahimDamcida
Three-timePermanentSecretaryinthreedifferentministries(MinistryofTrade(1966to1970),MinistryofDefence(1970to1975)andMinistryofFinancein1975)
FLOURMILL JeryyGana MinisterofInformation
GLAXOSMITH TundeLemo DeputyGovernorinchargeofOperations
GUARANTY OwelleGilbertPOChikelu MinisterofEstablishment
OluwoleSOduyemi DeputyGovofCBN
FaroukBelloBunza Senator
OlabodeAgusto
DirectoroftheNationalPensionCommission,MemberoftheCentralBankofNigeriaMonetaryPolicyCommitteeandDirector-GeneraloftheBudgetOfficeoftheFederation
GUINEAINS FredUdechukwu CommissionerofFinanceAnambra
SenatorMohammedSanusiDaggash SenatorforBornoNorth;MinisterforNationalPlanning
GUINNESS SundayTomasDogonyaro Ambassador
IPWA ChiefSilasBandeleDaniyan MinisterofNationalPlanning
EmmanuelOlatunjiAdesoye MinisterofWorks
FolorunshoDaniyan SpecialAdvisertoKogiStategovernment
AbubakarSadiqZakariyaMaimalari MilitaryAdministratorofJigawaState
JBERGER NuraImam MinisterwithresponsibilityfortheMines,Power&Steel
JafaruDamulak MemberoftheHouseofRepresentatives
JULI PrinceJuliusAdelusi-Adeluyi MinisterofHealthandSocialServices
SirRemiOmotoso,MFR Ekitistategovernor
LASACO IsmailAdebayoAdewusi CommissionerforFinanceandEconomicPlanningandBudget
AshimAdebowaleOyekan CommissionerforEnvironmentandPhysicalPlannin
AderinolaDisu SpecialAdvisertoLagosStateGovernoronCentralBusinessDistrict
SanniNdanusa
NigerState’sCommissionerforWorksandInfrastructuralDevelopment,MinisterofYouths,Sports&SocialDevelopmentin2008
LAWUNION RemiBabalola MinisterofStateforFinance
LINKASSURE UdomaUdoUdoma SenatorfortheAkwa-IbomSouth
BukarUsman Federalpermanentsecretary
SilvaOpuala-Charles Commissionerforfinanceandbudget
JohnAndersonEseimokumoh CommissionerforBayelsa
PatmoreDuateIyabi CommissionerforFinance,BayelsaState
IkobhoAnthonyHowells DirectoratMinistryofFinanceincorporated,BayelsaState
LIVESTOCK SefiuAdegbengaKaka DeputyGovernorofOgunState
MAYBAKER TheophilusYakubuDanjuma FederalMinisterofDefence
DavidDankaro PermanentSecretaryoftheMinistryofFinanceandEconomicDevelopment
EmeUfotEkaette SenatorforAkwaIbomSouth
EdugieAbebe PermanentSecretaryoftheFederalRepublicofNigeria
MBENEFIT FestusPortbeni AmbassadortoEquatorialGuinea;MinisterofTransport
MORISON MuhammedAMuhammed Bauchistatesenator
OmotokePaul-Lawal
43
MRS SamailaKewa Com-missionerforFinanceandCommissionerforEducation
NBChiefKolawoleBabalolaJamodu MinisterofIndustry
NenadiEUsman MinisterofFinance
FrankNweke
MinisterofYouth,MinisterofInformationandthenMinisterofInformationandCommunications
IfuekoMOmoiguiOkauru ExecutiveChairmanoftheFederalInlandRevenueServiceofNigeria
NCRAmbassadorHamzatAhmadu
PrincipalSecretarytothreeofNigerian'sHeadofState,MajorGeneralAguiyi-Ironsi,GeneralYakubuGowon,andGeneralMurtalaMuhammed.
NEMAlhajiMohammedMunirJa'afaru
HonourableCommissionerforLocalGovernmentandCommunityDevelopment,HonourableCommissionerforInformation,HomeAffairsandCulture,HonourableCommissionerforAgricultureandNaturalResources,KadunaState
NIGERINS YusufHamisuAbubakarKadunaStateCommissionerforHealthandSocialDevelopment,CommissionerforFinanceandEconomicPlanning
NIGROPES BodeOlajumoke SenatorforOndo
NNFMAlhajiAminuAlhassanDantata KanoStateCommissioner
OANDO GeneralMMagoro FederalCommissionerofTransport,MinisterforInternalAffairs
OmamofeBoyo MinisterofPetroleum
Ms.AmalInyingialaPepple HeadoftheCivilServiceoftheFRN;Permanentsecinnumerousministries
AmmunaLawanAli PermanentSecretaryandservedinvariousMinistries
TanimuYakubu
ChiefEconomicAdviserandChiefofStafftothePresident;HonourableCommissioner,MinistryofFinance,BudgetandEconomicPlanning,KatsinaState
PHARMADEKO AfolabiAdeola(Mr)
ChairmanoftheNationalPensionCommission,Vice-PresidentialcandidateoftheActionCongressofNigeria(ACN)candidateoftheActionCongressofNigeria(ACN)
PRESCO JamesBErhuero PermanentSecretary,DeltaStae
AtedoPeterside HonorarySpecialAdvisortotheExecutiveGovernorofRiversState.
ShettimaMustafa
MinisterofAgricultureandNaturalResources;between1990and1992,HonourableMinisterofDefencefrom2008to2009andMinisterofInteriorbetween2009and2010.
AiguobasinmwinAkenzua SpecialAdvisertotheExecutiveGovernorofEdoState
PZ EmmanuelCEdozien EconomicAdvisertoPresidentShagari
LawalBatagarawa
PermanentSecretaryinKadunastateandbetween1999and2003hewasMinisterforEducationandlateraMinisterforDefence;between2003and2007hewastheSpecialAdvisertothePresidentonIntra-PartyRelations.
KolaJamodu MinisterofIndustry.
RAKUNITYAlhajiMallamMuhammedLawanBuba CommissionerforHealthandMemberoftheStateExecutiveCouncil
ROKANA ChukwuemekaUgwuh MinisterofCommerceandIndustry
RTBRISCOE AlhajiSanusiAdoBayero PermanentSecretary
SMURFIT MosesOAjaja OndoStateCommissionerforCommerce&Industry
STDINSURE AlhajiYahayaSa'ad SpecialAdviseronSpecialDutiestotheExecutiveGovernorofKadunaState.
DominicOneya AdministratorofKanoandBenueState
PaulObi AdministratorofBayelsa
RamseyMowoe PermanentSecretaryintheFederalCivilService
STERLING TamarakareYekwe CommissionerforJustice,BayelsaState
THOMASWY NenadiEUsmanCommissionerofWomenAffairs,EnvironmentandHealthinKadunaState;ministeroffinance
TOURIST AlexanderIbru MinisterofInternalAffairs
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FelixIbru GovernorandSenatorofDelta
TRANSEXPR OtunbaTBAdebayo CommissionerforOgunState
AhmanPategi MemberoftheHouseofReps
UmarJimada
InformationOfficerandtheAdministrativeOfficerintheGovernor’sofficeandCabinetDepartmentrespectively
TRIPPLEG FelixKolawoleBajomo MemberoftheSenatefortheOgunWestconstituencyofOgunState
UAC MohammedInuwaWushishi CommissionerforIndustries;ChiefofArmyStaff
AwunebaAjumogobia WifetoMinisterofStateforPetroleumandForeignMinister
UAC-PROP IbrahimAlawoMohammed MemberoftheKwaraStateHouseofAssemblyfrom1979to1983
HalimaTAlao
HonourableMinisterofStateforEducation;MinisterofStateforHealth,andHonourableMinisterofEnvironment,HousingandUrbanDevelopment
OkonAnsa
CommissionerforAgricultureandCommissionerforCommerce&IndustriesinAkwaIbomState
UBA JosephChieduKeshiPermanentSecretary,CabinetSecretariat,thePresidency;andPermanentSecretary,MinistryofForeignAffairs.
FolukeAbdul-Razaq
CommissionerintheMinistriesofFinanceandWomenAffairsinLagosState(97-99)
OwanariDuke FirstLadyofCrossRiverStateofNigeria
Ja'afaruPaki
SpecialAssistantonPetroleumMatterstoNigeria’sPresidentOlusegunObasanjo
AdekunleOlumide FederalPermanentSecretary
FerdinandAlabraba SpecialAdvisertotheGovernmentofRiversStateofNigeria
UBN IbrahimAbdullahiGobir SenatorforSokotoEast
OnikepoAkande MinisterofIndustry
UdomaUdoUdoma SenatorfortheAkwa-IbomSouth
UNIC EAOShonekan FormerPresidentofNigeria
UNILEVER UUdoUdoma SenatorfortheAkwa-IbomSouth
Mr.AtedoPeterside HonorarySpecialAdvisortotheExecutiveGovernorofRiversState.
AbbaKyari CommissionerforForestryandAnimalResources
IgweNnaemkaAAchebe ObiofOnitsha
UNIONDICON AliyuIsmailaPermanentSecretary,Political&EconomicAffairstotheOfficeoftheSecretarytotheGovernmentoftheFederation
UTC AfolabiAdeola(Mr)
ChairmanoftheNationalPensionCommission,Vice-PresidentialcandidateoftheActionCongressofNigeria(ACN)candidateoftheActionCongressofNigeria(ACN)
VANLEER OlunkleAdebayoObadinaFormerPresidentialcandidate;PresidentialAdviseronBudgetAffairsandDirectorofBudget
UAMutallab
FederalCommissioner(i.e.Minister)forEconomicDevelopment&Reconstruction
WAPCODr.ShamsuddeenUsman,CON FinanceMinister;MinisterofNationalPlanning
WEMABANK OmololuSMeroyi OndoSouthSenator
PatrickAyoAkinyelure OndoCentralSenator
ZENITH AlhajiBabaTela Senator
AmalPepple HeadoftheCivilServiceoftheFRN;Permanentsecinnumerousministries
HarunaUsmanSanusi
PermanentSecretary,Budget,FederalMinistryofFinance;PermanentSecretary,MinistryofDefence,aswellasPermanentSecretary,OfficeoftheHeadofCivilServiceoftheFederation
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to finance: the role of campaign contributions. Journal of Financial Economics 88(3), 554-80.
Do, Quoc-Anh, Yen Teik Lee, and Bang Dang Nguyen (2015), "Political Connections
and Firm Value: Evidence from the Regression Discontinuity Design of Close Gubernatorial Elections." CEPR Discussion Paper No. DP10526.
Faccio, M. (2006), “Politically Connected Firms”, American Economic Review, 96: 369-
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bailouts. Journal of Finance 61(6), 2597-635. Ferguson, T., Voth, H. (2008), Betting on Hitler – the value of political connections in
Nazi Germany. Quarterly Journal of Economics 123(1), 101-37. Fisman, David, Raymond J. Fisman, Julia Galef, Rakesh Khurana, and Yongxiang
Wang (2012), "Estimating the Value of Connections to Vice-President Cheney." The B.E. Journal of Economic Analysis & Policy 12.3
Fisman, R. (2001), “Estimating the Value of Political Connections”, American Economic
Review, 91: 1095-1102 Goldman, E., Rocholl, J., So, J. (2009), Do politically connected boards affect firm
value?” Review of Financial Studies, 22(6), 2331-60. Gonzalez Felipé, Prem Mounu (2016), “Losing Your Dictator: Firms During Political
Transition”. Working Paper, Stanford University. Imai, Masami, and Cameron A. Shelton (2011), "Elections and Political Risk: New
Evidence from the 2008 Taiwanese Presidential Election." Journal of Public Economics 95(7-8), 837-49.
Jayachandran, S. (2006), The Jeffords Effect. Journal of Law and Economics 49(2), 397-425. Johnson, S. Mitton, T. 2003. Cronyism and capital controls: evidence from Malaysia.
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Journal of Financial Economics 67(2), 351-82. Khwaja, A. I., and Mian, A. (2005), Do lenders favor politically connected firms? Rent
provision in an emerging financial market. Quarterly Journal of Economics 120(4), 1371-411.
Knight, B. (2007), Are policy platforms capitalized into equity prices? Evidence from
the Bush/Gore 2000 Presidential Election. Journal of Public Economics 91(1-2) 389-409.
Li, H., Meng, L., Wang, Q., Zhou, L.-A. (2008), Political connections, financing and
firm performance: Evidence from Chinese private firms. Journal of Development Economics, 87(2), 283-99.
Mattozzi, A. (2008), Can we insure against political uncertainty? Evidence from the U.S.
Stock Market. Public Choice 137(1-2), 43-55. Roberts, B. E. (1990), A dead senator tells no lies: seniority and the distribution of
federal benefits. American Journal of Political Science 34(1), 31-58. Osamwonyi,I.O.,TafamelE.A.(2013),FirmPerformanceandBoardPolitical
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